usaha untuk mengekalkan integriti model rangkaian yang dicadangkan, protokol ... piawaian Makanan dan Ubatan Persekutuan dan Pentadbiran (FDA) yang ...... Yee, Fuminori Kobayashi, Shozo Komaki â A proposal of Body Implementable.
PRIORITY AWARE ARCHITECTURE AND COMMUNICATION PROTOCOLS FOR A COGNITIVE RADIO BASED HOSPITAL
ISHTIAK AL MAMOON
A thesis submitted in fulfilment of the requirements for the award of degree of Doctor of Philosophy
Malaysian-Japan International Institute of Technology Universiti Teknologi Malaysia
AUGUST 2016
ii
DECLARATION
I declare that this thesis entitled ―Priority Aware Architecture and Communication Protocols for a Cognitive Radio Based Hospital‖ is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
Signature : Name : Date :
Ishtiak Al Mamoon August 2016
iii
DEDICATION
To my respectable parents, wife and younger brother who gave me endless love, trust, constant encouragement over the years, and for their prayers. This thesis is dedicated to them.
iv
ACKNOWLEDGEMENT
All tributes go to almighty Allah, for the successful completion of this thesis and fulfillment of the author‘s dream into reality. Firstly, the author thanks them all those who helped him in completing this goal. Especially, the author would like to thank Dr. A.K.M Muzahidul Islam and Assoc. Professor Dr. Sabariah Baharun, supervisors of the thesis work, for giving their support for the last three years. The author thanks them for their patience in fulfilling knowledge gaps even though those were silly and simple.
The author also shows the gratitude to Professor Dr. Shozo Komaki and Assoc. Professor Ashir Ahmed for giving their valuable suggestions, time, and effort to make this work effective.
The author also takes the pleasure to thank all of his friends in the CSN iKohza, especially Mr.Nafees Mansoor, Mr. Asim Zeb, Mr. Mahdi Zareei, Mr. Mojtaba Alizadeh and Mr. Atiqur Rahman for giving him their best support.
Finally, the author has the honor to thank his parents and family for their ever loving and caring supports which can never be expressed in words.
v
ABSTRACT
Wireless communication technology is the prime attribute to improve mobility, flexibility and reliability for hospital information system. However, due to the growth of wireless devices in near future, healthcare services may face challenges on medical spectrum scarcity, electromagnetic interference (EMI) to biomedical devices and medical data transmission reliability. To mitigate these issues cognitive radio (CR) can be improvised and fine-tuned the wireless healthcare service system. However, contemporary research on cognitive radio driven healthcare has shown limited guidelines for priority policy and network model for hospital. Thus, the main objective of this research is to design a CR based system for healthcare services where all of the hospital devices are CR enabled and categorized as per the activity. An intelligent dynamic priority enabled queuing management based hospital traffic transmission mechanism is then introduced in the proposed system. The proposed priority mechanism intelligently determines the critical level of medical management by computing weight of hospital traffics considering location and device priority. A hierarchy based hybrid network architectures, models, maintenance and device EMI-aware communication protocols are also developed for proposed hospital system. These proposed heterogeneous architectures include clustering concepts for cognitive base stations and non-medical devices where cluster head (CH) are selected by CH elector value that is based on priority of location and device, channel and mobility rate of devices. In order to maintain the integrity of the proposed network model, node joining and node leaving protocols are also proposed. Moreover, three EMI and priority aware different device to device (D2D) communication protocols are also proposed for hospital in this study. Finally, simulation results show that the critical emergency medical traffic obligates very low drop rate, delay, network maintenance duration comparing to other hospital devices. . The transmission packet drop rate is found to be 1.56% for the critical medical data and this elevates the emergency medical data reliability. The proposed transmission method is also shown to outperform the other wellknown queuing methods such as DropTail and Random Early Detection (RED). The latency delay of majority hospital devices is within the threshold level of the Federal Drug and Food Administration (FDA) prescribed standards for wireless medical devices.
vi
ABSTRAK
Teknologi komunikasi tanpa wayar merupakan ciri utama dalam meningkatkan kebolehgerakan, fleksibiliti dan kebolehupayaan sistem maklumat hospital. Memandangkan pertumbuhan pesat peranti tanpa wayar pada masa akan datang, perkhidmatan penjagaan kesihatan mungkin menghadapi cabaran kekurangannya spektrum perubatan, gangguan elektromagnet (EMI) kepada peranti bio-perubatan dan kebolehupayaan penghantaran data perubatan. Bagi mengatasi isu ini, radio kognitif (CR) boleh ditambah baik bagi memperhalusi sistem perkhidmatan kesihatan tanpa wayar. Walau bagaimanapun, penyelidikan radio kognitif terkini berkaitan garis panduan dasar keutamaan dan model rangkaian hospital adalah amat terhad. Justeru, kajian ini bertujuan merekabentuk sistem berasaskan CR bagi perkhidmatan penjagaan kesihatan dengan semua peranti hospital adalah berdaya CR dan dikategorikan mengikut aktivitinya. Mekanisme penghantaran trafik hospital berasaskan pengurusan baris-gilir mengikut keutamaan dinamik yang pintar seterusnya telah diperkenalkan dalam sistem ini. Mekanisme keutamaan yang dicadangkan ini bijak menentukan tahap kritikal pengurusan hospital dengan memberi pemberat bagi trafik hospital setelah mengambil kira keutamaan kedua lokasi dan peranti. Senibina, model, penyelenggaraan dan protokol komunikasi peranti berpandukan-EMI bagi rangkaian hibrid berasaskan hierarki juga dibangunkan bagi sistem ini. Senibina dicadangkan ini mengambilkira konsep kluster bagi stesen pengkalan kognitif dan peranti bukan perubatan di mana kelompok (CH) telah dipilih menggunakan nilai pemilih CH yang dihitung mengikut keutamaan lokasi dan peranti, saluran dan kadar kebolehgerakan peranti. Dalam usaha untuk mengekalkan integriti model rangkaian yang dicadangkan, protokol masuk dan keluar nod turut dicadangkan. Tambahan pula, tiga EMI dan protokol peranti ke peranti (D2D) komunikasi mengikut keutamaan yang berbeza turut dicadangkan. Akhir sekali, keputusan simulasi menunjukkan bahawa trafik perubatan kecemasan yang kritikal memberi kesan kepada kadar penurunan, penangguhan dan jangka masa penyelenggaraan rangkaian yang rendah berbanding dengan peranti hospital lain. Sistem penghantaran dan kaedah komunikasi yang dicadangkan ini juga didapati lebih baik berbanding kaedah dan piawai peranti perubatan tanpa wayar sedia ada. Kadar penurunan paket data perubatan kritikal didapati 1.56 % dan ini menaikkan kebolehpercayaan data perubatan kecemasan. Kaedah penghantaran yang dicadangkan juga didapati mengatasi kaedah beratur terkenal yang lain seperti kaedah Ekor susut dan Pengesanan Awal Rawak (RED). Kelewatan pendam bagi kebanyakan peranti hospital adalah pada paras ambang piawaian Makanan dan Ubatan Persekutuan dan Pentadbiran (FDA) yang ditetapkan bagi peranti perubatan tanpa wayar.
vii
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF ABBREVIATIONS
xiv
LIST OF APPENDICES
xvii
INTRODUCTION
1
1.1 Background and Motivation of Research
1
1.2 Problem Statement
5
1.3 Research Objectives
6
1.4 Research Scope
6
1.5 Thesis Contributions
7
1.6 General Research Methodology and Framework
9
1.6.1 Research Mapping
10
1.6.2 Data Need
10
1.7 Thesis Organization
11
LITERATURE REVIEW
14
2.1 Introduction
14
2.2 Study on Wireless Health Care System
15
2.3 Contribution of Cognitive Radio (CR) in Healthcare
17
viii
3
2.4 Existing CR Based Healthcare System
19
2.5 Literature Analysis on CR Based Healthcare System
28
2.5.1 Architecture for Hospital
28
2.5.2 Device Status of Hospital Management
30
2.5.3 QoS Management for Hospital
32
2.5.4 Communication Protocols for Hospital
33
2.6 Concluding Remarks
35
SYSTEM DESCRIPTION AND PRIORITY MANAGEMENT
36
3.1 Introduction
36
3.2 Methodologies for System Design and Priority Management
37
3.2.1 Methodology of CR Based Hospital and Device Categorization 3.2.2 Methodology of Priority Management Policy
37 37
3.3 Development of CR Based Hospital and Device Categorization
39
3.3.1 Cognitive Radio Capable Base Stations (CBS)
39
3.3.2 Hospital Administration Center (Database, Monitoring Cell) 3.3.3 CR Enabled Medical Devices 3.3.3.1 Life Supporting Real Time Medical Devices
40 42 42
3.3.3.2 Life Supporting Non-real Time Medical Devices
43
3.3.3.3 Non-life Supporting Real Time Medical Devices
43
3.3.3.4 Non-life Supporting Non-real Time Medical Devices 3.3.3.5 Emergency Telemedicine Category 3.3.4 CR Enabled Non-Medical Device
43 44 44
3.3.4.1 Non-medical Real-time Devices
45
3.3.4.2 Non-medical Non-real Time Devices
45
3.3.5 CR Enabled Medical Sensor and Sink Access Control (CT-SAC)
45
ix
4
3.4 Medical Radio Resource Management
48
3.5 Priority Management
49
3.5.1 Proposed Priority Policy
49
3.5.2 Priority Access Mechanism
52
3.5.3 Priority Queue and Scheduling Algorithm
54
3.6 Concluding Remarks
61
NETWORK ARCHITECTURE
62
4.1 Introduction
62
4.2 Methodologies for Network Architecture and Communication
62
4.3 Network Formation
63
4.4 Proposed Architecture
64
4.4.1 Network Architecture of Cognitive Base Station (CBS) Backhaul System 4.4.2 Network Architecture of CT-SAC
66 68
4.4.3 Flat network Based Architecture for Non-medical Device 4.4.4 Start topology based Architecture for Medical Device 4.5 Maintenance Algorithm for the Network
70 71 72
4.5.1 Node Move-In Algorithm
73
4.5.2 Node Move-Out Algorithm
77
4.6 Communication Protocols
79
4.6.1 Protocol for Medical Device to Admin Center
80
4.6.2 Protocol for Non-medical to Non-medical CR Device
83
4.6.3 Protocols for CR Aided Sensor, CT-SAC and Admin Center
5
87
4.7 Concluding Remarks
89
SIMULATION RESULTS AND ANALYSIS
91
5.1 Introduction
91
5.2 Simulation Verification and Validation (V&V)
92
5.3 The Simulation Environment
94
x 5.4 Priority Management Evolution of Cognitive Radio Based Hospital
96
5.4 Performance Evaluation for Network Maintenance Scheme
98
5.4.1 Node Move-In
98
5.4.2 Node Move-Out
99
5.5 Comparison between Proposed System with Other Standards
6
100
5.5.1 Comparison of Queuing Management
101
5.5.2 Comparison of Latency Period (QoS Performance)
102
5.6 Concluding Remarks
103
CONCLUSION
104
6.1 Summary
104
6.2 Research Findings
105
6.3 Suggestions for Future Research
106
REFERENCES
108
Appendices A-B
116-118
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
1.1
Research mapping
10
2.1
CR based healthcare literature review Matrix
34
3.1
Characteristic of different sensor with location information
46
3.2
Priority level of proposed hospital device
50
3.3
Proposed scheduling algorithm
57
3.4
Device priority assignment table
59
3.5
Location area priority assignment table
59
4.1
Node Move-In algorithm
74
4.2
Node Move-Out algorithm
78
4.3
Communication protocol 1: Medical device to admin center
82
4.4
Communication protocol 2: Non-Medical to non-medical
85
4.5
Communication protocol 3: CT-SAC to admin center
88
5.1
Simulation information for proposed hospital system
95
xii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Medical telemetric frequency Allocation of WMTS Band
2
1.2
Cognitive radio network
5
1.3
Operational framework
9
2.1
Wireless medicine and healthcare system in conventional hospital
16
2.2
Cognitive radio network scenarios
18
2.3
Cognitive radio system in a hospital environment [26-27]
21
2.4
Spectrum ranges for WMTS bands with access priority [29]
22
2.5
CR-BAN based medical triage, EMR DB and monitoring system
24
3.1
System design of cognitive radio enabled hospital
41
3.2
CogMed facilitated by cognitive radio enabled sensor
47
3.3
Block diagram of priority management for proposed system
52
3.4
System diagram priority queue mechanism
55
3.5
Block diagram of scheduling algorithm
58
4.1
Generic Architecture of proposed hospital System
64
4.2
Hierarchical architecture of entire hospital system
65
4.3
CBS backhaul network system
66
4.4
Triangular shaped partial mesh network based CBS backhaul
66
4.5
Assigned CBDV on CBS backhaul
66
4.6
Cluster formation on CBS backhaul
66
4.7
CT-SAC network architecture in star topology with redundancy 69
4.8
Cluster based non-medical device architecture
71
4.9
Star topology based CT-medical network architecture
72
4.10
Flowchart for node Move-In algorithm
76
4.11
Flowchart for node Move-Out algorithm
79
xiii 4.12
Communication protocol for medical devices to admin center
4.13
Communication protocol for non-medical to non-medical devices 86
4.14
Communication protocols for CRSN, CT-SAC and admin center 89
5.1
Comparison of simulation and calculation from theory
93
5.2
Comparison of proposed Simulator and real world simulator
94
5.3
Packet drop percentage
96
5.4
Average queuing period (ms)
97
5.5
Average network delay (ms)
97
5.6
Node Move-In algorithm
99
5.7
Node Move-Out algorithm
100
5.8
Comparison between proposed queue, RED and DropTail
5.9
83
algorithm
101
Comparison between proposed device latency with standards
102
xiv
LIST OF ABBREVIATIONS
AAMI
-
Association for the Advancement of Medical Device
AC
-
Access Class
AECC
-
Additional Emergency Control channel
AIFS
-
Arbitrary Inter Frame Space
AQM
-
Active Queue Management
BER
-
Bit Error Rate
BNC
-
Body Network Controller
BSC
-
Base Station Controllers
BTS
-
Base Transceiver Stations
CAP
-
Contention Access Period
CBS
-
Cognitive Radio Base Station
CBSV
-
Cognitive Base Station Value
CCC
-
Common Control Channel
CCSS
-
Centralized Cooperative Spectrum Sensing
CCU
-
Coronary Care Unit
CFP
-
Contention Free Period
CH
-
Cluster Head
CHSV
-
Cluster Head Selector Value
CM
-
Cluster Member
CR
-
Cognitive Radio
CRC
-
Cognitive Radio Controller
CRN
-
Cognitive Radio Network
CRSN
-
Cognitive Radio aided Sensor Network
CSS
-
Cooperative Spectrum Sensing
CT
-
Cognitive Terminal
CT-SAC
-
Cognitive Terminal enabled Sink Access Control
D2D
-
Device to Device
xv DCC
-
Dedicated Control Channel
DCF
-
Distributed Coordination Function
DCSS
-
Distributed Cooperative Spectrum Sensing
DSA
-
Dynamic Spectrum Access
ECG
-
Electrocardiogram
EDCA
-
Enhance Distributed Channel Access
EEG
-
Electroencephalogram
EMI
-
Electro Magnetic Interference
E-SLR
-
Extensive Systematic Literature Review
FCC
-
Federal Communications Commission
FDA
-
Food and Drug Administration
FIFO
-
Frist in Frist out
GPS
-
Global Positioning System
GSM
-
Global System for Mobile Communications
GW
-
Gateway
HCF
-
Hybrid Coordination Function
HIS
-
Health Information System
ICU
-
Intensive Care Unit
IEEE
-
International Electrical and Electronic Engineering
IETF
-
Internet Engineering Task Force
ISM
-
Industrial Scientific and Medical
LA
-
Location Area
LS
-
Life Supporting
MAC
-
Media Access Control
MICS
-
Medical Implant Communications Service
MIMO
-
Multiple Input Multiple Output
MPC
-
Model Predictive Control
MSC
-
Mobile Switching Centre
NLS
-
None Life Supporting
NMS
-
Network Monitoring System
OfCom
-
Office of Communications
OPD
-
Outdoor Patient Department
OT
-
Operating Theater
xvi PCF
-
Point Coordination Function
PoS
-
Point of Sale
QoS
-
Quality of Services
RED
-
Random Early Detection
RF
-
Radio Frequency
RFC
-
Request for Comment
RFID
-
Radio Frequency Identification
RSSI
-
Received Signal Strength Indication
RTS/CTS
-
Request To Send/Clear To Send
SH
-
Spectrum Hole
SNR
-
Signal Noise Ratio
SPF
-
Single Point of Failure
SSF
-
Spectrum Sensing Functionality
STDMA
-
Spatial Time-Division Multiple Access
TR
-
Trans-Receiver
TVWS
-
TV White Space
UGC
-
Utility Graph Coloring
UHF
-
Ultra High Frequency
UWB
-
Ultra Wide Band
W-CB/FQ
-
Weighted Class Based of Fair Queue
WMTS
-
Wireless Medical Telemetry Service
WRAN
-
Wide Regional Area Network
xvii
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
List of Publications (Journal)
116
B
List of Publications (Conference Proceedings)
117
CHAPTER 1
INTRODUCTION
1.1
Background and Motivation of Research
Wireless technology is surging rapidly [1]. The technological development of wireless communication has become the vital aspect of modern-day society. The impact of wireless technology can be seen in the developments of consumer devices such as cell phones, personal digital assistants and laptops [2-4]. Apart from these, a lot of efforts have been put on wireless driven automation and safety applications, smart grid applications, and wireless medical devices [1, 5]. From the past decade, biomedical and e-health researchers are trying to adopt wireless communication technologies to e-health care services to eliminate spaghetti of wire in a hospital [1-2, 6]. Wireless communication is a key technology that provides mobility and flexibility to the e-health applications such as remote patient monitoring, telemedicine and others. With the advent of heterogeneous wireless access networks, wireless service providers (WSPs) can combine the complementary advantages of different wireless access networks operating on both licensed and unlicensed bands to serve an increasing amount of automated health monitoring demands [3, 7].
The communication of medical information such as vital signs can be performed using any or a combination of the existing wireless technologies, for example, cellular, WiFi, Zigbee, Bluetooth and others. Each of these technologies provides different advantages in terms of coverage (cellular), bandwidth (WiFi) and very low power (ZigBee and Bluetooth) [1-4, 8]. The impact of wireless technology can also be seen in the development of consumer devices such as, smart phones, PDAs, laptops, etc. [9]. There is an ever-increasing demand for more radio spectrums
2 but on the other hand, radio spectrum is a finite precious natural resource. For medical purposes, Federal Commission of Communication (FCC) has allocated a very small amount of spectrum such as Medical Implant Communications Service (MICS), Wireless Medical Telemetry Service (WMTS), and Industrial Scientific and Medical (ISM) bands [1-3, 10]. This leads to a spectrum scarcity problem for the upcoming wireless biomedical technologies and Tele health applications. WMTS is one of the popular bands and mostly used telemetric medical band, however, FCC has also allocated this band to other services like government, military and satellite transmission. Figure 1.1 shows that almost forty percent (40%) of WMTS band is being utilized without any defined channelization for medical telemetric and telehealth purposes [2, 4, 6].
Figure 1.1 Medical telemetric frequency Allocation of WMTS Band Moreover, on top of this spectrum scarcity problem, there also exists radio spectrum being underutilized. Several surveys on radio spectrum utilizations show that spectrum bands are underutilized with variance of frequency, time and space. Radio spectrum is 15% to 85% underutilized with temporal and spatial variances according to the FCC [10].
Researchers propose different techniques, models, algorithms and ideas in recent past for wireless healthcare system and wireless biomedical device based healthcare facility. Nevertheless, healthcare systems and facilities have encountered interaction problems with their adjacent wireless medical and non-medical devices [1-2, 7, 9]. Most of the researchers focus on Electro Magnetic Interference (EMI) emitted from various wireless bio-medical and other non-medical devices (such as,
3 cell phone, PDA and other communication and surveillance equipment) during data transmitting [1, 5, 11]. The EMI can create hazardous situation for the other noncommunicating medical devices in the hospital like equipment malfunction, restart and others. Moreover, there is a big issue with medical spectrums (such as WMTS, MICS and unlicensed ISM band) might not be adequate for future healthcare scheme. Communication interference can be a common issue in the future [6, 10, and 11]. As mentioned earlier spectrum is natural and finite resource and this resulted in most part of spectrum been fully allocated for various purposes.
The discussion above, leads to Dynamic Spectrum Assess (DSA) or opportunistic frequency usage for future wireless medical field [7, 9]. To resolve the EMI issue, hospital environment needs to consider different opportunistic network and DSA capable wireless medical and non-medical equipment. An EMI aware centralized cooperative spectrum fusion scheme should be introduced to manage that system and such scenario has not been considered in any existing work. Recent researches also show some primary concepts of medical device priority scheme being investigated [1, 5-7, 10].
J. Mitola [III] first developed the opportunistic network or cognitive radio concept. The key notion of cognitive radio is to use the underutilized radio spectrum in an adaptable manner [12]. Cognition is the scientific term for the process involved in knowing, which includes perception and judgment. Such process intelligently detects surrounding environment and makes decision based on what is learnt. Having the information of the surrounding communication situation, Cognitive Radio, a smart wireless communication system has the ability to orient itself to the condition, decide on the course of action and apply this course of action by making corresponding changes in certain operating parameters in real time. It ensures appropriate spectrum utilization to overcome the unlicensed band issue, bandwidth constrains, EMI optimization and reliability for wireless devices [11-13].
In a cognitive radio (CR) system, there are two types of users: primary and secondary users. Primary users or licensed users are users who have legacy rights to use the spectrum. On the other hand, secondary users or unlicensed users have lower
4 priority and can opportunistically use the frequency bands without any interference to primary users. Therefore, cognitive capabilities are required in secondary users. Cognitive radio–based transmission process involves the following four steps: spectrum sensing, adaptive learning, spectrum decision, and transmission parameter setting. A cognitive radio should have ability to measure, sense and be aware of the characteristics of the radio channel atmosphere like availability of spectrum, power, interference, noise level, user application and other operating restrictions. Adaptive learning steps recognize the behaviors of not only the primary user but also the characteristics of the secondary data (such as data rate requirement, transmission mode, bandwidth and acceptable Bit Error Rate (BER). Based on the information of spectrum sensing and adaptive learning, CR device understands the user parameter and predict the future space from spectrum and an appropriate channel is selected according to the channel capacity of the spectrum holes and requirement of secondary users [11-15].
Figure 1.2 addresses an illustration on how cognitive radio network works without the interference of primary user (PU). After choosing the specific spectrum band and determining the transmission parameters, the CR transmitters can then be communicating with each other. The CR has to keep the information of radio channel environment. Radio environment is dynamic due to the rapid change of primary user appearance, movement or channel variations. Moreover, currently operated CR has to switch to another free band if the rightful owner of current spectrum suddenly appeared. For wireless healthcare infrastructure, cognitive radio network (CRN) can play a vital role to escalate the medical Quality of Services (QoS) and to ensure appropriate spectrum utilization to overcome the unlicensed band issue, bandwidth constrains, EMI optimization and reliability for wireless devices [14-15].
5
Figure 1.2 Cognitive radio network
1.2
Problem Statement
There is coexistence of multiple wireless medical and non-medical devices within the same licensed spectrum in hospital, and there exists EMI effect on different biomedical devices that degraded the quality of wireless tele-health services in healthcare facility. It is also observed that no studies have been made on uniform priority and transmission mechanism in tele-healthcare and telemedicine. Moreover, a well-designed hospital architecture, system description and device to device communication protocols have not being studied in recent CR based hospital and healthcare.
The study attempts to investigate the strengths and limitation of different cognitive radio based healthcare system. The study emphasize on the modeling of
6 priority aware cognitive radio based hospital network architecture along with communication protocols, and investigate the pattern of priority policy enabled hospital device and proposed network maintenance scheme.
1.3
Research Objectives
The objectives of the proposed research are to investigate and analyze the existing system design and description for cognitive radio based healthcare, to come up with a priority aware cognitive radio based hospital where system description, hospital device categorization, a dynamic priority management, a hybrid architecture for hospital with network maintenance protocols and device to device communication protocols. The research objectives are summarized as follows:
To initiate a new system design of cognitive radio based hospital and to introduce a priority mechanism policy for proposed system ensuring reliability to emergency medical data.
To develop hierarchical based system architecture for proposed cognitive radio based hospital and network maintenance algorithms for the architecture.
To formulate communication protocols for cognitive radio based hospital considering priority and EMI immunity of hospital devices.
1.4
Research Scope
For this research, a fully structured cognitive radio enabled healthcare center is considered where all wireless medical devices are dynamic spectrum access (DSA) capable. CR enabled medical devices are considered to be static with a little movement of the CR based Body Area Network (CR-BAN) and with a predefined
7 set of licensed spectrum using common control channel set. As well as non-medical devices in hospital are also CR capable. Spectrum sensing technique and functionality is not considered in this study although some guidelines of spectrum allocation for hospital purpose are narrated. This research complies with a certain portion of similar standard like International Electrical and Electronic Engineering (IEEE) 802.11e Access Class (AC), Internet Engineering Task Force (IETF) Request for Comment (RFC) 2903 relating to priority mechanism and medical queuing management.
1.5
Thesis Contributions
Evaluation of a cognitive radio based hospital management system design and categorization of each of the devices based on its priority is discussed in this study. A device based priority mechanism policy similar to IEEE 802.11e access mechanism system and scheduling mechanism using IETF RFC 2309 are proposed. Consequently, a hybrid architecture model with maintenance protocols and device to device communication protocols is established for proposed cognitive radio network based hospital. Summary of the contributions of our study are stated below:
A robust system design for cognitive radio based hospital management is established where devices are categorized as medical and non-medical devices. The categorization is based on devices medical priorities, location information of devices and level of medical emergency. This design considers an administration zone that contains different databases, a centralized Network Monitoring System (NMS) and medical monitoring system.
Priority policy is rarely described in contemporary literature in cognitive based healthcare. Thus, a priority policy and scheduling mechanism considered in this study is included to enhance the reliability of health data. The priority policy is considered for the proposed system design, where medical and non-medical devices are scheduled according to their functions
8 and locations are introduced. A scheduling mechanism based on queuing theory is also proposed for cognitive radio based hospital. The proposed queuing mechanism ensures a robust scheduling of hospital data transmission whereby critical and emergency medical category devices getting top priority with negligible packet drop rate so as to ensure the medical data reliability. Moreover, the simulation results proved that the proposed system design and priority mechanism policy is well fitted for cognitive radio enabled hospital network and guaranteed the medical QoS for proposed system.
A three layer network heterogeneous architecture is designed for cognitive radio based hospital. Top layer is fitted with a cluster centered Cognitive radio Based Station (CBS) where an equation is formulated for Cluster Head (CH) selection for CBS. In the middle layer, architecture of cognitive radio based medical and non-medical are described. Non-medical devices are creating cluster and CH selection based on device mobility and priority. Finally, the bottom layer describes the architecture of Cognitive Radio aided Sensor Network (CRSN). Proposed hierarchical network architecture is providing a smooth core network and transmission planning of hospital driven wireless medical network. A maintenance scheme for the proposed architecture, namely node Move-In and node Move-Out is also introduced. Maintenance algorithms for both cases are formulated to enhance the reliability and integrity of architecture.
Three device to device communication protocols for proposed cognitive radio based healthcare are designed. The construction of communication protocols is emphasized to reduce the EMI effect on medical devices and to enhance the reliability of emergency medical devices.
9 1.6
General Research Methodology and Framework
The methodology of this research is based on the Extensive Systematic Literature Review (E-SLR), which is conducted to bring out the related literatures on cognitive radio based healthcare system. The operational framework of this research is presented in Figure 1.3. Study and analyze the literatures help to find the research gap in cognitive radio driven hospital. This study leads to determine problems associated with the existing system designs, medical QoS, architecture and transmission protocols for cognitive radio based hospital.
Figure 1.3 Operational framework Once the existing techniques and problems related to CR hospital with the existing techniques are understood, new system design is proposed. The developed system design of CR enabled hospital is analyzed based on mathematical analysis. Simulation environment is designed and simulation is conducted to validate the performance of the system design, where NS2 is used as the simulation tool. The simulation results are then compared with different standards and well established protocols. Once it is found that the proposed system design of hospital performs
10 better than or making a balance with the different benchmarking, results is submitted to publish.
1.6.1
Research Mapping
The research mapping of the work is presented in Table 1.1, where research objectives, research approaches and methodologies are presented.
Table 1.1: Research mapping No
Research Objective
Research Approach
Methodology
1.
To thoroughly investigate
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11 medical and non-medical devices. The priority of the devices also depends on the location of hospital.
1.7
Thesis Organization
The motives of the study are to explore and analyze the existing cognitive radio enabled healthcare system. The aim is to provide a fully opportunistic radio enabled hospital design where all CR devices are categorized according to their services. A scheduling mechanism for hospital devices based on dynamic priority scheme is established. Here, this study also incorporate hybrid architecture with maintenance protocols and priority, EMI aware communication protocols for proposed CR enabled hospital. This thesis consists of seven chapters with the elaboration of each chapter is given below.
Chapter 1 presents the thesis introduction. It provides the overview and background of the study, problem statement, objectives, scope and the significance of the research.
Chapter 2 discusses on the literature reviews of different system design and mechanism for cognitive radio driven healthcare. It provides analysis and comparisons of the existing cognitive radio based healthcare. A critical comparison is also highlighted at the end of the chapter in tabular format. It is observed that a fully defined system design which includes the categorization of devices, dynamic medical priority, and heterogeneous architecture based hospital and robust Device to Device (D2D) communication protocols are incorporated in the existing CR enabled healthcare system.
Chapter 3 explains the development of the proposed cognitive radio based hospital system and its definition. The proposed model is equipped with CR base station (CBS) backhaul, a hospital administration system equipped with different medical databases, database for EMI threshold level of hospital reside devices, historical spectrum information and several medical, non-medical devices. CR
12 capable hospital devices are mainly classified as medical and non-medical. Here, the categorizations of devices to include life supporting/non-life supporting as per the device location and service type are proposed. Based upon the device category and location of devices, a medical priority management is evolved. Moreover, the proposed device priority supported scheduling mechanism is also explained along with its priority algorithm. Finally, a general guideline for medical spectrum resource management is also briefed at the end of the chapter.
Chapter 4 presents heterogeneous topology based cognitive radio enabled hospital hierarchical architecture, which includes location priority aware cluster based CBS architecture, a star topology based design for medical devices, sensor network and a mobility, device priority aware mobile cluster driven architecture for non-medical devices. A network maintenance protocols namely node Move-In and node Move-Out protocols are introduced for the proposed architecture. The explanation of three device to device communication protocols, namely medical device to administration center communication protocol, non-medical to non-medical communication protocol and medical CR sensor to administration center communication protocol, is also given.
Chapter 5 provides the validation of the simulation platform, proposed priority scheduling algorithm, system design and the network maintenance protocols. The chapter presents and discusses results of the proposed CR enabled hospital in the simulation environment. In this chapter, Verification and Validation (V&V) of preferred simulation (NS2) is presented. Afterwards, a small CR enabled hospital NS2 based simulation environment is designed.
For performance comparison,
priority enabled and non-priority hospital environment test cases are developed. Packet drop ratio, average queue delay period and average received period are considered for evaluating the proposed priority scheduling management. Along with this, the duration for new node joining and leaving is also considered to evaluate the architecture and maintenance protocols. The proposed packet drop rate results, data rate and latency are also compared with well-known queuing mechanism and wireless medical standards.
13 Finally, findings of the study whereby future works are also highlighted in chapter 6, followed by list of the publications attained through this work.
14
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
Cognitive Radio (CR) is next-generation wireless communications that can resolve the burning issues in wireless healthcare sector. The application of CR in medical wireless environment can accommodate to the aforementioned challenges. Till now, proposals and literatures on cognitive radio based healthcare are very limited. While there are numerous research efforts investigating CR and wireless healthcare respectively, the research on CR-based healthcare management remains at the beginnings stage. This chapter is a pioneer approach on a literature survey in CR based healthcare era. In this chapter, detail review on the limited updated literature on CR based hospital and healthcare system is presented. Moreover, highlighting some uncovered issues of previous studies such as fully structured hospital network architectures controlled monitoring system and medical priority. We critically analyze these proposals from network architectural, topological and medical quality of service parameters point of view. The organization of this chapter is as follows. Section 2.2 presents a summary of wireless healthcare system. Section 2.3 presents the possible contribution of cognitive radio in wireless healthcare. Section 2.4 narrates about contemporary CR driven healthcare literature review. Section 2.5 has a discussion about analysis of the CR driven healthcare system.
15 2.2
Study on Wireless Health Care System
The healthcare sector is an ideal example of how cognitive networking and cognitive network techniques can be employed to enhance the robustness, scalability and flexibility in this sector. The limitations of wireless communication for healthcare applications are EMI and spectrum scarcity [14, 15]. Most of the medical devices especially biomedical devices are very much sensitive with EMI. Another crucial is QoS provisioning of health care applications. Seamless connectivity and security has to ensure healthcare data that include the patient data, service data and facility system should have zero tolerance for unauthorized eavesdropping and intrusion [1-5, 7-9, 16]. Third and most important is that the limitation of bandwidth, because different e-health application requires a reasonable amount of bandwidth such as for hospital information management system needs 1-10 Mbps and latency below 1 sec, the telemedicine process requires at least 10 kbps- 1 Mbps and latency consideration is 10-250 millisecond [4, 5, 16-18].
On the contrary, wireless
intelligent emergency medical management system patient monitoring and physical rehabilitation system is needed 10-100 Kbps and less latency. Conventional wireless system e.g. WiFi mesh, WiMAX cannot fully ensured the above mentioned QoS for transmission because of limitation of spectrum according to FCC regulation and other issue like transmission capacity and required of BER threshold level [4-8, 10, 12].
Figure 2.1 illustrates a regular wireless healthcare facility consisting of general cabins, administration center, OT, ICU and emergency unit. The different monitoring units are connected to admin center via wireless access points. Biomedical medical devices are operated by WMTS ISM and MICS band. Wireless medical devices like different sensor, Bio-sensor and body sensor and other wireless devices cause co-interference problems if at least two types of devices operate on the same frequency at the same time in the same area. Hence, other wireless devices can cause wireless medical devices to malfunction even though they do not operate within a very close proximity. Moreover, medical devices are EMI affected by wireless transmission of the close vicinity devices. The handheld communication devices can cause undesired interference (EMI) to other medical device. That is why
16 conventional healthcare facility equipped with frequency jammer to protect those medical devices. However, this is not intelligent solution for hospital because voice communication should be a vital part for tele healthcare system [5, 8, 10-11].
Figure 2.1 Wireless medicine and healthcare system in conventional hospital There are a large number or proposals available for telemedicine that are based on wireless networking in the license-free spectrum. The well-known wireless communication standards are started from IEEE 802.11 to IEEE 802.16 that belongs to different sensors, body area network, WLAN, WWAN. One common problem with networks working in the license-free spectrum is the increasing difficulty of QoS provisioning. Currently, the license-free spectrum has been crowded by IEEE 802.11-based WLANs, IEEE 802.15-based WBANs and WPANs, and IEEE 802.16based WiMAX networks. Transmissions in the unlicensed bands can experience interference from other networks sharing the same spectrum, making it very difficult to predict the service quality. That includes spectrum utilization, security, transmission collisions, and other issues between the same or different wireless technologies, posing a major problem for a health monitoring system. Some telemedicine traffic, such as tele-diagnostic with interactive audio and video transmissions, may require very high bandwidth and strict delay and jitter requirements. Supporting this type of traffic can greatly reduce the capacity and service quality of other existing traffic. The coexistence of multiple networks in the same license-free spectrum also brings challenging issues including spectrum
17 utilization, security, transmission collisions, and other issues between the same or different wireless technologies, posing a major problem for a health monitoring system which should support traffic with strict QoS requirements. Supporting telemedicine services using the licensed spectrum may provide better QoS, although this is at a higher cost than using the license-free spectrum [1, 4-5, 9, 16, 18-19].
2.3
Contribution of Cognitive Radio (CR) in Healthcare
Cognitive radio network technology has unblocked new passages to the future wireless applications. In 1999, Joseph Mitola III introduced the concept of cognitive radio, where the main objective is to use the underutilized radio spectrum in an opportunistic manner [12, 14, 15, and 20]. Therefore, CR can be defined as an intelligent wireless device that has the ability to adjust itself according to situations, to decide the course of actions and to execute the instructions by making suitable adjustments in certain operating parameters (transmit-power, carrier frequency, and modulation strategy) in real time [12, 21]. Current researches mainly focus to facilitate cognitive radio technology for secondary access on TV white space (TVWS). This is because bulk amount of unused UHF spectrum resides in TVWS band. Meanwhile, FCC and Office of Communications (OfCom) comprehend the cognitive radio technology and suggest pursuing with the TVWS (VHF and UHF) for experimental purpose [21-23, 25]. This is because TV bands have enhanced diffusion on the frequency spectrum and seldom change frequency and location. A CR is defined as an intelligent wireless communication network of CRs, where the network can improve the end-to-end performance of the system by adaptively reconfigure its communication parameters. There are two types of user in CR, namely primary user (PU) and secondary user (SU). Primary user (PU), also known as the primary service licensed user, has the exclusive right on the radio spectrum. On the other hand, ssecondary user (SU) is the secondary service and/or unlicensed user, also known as the cognitive user, who utilizes the free spectrum opportunistically and has to vacate the spectrum band as soon as a PU appears. Observing the radio frequency environment, the segments of the free or unused spectrum band can be spotted [12-15, 22, 24-25].
18
Figure 2.2 Cognitive radio network scenarios Figure 2.2 is an illustration of how cognitive radio network works without interfering with the primary user. Identify if the presence and absence of the PU and act accordingly are the fundamental tasks for each cognitive radio terminal (CT). For a certain geographical area, information regarding the usage of spectrums and locations of primary users are required in CR. This information can be acquired by using spectrum database or by sensing spectrum [21-25]. The spectrum database can provide detailed radio environment map with advanced features such as quality of the channels. For instance, spectrum database may provide the secondary users records regarding current spectrum usage, PU‘s location PU‘s transmitting power, etc. Spectrum database also provides expected duration of certain spectrum usage. Thus, spectrum database relies on data regarding the spectrum, with the specific incumbent services and exact protection requirements. This is because, different incumbent systems have specific interference protection requirements. As suggested by FCC, cognitive radio can send a query to spectrum database regarding spectrum availability in its proximity [22-24, 26, 28].
Cognitive radio network could play vital role in future wireless healthcare system and medical telemetric monitoring system [14, 22-23]. In the conventional spectrum management, the spectrums are statically allocated to each licensed user.
19 Currently, when the number of licensed users increase, it becomes difficult to find a vacant channel for new or existing services because most of the spectrum is already occupied. However, most of the licensed spectrum is rarely continuously used all the time and in space [22]. In addition, for wireless medical devices, other wireless devices also cause EMI problems if both types of devices operate on the same frequency at the same time in the same area. Hence, other wireless devices can cause wireless medical devices to malfunction even though they do not operate within a very close proximity as previously described. For example, for WMTS medical band, most of the current medical telemetry devices operate in the 460–470 MHz band that FCC specifies as the band to be used by 2W or higher handheld and other mobile transmitters. Therefore, the handheld devices can cause undesired interference to wireless telemetry systems. To avoid this problem, cognitive radio techniques can be used in a healthcare environment [1, 22, 26, 29, and 28]. The cognitive radio systems first sense the environment before transmitting any data. Then, they find the opportunities to transmit their data without any interference with medical devices by adaptively tuning transmission parameters (e.g., transmit power and modulation technique) depending on the characteristics of the environment (e.g., locations of medical devices, RF immunity level of medical devices and channel capacity). At the same time, the cognitive radio system dynamically learns the behaviors of the medical devices and other wireless devices (e.g., to predict spectrum occupancy and probability of interference with the medical devices in their vicinity) to increase the spectrum utilization and the safety of the medical devices.
2.4
Existing CR and Priority Aware Wireless Healthcare System
Several studies have given guidelines for the use of cognitive radio in healthcare environment for interference mitigation and how opportunistic network can be used in this sector. The research or real time project work on cognitive radio based tele-healthcare system is getting a lot of attentions for the past four years. However, a complete system design, categorized the medical device as per any intelligent priority mechanism system and medical communication protocols are absent in existing proposed CR based health care system. Here we will discuss some
20 limitations of the recently proposed CR based e-health where we have considered not only the cognitive radio enabled hospital or senior healthcare environment but also CR enabled Mobile Body Area Network (MBAN), telemedicine and pervasive healthcare systems.
Phunchongharn et al. first time introduced a EMI aware cognitive radio network based hospital environment where all wireless medical devices are consider as primary device (PU) and other wireless non-medical devices that use in hospital were considered as secondary (SU). The location of device is determined by RFID network system that installed in hospital. The proposed cognitive radio based healthcare systems contains an inventory system at area 7, the cognitive radio controller at area 5, and cognitive radio clients (communicators) at area 1, as shown in Figure 2.3. The rest of the area (other than 1, 5 and 7) in Figure 2.3, contains medical devices that are considered as primary user in their proposed system. The wireless medical devices are classified into Life Supporting (LS) and None Life Supporting (NLS) manner. ELS and ENLS are the immunity level of EMI for the life-supporting and non-life-supporting device accordingly.
The EMI immunity level is required to articulate the maximum transmittable power for any CR device in the hospital area. The inventory system preserves information about all medical devices in the hospital (such as location, activity status, and EMI immunity level). The inventory system can update the real-time location information of active and passive medical devices and wireless e-health devices in a hospital environment by installing a Radio Frequency Identification (RFID) tracking system. The location information is required to control the EMI hazard in wireless hospital area. The authors have introduced an EMI aware Request To Send/Clear To Send (RTS/CTS) access mechanism protocol that is adaptive to their proposed hospital environment.
21
Figure 2.3 Cognitive radio system in a hospital environment [26-27] Though the proposed system is base station centric architecture, the author did not consider any co-operative spectrum sensing method and did not categorize the wireless medical/non-medical devices in their papers [26-27]. Same author at reference [28], have focused on transmission scheduling and power control of secondary users (SU) in multiple Spatial reuse Time-Division Multiple Access (STDMA) networks. Their objective was to maximize the spectrum utilization of SU and minimize the power consumption subject to the EMI constraints for active and passive medical devices and provides QoS of SU. They have also proposed a joint scheduling and power control algorithm based on a greedy approach to solve the multiple access control problem with much lower computational complexity. And after that, an enhanced greedy algorithm was proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling [28].
22 Doost-Mohammad et al. has proposed a cognitive radio based healthcare infrastructure and spectrum access policy on WMTS frequencies on the activity patterns of the high priority users, and the quality of service constraints of the patients information, while ensuring protection to existing higher priority transmissions and the safe operation of sensitive medical equipment as shown in Figure 2.4. They also studied the pattern of WMTS band on a specific test bed area (Boston Hospital area) and also discuss the current state of the art and the major challenges in the implementation of this new cognitive radio assisted medical telemetry paradigm. They did not considered the other medical band and any specific infrastructure for tele-healthcare in hospital system [29]. Spectrum Sensing and predefined architecture is not also mentioned in this study [29].
Figure 2.4 Spectrum ranges for WMTS bands with access priority [29] Chávez-Santiago et al. has discussed cognitive radio techniques that can be applied to Ultra Wide Band (UWB) medical body area networks to improve their coexistence with other electronic systems through frequency agility and frequencydomain spectrum shaping. Instead of Body Network Controller (BNC), they had
23 designed a Cognitive Radio Controller (CRC) that can allow multi frequency agile sensor nodes to transmit in unoccupied parts of the fused spectrum band. They are introducing spectrum broker for common control channel selection. And lastly they have a solid contribution of physical and Media Access Control (MAC) layer in their new proposed system [30].
Feng et al. has introduced an infrastructure based CR for telemedicine, where cognitive base stations sense available spectrum and forward data for associated healthcare stations. The authors has been proposed two types of spectrum sensing techniques periodic and triggered for supporting urgent and real-time periodic telemonitoring traffic in the network. The proposed infrastructure only deal the traffic such as Electrocardiogram (ECG), Electroencephalogram (EEG), blood pressure, and glucose monitoring data, for which a patient is usually continuously monitored over a long period of time. Therefore, admission control and multimedia based medical communication (e.g. tele-diagnostic and tele-consultation, biomedical signals and vital parameters) that required on short period but high throughput communication was not considered [31].
Dong et al. has proposed a robust architecture and design of a cognitive radio-based infrastructure to monitor real-time body sensor based network patients‘ vital signs, collect, and document medical information as shown in Figure 2.5. A cognitive radio should have ability to measure, sense and be aware of the characteristics of the radio channel atmosphere like availability of spectrum, power, interference, noise level, user application and other operating restrictions. Adaptive learning steps used to recognize the behaviors of not only the primary user but also the characteristics of the secondary data (like data rate requirement, transmission mode, bandwidth and acceptable BER). Based on the information of spectrum sensing and adaptive learning, CR able to understand the user parameter and predict the future space from spectrum and an appropriate channel is selected according to the channel capacity of the spectrum holes and requirement of secondary users. The information is routed to nearby CR nodes that are attached to patient bed and anywhere in hospital. CR transmits Electrical Medical Record (EMR) to monitoring zone and other medical record databases. This proposed network can control the
24 conventional wireless infrastructure and reduce the cost of implementation in hospital. Research challenges in development of cognitive radio healthcare automation network are also discussed. To allocate spectrum resource efficiently, knowledge of the location of the functioning devices is critical. To get the correct location of cognitive enabled medical device, the authors propose indoor geo location system instead of Global Positioning System (GPS) due to Non-Line-ofSight in indoor environment.
Figure 2.5 CR-BAN based medical triage, EMR DB and monitoring system Authors have also discussed the high priority medical device spectrum sharing and interfering problem and introduced graph coloring theory based on cognitive assignment. Moreover, to overcome the traditional graph coloring theory, author proposes Utility Graph Coloring (UGC) based on cognitive assignment and after simulated the result is 72% better than conventional graph coloring methods. This new techniques work in two stages and not only attempt to minimize the number of colors but also attempt to maximize the spectrum reuse and increasing the maximum number of users admittance. In first stage traditional graph coloring theory is applied to find the color of graph and in next stage the occurrence of the colors in
25 the graph is calculated. They also create a test bed and proved that dynamic spectrum access bandwidth achieved in much more than traditional Wi-Fi communication in different Network interference level [32].
Dramane et al. has proposed framework for interference mitigation in hospital context. They have labeled some defined CR node functions allowing hospital environment communications without interferences. Afterwards, simulates the proposed system and develops a test bed on their proposed a function for calculating the EMI aware transmission power and function is powered by the patient‘s location coordinates given by Grey Model technique. The authors only consider the patient communication (personal and vital monitoring) as CR in their proposed framework [33-34].
Moer et al. has presented a new architecture of a wireless communication link between ambulatory medical service and the hospital based on the concept of cognitive radios. The sender/receiver module in the ambulance was allowed to use wideband spectrum and continuously searching for suitable vacant frequency for medical data transmission [35].
Chávez-Santiago et al. has proposed a cognitive radio enabled wearable sensors in hospital where a dual-band EMI aware RTC/CTS protocols for crucial small healthcare zone such as intensive care unit (ICU) and operating theater (OT) is introduced. The author anticipated that in near future, a great number of medical body area network (M-BAN) operates in unlicensed band (such as ISM (2360MHz2500 MHz, 900 MHz and other band) that create coexisting problem with conventional medical device. In their study, CR devices are proposed to operate in 2.4 GHz with Dedicated Control Channel (DCC). Along this consideration, the author proposes to use an Additional Emergency Control channel (AECC) in different spectrum band which serves as a control or data channel for possible interferers for reducing the outage probability. The result of the proposed protocol algorithm shows that, no wireless transmission related interference occurred in large area with the traditional non communicative medical devices and the outage
26 probability is very low compare to the outage probability of small area (such as Intensive Care Unit (ICU), Coronary Care Unit (CCU) [36-37].
Dramane et al. has recently published an article on cognitive radio driven multimedia data transmission based remote patient monitoring. The author proposed an on-the-fly wireless resource reservation and ensures the improvement of multimedia transmission QoS by introducing a channel sharing decision-making process named as Model Predictive Control (MPC). In this study, the cognitive base station based relay transmission is considered where the packet loss, latency issue and spectrum hands-off strategies are carefully examined. However, the centralize transmission schemes and any proper architecture concept are missing [38].
Kahsay et.al. has drawn a comparative performance analysis between the original IEEE 802.15.6-based communication system utilizing user priority scheme and a system utilizing a traditional random back-off scheme for wireless hospital environment. The research proves that the performance of the IEEE 802.15.6 user priority scheme is approximately the same than the random back-off scheme in scenarios containing only high-priority medical data. Even in case of medical and low-priority nonmedical transmissions, the proposed priority mechanism provides satisfactory performance in terms of throughput and packet delivery ratio for medical applications [39].
Cao et al. has proposed a QoS provisioning framework for wireless body area sensor networks employing IEEE 802.15.4. This framework support three classes of service (alarm/control, command/data and routine traffic) and enable a good time constraint compliance ratio for all classes. IEEE 802.15.4 is used in the beaconenable mode, i.e. with a coordinator. The time is cut into a Contention Access Period (CAP) and multiple Guaranteed Time Slots (GTS) from the Contention Free Period (CFP). Each node is assigned a GTS to transmit its routine traffic (large periodic traffic). The traffic with the lower delay tolerance is sent in CAP, and the two remaining classes of traffic are sent in CFP. These two propositions have many advantages for healthcare applications: they support service differentiation, low latency for real-time traffic and high throughput demand. However, reliability and
27 rate control have not been really considered. The second proposition keeps a good compliance to the standard [40].
Raza et al. has proposed an optimized priority assignment mechanism (OPAM) to increase the throughput of time critical data packets for biomedical WSN devices. OPAM decides at runtime of medical device data flow based on level of its criticality. Authors propose a device classifier scheme that distingue different medical device data flows and also allocates queues according to the device priority. The scheduler mechanism is adaptively scheduled packets on the basis of the priority assigned by the OPAM. The average queuing delay for medical sensors is calculated for both priority and non-priority device queues and compare with delay threshold for conventional wireless medical devices [41].
Hyungho et.al. has developed a wireless medical priority categorized scheme based on AC of conventional 802.11e where medical applications are prioritized according to the medical urgency. The proposed mechanism provides guaranteed absolute priority to each traffic category, which is critical for medical-grade quality of service (QoS). Afterwards, authors introduce the weighted diagnostic distortion (WDD) as a medical QoS metric to measure effectively the medical diagnosis by extracting the main diagnostic features of medical signal. Their simulation result shows that the proposed mechanism, together with medical categorization using absolute priority, can significantly improve the medical-grade QoS performance over the conventional IEEE 802.11e MAC protocols [42].
Rashwand et.al has developed a prioritized bridging mechanism between the IEEE 802.15.6-based wireless body area networks (WBANs) and the IEEE 802.11e enhanced distributed channel access (EDCA)-based wireless local area network (WLAN) to route the medical data towards to medical center. They have proposed a mapping scheme for eight WBAN user priorities (UPs) and four access categories (AC) of IEEE802.11e, which ensures the medical quality of service for WBAN nodes. The results of this work indicate that the AC differentiation by AIFS outperforms the differentiation by CW in the sense that it does not deteriorate the end-to-end delay of relayed WBAN traffic and ordinary WLAN traffic [43].
28 2.5
Literature Analysis on CR and Wireless Based Healthcare System
In this section, we have discussed and critically analyzed the contemporary cognitive radio healthcare systems, based on some parameters like the architecture and infrastructure type, consideration for device in hospital environment, services categories spectrum sensing methods, common control channel, radio characteristics etc. Section 2.5.1, discusses about the analysis of architecture of cognitive radio based hospitals. Hospital device status and characteristics are analyzed in in section 2.5.2. Critical discussion on CR based healthcare QoS and communication protocols are narrated in section 2.5.3 and 2.5.4 respectively. We have created a literature review matrix (as per Table 2.1) and compared with our proposed cognitive based healthcare system. From the table 2.1, we have figured out drawbacks on current cognitive radio enabled healthcare Literatures. In proposed cognitive radio based hospital model, we have mostly covered these shortcomings.
2.5.1
Architecture for Hospital
Most of the literature supports homogeneous network architecture based infrastructure [26-29, 34]. For small healthcare facility and tele-health system homogeneous network topology is sufficient. However, for the senior health facility like hospital, it is obvious to consider the heterogeneous network and architecture [39]. In hospital, several types of stationary and mobile devices exist. To properly maneuvering these devices in the fixed and dynamic environment, hybrid network topology needs to consider. Recent cognitive infrastructure related literatures have talked about homogeneous or simple network model for cognitive radio driven hospital [26, 39-40]. This is inadequate for a large healthcare environment [40]. Different types of wireless devices operating in hospital have different standards (such as IEEE 802.11, IEEE 802.15, IEEE 802.16, IEEE 802.22) and have different network models [1-4, 40, 44-46]. Thus, it is not wise to use flat network topology for the wireless hospital zone [39-40]. Therefore, the research challenge is to design a hybrid and heterogeneous multi topology network architecture for cognitive radio enabled hospital system [26, 28, 32, 38, and 40]. Using a single controller with lower
29 transmission power, the transmission range may be problematic to cover all the hospital areas [26]. As a result, this may increases EMI orn nearby medical devices which degrade medical QoS performance [27-28]. Furthermore, a single controller and access point may not be sufficient.
It is proposed that, the architecture for wireless hospital can be designed by considering a hierarchical-based model, comprised of multiple controllers with CR capabilities in order to adjust its transmission power so the EMI effects on biomedical devices can be reduced [44]. The extended architecture of hospital can be similar to a conventional Global System for Mobile Communications (GSM) [47]. ]. In GSM system, the Base Transceiver Stations (BTSs) acts as end access point and all mobile stations (MS) are connected to their adjacent BTS. Moreover, BTSs are connected Base Station Controllers (BSC), and BSC communicates with the Mobile Switching
Centre
(MSC),
which
forms
a
multi-level
hierarchical-based
heterogeneous network topology [47] The hierarchical model provides several advantages including larger coverage area and increase the network coverage capacity for wireless healthcare service. To secure the medical reliability, medical device can directly communicate with base station in a single hop manner. On the other hand, CR non-medical device can communicate with base station in multi hop fashion. Even the non-medical devices are more mobile than medical devices. There are several challenges associated with this proposed model. For instance, it may increase the overhead (e.g. average transmission delay and control messages) due to multi hop transmissions and mobility of CR enabled devices. Moreover, every CR devices are Multiple Input Multiple Output (MIMO) radio or at least two transreceivers have to be there. Some limitations exist in single Trans-Receiver (TR) based CR [48-51]. Though (Phunchongharn et al. [26-28]) has suggested single TR as author has considered that low priority non-medical device is CR capable only. The advantage of two trans-receivers is that one radio is dedicated for continuous spectrum sensing and another is used for transmitting data. Global Common Control Channel (CCC) should be considered in cognitive healthcare system to reduce the transmission complexity [30-32, 34, 36-38]. It is also recommended that some licensed spectrum should be conserved for global CCC usage in hospital [26-28, 38]. Cognitive radio based hospital devices regardless medical or not medical, both should be equipped with MIMO for smooth operation. To develop cognitive radio
30 based hospital architecture, group theory supported clustering concept can play a vital role [52-54]. Clustering idea on complex hybrid network like wireless hospital resolves and handles many issues like device mobility, network formation and also reduced the communication overhead [55-58].
2.5.2
Device Status of Hospital Management
Medical and non-medical devices are not CR capable in most of the CR based healthcare literature. Future spectrum scarcity is one of the vital arguments of cognitive radio in wireless healthcare system. Medical bands are also limited in recent decade. To avoid that inescapable situation for future healthcare, we need to consider all medical and non-medical (for communication purpose) are CR enabled. CR enabled medical and non-medical devices in hospital might be resultant of consumes higher energy when performing the cognition attributes (such as learning, sensing and adapting with wireless environment) [14, 23, 26-29, 32-33, 35]. Moreover, cognitive radio enabled hospital devices may change its transmission parameters (such as frequency and transmission power) according to the type of operating network (e.g. IEEE802.11 and IEEE802.16, IEEE 802.22) being selected [2, 5, 48-50].
The cognitive radio enabled hospital devices escalate the complexity of circuit design of devices [51, 55-56]. To mitigate the future spectrum scarcity and keep the medical information transmission quality, all medical, non-medical communicator, and BAN should to be cognitive radio and dynamic spectrum capable. However, all hospital devices to cognitive radio capability transformation can lead a spectrum acquisition combating situation. To resolve this issue, device centric priority mechanism and categorize the devices as per their services need to be considered. Location of a device is a vital requirement of CR based healthcare system to resolve the EMI issue, device priority and transmission power calculation of CR devices [19,23 ,26-34 and 40].
31 Most of the authors have suggested RFID, GPS, indoor positioning system, Received Signal Strength Indication (RSSI) technology and gray model to locate the co-ordinate of CR device [16, 26-29, 32-34]. However, for an infrastructure based healthcare system a predefined RFID grid network could be a good solution. In order to reduce the interference to bio-medical devices, accurate detections of the physical locations of these devices is essential. RFID-based transceivers have been proposed in [26-29] to implant RFID readers and Radio Frequency (RF) tags into bio-medical devices in order to detect their respective physical locations. RFID transceiver is seemed to be appropriate due to its low-power transmissions. However, it has been shown in [27] that, transmitting wireless devices might cause interference to biomedical devices within its vicinity.
One of the cognitive radio features is adjusting the transmission parameter if necessary. Suppose, the cognitive radio has learned that the adjacent biomedical devices within its vicinity, facing EMI problem or the emitted EMI level of operating cognitive wireless device is unendurable for other device; it will dynamically change it frequency. Without accurate location information offered by RFID, there may be greater challenge to reduce SUs‘ interference to PUs, as well as to reduce the amount of overhead incurred in identifying the physical locations. In any advance hospital different type of medical non-medical devices exists. The location and service of device in hospital, a basic category of hospital device is formed; where the authors consider the medical device as PU and non-medical devices is SU [10-11, 26-29]. The authors are also classifying the medical devices as life supporting and nonlife supporting medical device where other non-medical devices are not categorized [26]. But to consider a fully opportunistic future wireless hospital environment, both medical and non-medical devices are need to be considered as cognitive radio enabled device [14-15, 20-22]. Similar to the categorization of medical devices in hospital, non-medical device also need to be characterized based on data transmission type, priority of service and location of device in hospital [59-65].
.
32 2.5.3
QoS Management for Hospital
There has been a very limited research on the enhancement of QoS performances (i.e. throughput, priority setup, latency, end-to-end delay and packet loss rate) in cognitive radio enabled hospital. CR has been applied to provide QoS differentiation for different types of traffic classes. Normally, there has been limited traffic classes in cognitive radio enabled hospital, which are normally categorized as PUs and SUs, even though higher number of available traffic classes may exist. In, four classes (i.e. real-time critical medical, real-time non-critical non-medical, nonreal time medical data and non-medical office/support applications) have been suggested in few literatures [1, 3-6, 16, 24]. However, only two traffic classes were investigated in cognitive radio based health care, in which PUs and SUs are categorized as high priority and low-priority traffic classes, respectively [22, 26, 28 and 30]. Further investigation can be pursued to increase the number of available traffic classes in cognitive radio enabled hospital. Priority-based scheduling schemes can be applied to provide context-based QoS differentiation. For instance, a traffic priority may vary with respect to the severity of the patients‘ health conditions (i.e. the degree or severity of life threatening conditions) or the importance of a particular room or zone (i.e. the degree of importance of the ICU/CCU and surgery rooms, as well as the general ward) [45-47].
A priority medical access scheme and scheduling mechanism are crucial in the development of a cognitive radio based hospital system [1-2, 4-5, 9, 14, 17, 2629, 45, 48-49]. That resolves the medical QoS problem like packet drop ratio, arrival time and end to end delay for the high priority medical data. Regarding priority management in wireless healthcare, limited researches done in CR based healthcare [26-38]. Literatures are also limited in other conventional wireless healthcare system [1-2, 6-9, 15-18, and 39-43, 66-67]. However, the conventional priority enabled wireless healthcare proposes IEEE 802.11 and IEEE 802.15.4 supported access mechanism concept where GTS is allocated for priority medical services. These literatures also consider different AC for QoS enabled wireless healthcare services [39-43, 67-68]. The CR based wireless healthcare only focus on medical service based priority management. The recent and future medical devices are operating in
33 multimode and provide multi services. Besides the location of device in hospital can also be priority determinate factor. It is considered that all hospital wireless medical or non-medical devices are not stationed at only one location and the priority of device can also be changed. Therefore, a dynamic priority policy and priority aware protocols, spectrum sensing and network maintenance scheme need to be developed for recent and future hospital care system.
2.5.4
Communication Protocols for Hospital
Like other structured network system, a fully cognitive radio based large healthcare system should be integrated with medical QoS enabled communication protocols. Majority literatures focus on the EMI effect and electromagnetic compatibility (EMC) on medical devices. This is a major shortcoming in wireless healthcare system. By using cognitive radio and dynamic spectrum access capable wireless device in hospital, the EMI problem is solved [26-28, 32-34, 36, and 38]. Thus EMI parameter should be included in transmission and communication protocols and policy. Few literatures support EMI aware RTS/CTS protocols and Signal Noise Ratio (SNR) scheduling algorithm [26-29]. Location of the device is the major obligation of these communication protocols. Other literatures narrates that a channel prediction based basic medical priority supported transmission decision making protocols [33-34]. However, to develop a cognitive radio based detail communication protocols for hospital; status, priority, position and EMI threshold of medical and non-medical devices need to be considered. Normally in a hospital, medical devices are communicated to any central monitoring, medical data center and controlling system as demand basis [1, 4-8, 26-29]. Usually no communication between medical and non-medical medical device need to consider for cognitive radio based hospital system [29]. On the contrast, the hospital communication policy for non-medical devices should be allowed to communicate with other non-medical devices or to central controlling system of hospital. Non-medical devices can retrieve any data from medical devices from central controlling [55]. The analysis of contemporary literature review and Table 2.1 as follows:
34 Table 2.1: CR based healthcare literature review Matrix
35 2.6
Concluding Remarks
Cognitive radio is very recent approach in wireless communication era. Moreover, the medical application in this newborn communication field is also very recent. In this chapter, literatures on cognitive radio-based healthcare and priority aware wireless healthcare are reviewed. Some critical concerns on traditional wireless healthcare system are addressed such as architectural and topological issues on recently proposed CR-based healthcare system, reduction of EMI to medical devices, particularly life-threatening bio-medical devices, and the enhancement of QoS performances (i.e. throughput and delay). Critical analysis on these literatures is also presented. Whereby, the justification for proposed hospital system design, priority management and system architecture has been highlighted.
36
CHAPTER 3
SYSTEM DESCRIPTION AND PRIORITY MANAGEMENT
3.1
Introduction
This chapter discusses the proposed system design of cognitive radio based hospital, known as CogMed. To overcome the shortcomings regarding hospital device status, medical and hospital QoS that is stated in Section 2.4, Subsection 2.5.2 and Subsection 2.5.3, this chapter introduces a novel system description, hospital device categorization scheme and a dynamic medical priority mechanism for cognitive radio based hospital. Hospital device classification, medical QoS and system design in contemporary CR based healthcare has limited discussion as observed in Chapter 2. The wireless medical and non-medical devices of cognitive radio driven hospital devices are categorized based on data transmission pattern, location and priority of medical services. Moreover, the medical radio resource management and a device centric dynamic priority management for the proposed CogMed are also discussed.
The proposed dynamic priority management policy copes with a device transmission access class mechanism that is similar with IEEE 802.11e wireless priority scheme. The CogMed priority scheme is also harmonized with a proposed queuing algorithm where two level of queue is considered for priority and nonpriority hospital devices. Section 3.2 narrates the methodologies of this chapter. Section 3.3 describes the definition and device status for a cognitive radio based hospital. Radio resource management narrates in Section 3.4. Radio resource management bridges between system definition and CogMed priority management. Section 3.5 describes the proposed CogMed priority definition, management and a
37 scheduling algorithm. Radio resource or spectrum management for hospital is mapped with device categorization and proposed priority mechanism.
3.2
Methodologies for System Design and Priority Management
The section describes the methodologies for this chapter. Sub Section 3.2.1 and 3.2.2, provide the methodologies for the system design and priority management accordingly.
3.2.1 Methodology of CR Based Hospital and Device Categorization
A cognitive radio capable entire hospital network system is proposed where all devices anticipated to be DSA aided. A centralized controlled network model is introduced in this study. The hospital devices are separated based on their service and locations in hospital. The CR enabled devices are categorized mainly medical and non-medical accordingly. Furthermore, these medical and non-medical devices are also characterized based on devices activities and locations. The proposed device categorization and hospital design follows the FDA prescribed wireless hospital design and device reliability.
3.2.2 Methodology of Priority Management Policy
A priority management is vital assignment for wireless healthcare. Since the high priority medical data is delay sensitive. Thus, it is necessary to develop a priority based transmission scheme for wireless healthcare system. The proposed cognitive radio based healthcare system has different type of cognitive radio enabled medical and non-medical devices. Our aim is to provide a better QoS and ensure the reliability on emergency medical data transmission. In this article, priority scheme is
38 proposed for medical and non-medical device based on queuing theory and access mechanism similar with IEEE 802.11e.
Queuing system is considered for use in a delay-guaranteed wireless network, in which all devices are transmitting towards a unique destination. In any network, queuing system has maintained the packet scheduling mechanism of egress port of access point. As per IETF RFC 1046, the definition of queuing is a list of jobs that are waiting for process in sequential manner and scheduling process narrates how much time would be allocated for process. In network management system, the queuing management of network traffic and scheduling algorithm reduce the network congestion, packet drop from queue buffer and reduce the end to end packet delay.
Queue concept is based on Little‘s theorem, which states a relation between the mean number of network traffic in the systems, mean arrival rate and the mean response time. Let λ(t) denote the number of packets arrived into the system in a time interval (0; t), and let μ(t) denote the number of departed packets in (0; t). Assuming that N(0) = 0,the number of packets in the system at time t is N(t) = λ(t) - μ(t). Let, the mean arrival rate into the system during (0; t) be defined as ρt = λ(t)/ μ(t). The mean arrival rate (ρt) also referred as utilization rate of a simple m/m/1 queue model. This model consists of queuing station where jobs arrive with a negative exponential inter arrival time distribution with rate λ. Furthermore, the job time service requirements are also negative exponentially distributed with mean E[S] = 1/ μ. This model also derives the mean response time of system E[R], considered that the system is in stable state, i.e. ρ= λ/ μ > 1. So, E[R] = E[S]/ (1- ρ); or response time is equal to 1/ (μ- λ).
Normally in wireless technology era, priority queue is well recognized. The disadvantage of this scheduling (queuing) is that if the number of same weighted packets exceeded the queue limit, the high priority packet might be dropped. Considering this situation, a hybrid queuing mechanism is included in proposed system. The proposed queuing method and scheduling mechanism are also mapped with IEEE 802.11e and RFC 2309 [68-71].
39 3.3
Development of CR Based Hospital and Device Categorization
This section describes the system definition, design and categorization of medical and non-medical devices of CogMed to follow the device service types and locations in hospital. The cognitive radio network based hospital (CogMed) is equipped with cognitive radio enabled wireless medical devices, medical sensors, cognitive radio network controller (CRNC) based mobile body area network (BAN), non-medical wireless communication and other hospital based facilitators. All cognitive radio enabled medical and non-medical devices are communicated to hospital administration center and other devices through cognitive base station (CBS) backhaul system. The definitions of different cognitive radio enabled hospital device and CBS is narrated in section 3.3.1 to section 3.3.5.
In the proposed system, wireless devices are mainly classified into medical devices and non-medical devices. The non-medical devices consist of medication or pharmacy information kiosk, wireless communicator (for doctor and nurse), video conferencing, RFID reader and surveillance system. For simplicity, the entire cognitive radio enabled wireless hospitals are divided into five main parts. The splitting up of cognitive enabled hospital devices are based on characteristic of device, label of service and service category. The divisions are as follows:
1. Cognitive radio capable base stations (CBS) 2. Administration (Admin) Center 3. CR enabled medical device 4. CR enabled non-medical device 5. Cognitive radio aided healthcare sensor network (CRSN) and Cognitive Terminal enabled Sink Access Control (CT-SAC)
3.3.1
Cognitive Radio Capable Base Stations (CBS)
The main purpose of CBS is to be used as an access point for all cognitive enabled medical/non-medical devices and CT-SAC. CBS is also acting as a fusion
40 center or spectrum decision maker for all cognitive devices including CT-SACs. Every device (regardless medical, non-medical) is capable to sense free spectrums and the presence of the primary devices within its range. Subsequently, every device sends the spectrum report to cognitive radio based station system (CBS) for appropriate spectrum decision by considering the EMI threshold level of different device within the range of operating device .The base station (CBS) must use high gain, high transmission capable and large coverage area antenna due to reduce the deployment cost and to get rid of network routing complexity. It is considered that CBS is equipped with DSA enabled MIMO TR. As indicated in Figure 3.1, the CBS are deployed in partial mesh topology manner. The network redundancy is also assured by intra backhaul wireless connectivity. It is assumed that, each CBS is deployed in such a way that within its vicinity or operating range, nodes are wirelessly connected to each other in triangular formation as shown Figure 3.1. The CBS creates a partially connected mesh network rather than star topology and a cluster networking approach is proposed to avoid single point of failure (SPF). The cluster head of CBS can be elected by considering the location priority, cardinality of each CBS and number of sensed channel.
3.3.2
Hospital Administration Center (Database, Monitoring Cell)
According to Figure 3.1, the upper rightmost portion is called administration, database (DB) and monitoring cell. This cell communicates with CBS backhaul system wirelessly .It is also considered transmission redundancy for administration center due to the high transmission load. This hospital administration center is equipped with different administrative modules, different database for medical (e.g. Electrical Medical Record EMR) or non-medical information (location, EMI inventory) and medical application interface (API) for devices.
41
Figure 3.1 System design of cognitive radio enabled hospital system The EMI inventory contains the EMI immunity level of medical devices. The EMI inventory can be updated the location information device by the help of RFID system of proposed hospital system. Location registers provide the information of current location of devices and coverage of CR enabled (medical/non-medical) devices. The spectrum broker for leased common control channel and a real time
42 spectrum occupancy database are also part of this administration center system. In the proposed system, the concept of spectrum broker is introduced where the broker preserves few licensed frequencies for common control channels. It is also assumed that these frequencies can be reused in different CBS, CT_SAC and cluster vicinity at same time. It is not a reliable or practical approach for cognitive radio based hospital, if the common control channels need to go through the spectrum sensing process. In other word, the emergency services like hospital, healthcare common control channel should be global or predefined. The entire hospital could be equipped with RFID reader and all devices (both static and dynamic device) are RFID tagged. Here, multilevel monitoring system and API for different level of secured access also exist.
3.3.3
CR Enabled Medical Devices
Different types of wireless medical devices, regardless of whether the devices are life supporting and nonlife supporting, are presently used in the hospital environment. The CR medical devices are MIMO TR capable. These life and nonlife supporting medical devices are categorized into real-time and non-real-time (store-forward) medical transmission. Medical devices are categorized based on their different properties, locations and current activates. As an example, medical device such as ECG, EEG monitors can be used in can be treated as a life supporting device in an Operating Theater (OT) while it will treated as non-life supporting device in the general ward. Thus, the cognitive enabled medical devices can be categorized as follows:
3.3.3.1 Life Supporting Real Time Medical Devices
These wireless real-time life supporting medical devices (MD-RT_LS) are directly supporting human life and are normally positioned at OT, ICU, CCU and other emergency care unit. These medical devices are responsible for real-time critical medical data transmission. There are some medical devices that deals with
43 real-time data and operates both in critical or regular medical care such as cardiac attack prevention unit, incubators, defibrillators are unique lifesaving equipment working on real-time environment and dealing with real-time data (streaming).
3.3.3.2 Life Supporting Non-real Time Medical Devices
The life supporting non real-time medical devices (MD-NRT_LS) transmits diagnosis data of patients from critical medical zone like neonatal care, critical care unit , ICU, emergency care unit and OT in non-real-time manner like store and forward method (EEG), periodic data forward (pulse oximeters (SO2) capnometers, hematology analyzers) are considered in life supporting non-real time medical device group.
3.3.3.3 Non-life Supporting Real Time Medical Devices
The real time life supporting devices can change the status to non-life supporting device (MD-RT_NLS) category by considering their situation and location. The situation and location information are retrieved from the hospital administration database center. The devices that are located at Outdoor Patient Department (OPD), general ward and non-emergency section could be indicated as non-life supporting real-time medical devices. In contrary, some devices are regarded to be non-real life supporting even though they are in critical medical location (such as medical video conferencing and endoscopic machine).
3.3.3.4 Non-life Supporting Non-real Time Medical Devices
The medical devices used in regular medical points (such as homecare, general ward, examination room) and are delivering non-real-time data, are considered as non-life supporting non real time medical devices (MD-NRT_NLS).
44 The example of these devices are EMR store forward system and drug/pharmacy information system.
3.3.3.5 Emergency Telemedicine Category
It is considered that, a large hospital management system must have wireless communication based remote consulting, medical monitoring service and emergency situation care unit. In the proposed hospital management system, all tele-medical devices (e.g. Wide Regional Area Network (WRAN) based tele-stethoscope, video/tele conference and other wired medical devices) and other wireless telemetric monitoring devices are considered as cognitive radio capable transmission system. There are two types of telemedicine system exist, They are long hop standalone telemedicine kiosk and wireless equipped ambulance service accordingly. These emergency real-time or non-real time devices (ET_RT/NRT) are getting the highest priority for acquiring spectrum from the spectrum historical data base which is located at the administration center.
3.3.4
CR Enabled Non-Medical Device
The non-medical devices (doctor/nurse PDA and other device for hospital management purposes) are divided as real time streaming or non-real time store and forward manner. The CR non-medical devices are MIMO TR capable. To escalate network system robustness and manage the non-medical mobile devices, clustering network concept is introduced. Non-medical devices have multi hop communication with cognitive base station. A cluster based organization for these devices can provide efficient way of communication. Cluster Head (CH) and Cluster Member (CM) are the components of proposed cluster for non-medical device network. CM can forward their information to CBS and administration center through the CH. The selection of the CH is based on low mobility attitude, short distance from CBS, greater coverage (high Tx power) capability and device high priority. The nonmedical devices are further categorized into two parts according to the device
45 transmission mode. The descriptions of two categories of non-medical devices are given as follows:
3.3.4.1 Non-medical Real-time Devices
Non-medical real-time devices (NMD-RT) are able to communicate each other in the form of voice and video for medical and Health Information System (HIS) in real-time manner.
Such devices are surveillance system, doctor/nurse
communication devices (PDA), internal wireless softPBX system and other type of audio video communicators.
3.3.4.2 Non-medical Non-real Time Devices
The store and forward mechanism based devices that are used in health information system (HIS) and medication purposes in a hospital are called nonmedical non-real time devices (NMD-NRT). The examples of non-medical non-real time devices are tele-printer, e-medical transcription access point, Point of Sale (PoS), bar code reader, HIS store/forward, e-prescription kiosk and RFID reader, etc. RFID reader detects the current device location and sends the data to central location register, which resides at the administration center.
3.3.5
CR Enabled Medical Sensor and Sink Access Control (CT-SAC)
Low powered sensor driven biomedical devices are crucial requirement for medical and clinical issues. In the proposed cognitive radio based hospital system, DSA enabled cognitive medical sensor is introduced. To embed DSA in wireless sensor network, a Cognitive Radio (CR) is installed in each sensor node. That sensor network is denoted as a cognitive radio sensor network (CRSN). There are many challenges associated with this technology including spectrum sensing and protocol
46 designing. In this proposed work, a sensor network for CR based hospital management is developed. Here, it is considered that cognitive radio is a capable multimedia medical sensor for real-time transmission and is also a delay sensitive medical sensor for store forward transmission. In this system, medical sensors are divided in different group according to their activities, which are described in Table 3.1.
Table 3.1: Characteristic of different sensor with location information [1-2, 16-17] Data Sensor Type
Sensor Name
Place
Type
Measured Parameter
Life supporting sensor real-time (LSS_RT)
AMON, ,ECG, EEG,ECG Module, Peak flow meter, Polysomonograph
Emergency, ICU, CCU, OT , Neonatal care unit
Real time or streaming
Heartbeat rate , respiratory flow, heart murmur, Brain electrical activity
Life supporting sensor non-realtime (LSS_NRT)
EMG ,EOG,AMON (SpO2, BP, temperature) , pulse oximeter, Swallable capsule( Radio Pill)
Emergency, ICU, CCU , OT, Neonatal care unit
Store forward , Periodic
Blood oxygen saturation SpO2 , blood/ intestinal /gasto- intestinal pressure, chemical composition of breath/fluid
Non-life supporting sensor real-time (NLSS_RT)
AMON, ECG, EEG, ECG Module, Peak flow meter, electric stethoscope
General ward, Cabin, OPD and examination room
Real time or streaming
Heartbeat rate , respiratory flow, heart murmur, Brain electrical activity
Non-life supporting sensor non real-time (NLSS_NRT)
Polysomonograph, EMG,EOG, AMON(SpO2, BP, temperature) , pulse oximeter, electronic nose
General ward, Cabin, OPD and examination room
Store & forward, Periodic
Oxygen saturation SpO2 , blood/ intestinal /gastointestinal pressure, chemical composition of breath/fluid
Rehabilitation purpose sensor real- time (RS_NRT)
Polysomonograph ,AMON(SpO2, BP, temperature), heat sensor
Patient Body
Real-time and streaming
Heart rate , heart murmur, continuous blood pressure rate, continuous temperature recording
Rehabilitation purpose sensor non-real- time (RS_NRT)
Polysomonograph, EMG, EOG, pulse oximeter, electronic nose
Rehabilitation Center and Patient body
Store & forward, Periodic
Blood oxygen saturation SpO2 , blood pressure
Mobile body area network sensor (MBN_RT/NRT)
Polysomonograph, implant body sensor, implantable pressure and different radio pill.
Patient Body
Real-time ,streaming and store forward using CBNC
Heart rate , heart murmur, continuous blood pressure rate, continuous temperature recording, Brain electrical activity
The medical sensors are categorized into life/non-life, rehabilitation purposes and MBAN. All of these sensors are working in real-time and non-real-time manner. These cognitive enabled sensors have direct single hop communication link with cognitive radio capable sink access control (CT-SAC). This CT-SAC is considered as local base station and spectrum decision maker or fusion center for the sensors
47 within its vicinity. The sink access stations are wirelessly (baud-band) connected with CBS backbone of hospital network as shown Figure 3.2.CT-SAC are categorized into two parts, that is, lifesaving access (CT-SAC_LS) and non-life saving (CT-SAC_NLS) access zone.
Figure 3.2 CogMed facilitated by cognitive radio enabled sensor
48 As shown in Figure 3.2, the red color CT-SAC is denoted as lifesaving access control. Only lifesaving/supporting medical sensor is able to get the spectrum decision and telemetric transmission through its adjunct red colored CT-SAC. On the other hand, green CT-SAC are responsible for non-critical non- life supporting sensors transmission and are dealing with non-life supporting medical and telemetric data. Different non-life supporting medical sensors, which are used for rehabilitation purposes communicate with user and administration center using this green CT-SAC. Figure 3.2 illustrates the system design and the proposed network architecture of medical sensors in hospital and different level of priority consideration based on the location and the functionality of the sensors. The green sink access points are cognitive radio enabled and single hop communication with CBS trunked backbone.
3.4
Medical Radio Resource Management
Cognitive terminal (CT) enabled medical, non-medical device, CT-SAC and medical sensors continuously sense the presence of PU and the vacant space at a given time and frequency. The device starts sensing from the lower band if it wants to communicate with receiver (mainly monitoring cell and application API, other CT enabled devices). We consider that other than medical sensors and MBAN, all cognitive enabled medical and non-medical devices including CT-SAC sense a range of temporal or spatial Spectrum Hole (SH) and the presence of PU. Therefore, CT sends the sensing report along with current location of device and other features to administration center via its nearby CBS. The adjunct CBS as well as administration center keep the real time tracking of spectrum mapping and create a spectrum historic database. It is considered that some licensed spectrums need to be fixed for common control channel. This approach is known as spectrum broker.
Those
licensed spectrum can be reused in different cell or CBS based coverage zone as global common control channel (CCC). Once, Spectrum Sensing Functionality (SSF) is occurred by any CT node including CBS, the sensed free or vacant spectrum compare with spectrum mapping database. This spectrum information is located both in administration center and CBS. If free spectrum is not available, then SSF goes for local sensing methods which means that the end device senses the vacant spectrum
49 and send the report to CBS or CT-SAC (in the case of CT enabled sensor). CBS takes the spectrum decision using centralized co-operative sensing method considering the location and priority of devices. CT-SACs act as decision maker of sensors within its vicinity, and set the priority to sensor nodes (depends on location and level of criticality) and to the transmission propagation.
It is also assumed that, non-real-time life supporting, nonlife supporting and emergency telemedicine devices only sense free spectrum from the WMTS band (608-614, 1395-1401, 1427-1432 MHz). Since, WMTS band is not capable for transmitting the real time (streaming) data. On the contrast, other medical and nonmedical devices can select vacant channel from any band (apart from WMTS) for transmission (as per Figure 3.3 and Table 3.2).
3.5
Priority Management
A medical priority scheme is included in the proposed system to support the properly explain category of hospital device and also support the medical QoS. Section 3.5.1 and 3.5.2 describe the priority system definition, mapping with proposed device category and priority access mechanism respectively. A queuing management based priority scheduling algorithm is developed in section 3.5.3.
3.5.1
Proposed Priority Policy
The devices are categorized according to the proposed medical priority benchmarking is shown in Table 3.2 and Figure 3.3. Considering the recent research situation and reality, this section proposes a device centric and location aware priority policy for cognitive based hospital system. In this study, proposed priority access mechanism compare with IEEE 802.11.e AC policy, where proposed priority categories is tried to map with IEEE 802.11.e‘s AC. Besides a queuing order algorithm is also included in CogMed where a two stage queuing is proposed. This
50 QoS measurement of device centric dynamic priority mechanism is also followed IEFT RFC 4594.
In proposed model, the priority mechanism for two level of vertical network is considered. CBS is considered as the backhaul of the first network level which the cognitive radio enabled hospital devices and CT-SACs. At the same time, CT-SACs can also be considered as the access point of CRSN that can be considered to be the second network level. As shown in both Figure 3.1, and Figure 3.2, the CBS are static and CT-SAC might be either static or dynamic. CT-SACs are communicating to administrative center through centralized CBS backbone.
Table 3.2: Priority level of proposed hospital device Device Categories
Transmission
Spectrum
Priority
Data Type
Selection
Level
Life supporting
Real-time
Medical Device
Medical data
Life supporting
Non-real-time
Medical Device
Medical data
Non-life supporting
Real-time
Medical Device
Medical data
Non-life supporting
Non-real-time
Medical Device
Medical data
Non-Medical Device
Real-time Non-
Any Band
High
WMTS
High
Any Band
Middle
WMTS
Low
Any Band
Middle
Any Band
Low
Any Band
High
WMTS
High
medical data Non-medical Device
Non-real-time Non-medical data
Emergency and Telemedicine
Real-time Medical data
Emergency and Tele-
Non-real-time
medicine
Medical data
51 The proposed CT-SAC based priority mechanism for different type of sensors is shown in Figure 3.2 This includes the ranking of the inner priority level of CTSAC based sensor from P1-P3. Here, P1 or high level is dedicated for life supporting sensor and MBAN in real time mode. Middle level priority (P2) is allocated for nonreal-time life supporting and non-life supporting real time sensors. Nonlife supporting non-real time sensor, rehabilitation purpose non-real time sensor that is on the store forward data transmission mode, are categorized as low priority or P3 (as per Figure 3.1 and Figure 3.3). Summary of the priority levels of proposed hospital devices is shown in Table 3.2. As indicated in Table 3.2, Figure 3.1 and Figure 3.3, the life-supporting medical devices and emergency/telemedicine devices are ranked as the high priority in terms of spectrum sensing and data transmission. The red colored transmission (high priority transmission) is allocated for lifesaving medical devices, emergency telemedicine services and life supporting CT-SACs.
On the contrary, the real time data transmission enabled nonlife medical devices and non-medical devices are given the middle priority. Blue transmission or middle level priority for abode mention devices. Rest of the medical and nonmedical devices are considered as lower priority. Non-real-time non-life supporting medical device and non-medical devices are in this group. The proposed priority policies also suggest that the priority level of CT-SAC can be changeable depending on the data transmission of different level sensors. If the high priority CT-SAC is transmitting the middle priority sensor information, that CT-SAC is changed it status to middle group and blue transmission as shown in Figure 3.2.
52
Figure 3.3 Block diagram of priority management for proposed system
3.5.2
Priority Access Mechanism
In this study, the priority system model following the IEEE 802.11.e application scheduling access mechanism is proposed. To support prioritize and real time data transmission in the IEEE 802.11 MAC protocol, the IEEE 802.11.e has
53 been standardized. The IEEE 802.11.e has a new channel access mechanism called the Hybrid Coordination Function (HCF), which is the combination and some enhancements of Distributed Coordination Function (DCF) and Point Coordination Function (PCF). IEEE802.11e introduces the priority of the devices by announcing four ACs. Each packet from the higher layer reaching the MAC layer is plotted to an AC for a prescribed priority. AC 3, AC 2, AC 1, and AC 0 are for voice, video, besteffort data, and background traffic, respectively as defined in IEEE 803.11e. To compare with the proposed priority definition with IEEE802.11.e, the highest priority life-medical can be mapped with AC3, high priority nonlife supporting medical device is plotted with AC2 and onwards. The conventional super frame consists of two periods; Contention Access Period (CAP) and Contention Free Period (CFP). With HCF, contention free period continue following by contention access period. Contention free period is a guaranteed time slot. All medical and nonmedical cognitive devices are located within the hospital network. CBS is the coordination point that initiates beacon and transmission period.
It is considered in the proposed access scheme, only the highest priority medical devices are getting the guaranteed time slot (CFP), and the Contention Window (CW) based CAP is allocated for highest priority non-medical data/ stream, middle and low priority medical and non-medical devices. The higher priority devices have low back-off time, Arbitrary Inter Frame Space (AIFS) and CW value. On the other hand, lowest priority devices are getting high back-off time, AIFS and CW value. To differentiate the traffic types, the Enhance Distributed Channel Access (EDCA) uses a set of AC specific parameters. These parameters are denoted as EDCA value. In that case, the guaranteed time slots of super frame are allocated for the life-saving real-time medical devices, alarm systems on critical zone (from ICU, CCU) that ensures the medical data reliability. The rest of the devices can access the super frame time slot as like the EDCA value of devices.
54 3.5.3
Priority Queue and Scheduling Algorithm
All cognitive radio enabled devices communicates through CBS. For that reason a priority driven and a queue management capable transmission admission and exit scheduling mechanism is necessary. The proposed scheduling algorithm and queuing management is deployed in each CBS end. In networking system, the queuing management of network traffic and scheduling algorithm reduce the network congestion, packet drop from queue buffer and reduce the end to end packet delay. We consider that hospital traffic is divided into four device priority based groups based on IEEE 803.11e access class (AC) concept and also similar to IETF RFC 4594 DiffServ Service Classes. RFC 4594 DiffServ Service class is comparable to IEEE803.11e traffic AC. A "service class" represents a set of traffic that requires specific delay, loss, and jitter characteristics from the network. Service class definitions are based on the different traffic characteristics and required performance of the applications/services. Active Queue Management (AQM) is considered as the major candidate to represent the status of RFC 4594 supported service class. AQM is a generic name for any of a variety of procedures that use packet dropping or marking to manage the depth of a queue.
To cope up with the proposed priority access mechanism, IEFT RFC 2309 supported AQM capable priority scheduling mechanism algorithm is proposed. Frist in Frist out (FIFO) is the simple form of queuing management where if the queue buffer is filled then the last packet drop from end which causes more packet drop and effect on transmission reliability. To mitigate these problems, RFC 2309 placed some recommendations to avoid queue lock-out, queue full problem and anticipated an active queuing method is called Random Early Detection (RED). This RED method is based on drops of incoming packets probabilistically or random drop of packet on full queue. This active queue follows and utilizes the advantage of Weighted Class Based or Fair Queue (W-CB/F_Q). In W-CB/F_Q, packets are classified and placed in queue as per ―best effort‖/ ―priority service class‖ traffic and weight of the packets [59-60]. In this system, all incoming packets are facing a combat situation. However, for medical and hospital network, only W-CB/F_Q or FIFO is not appropriate rather than a hybrid queuing based scheduling mechanism is
55 well fitted. Because the critical medical data should be scheduled by facing any fighting situation where packets drop and reliability is subjective. However, W- WCB/F_Q or combat soft queuing methods can be appropriate for the other noncritical hospital data. So a robust transmission system should be developed for hospital management where queue management architecture manages the length of packet queues by dropping packets when necessary or appropriate, while scheduling algorithms determine which packet to send next and are used primarily to manage the allocation of bandwidth among flows.
Figure 3.4 System diagram priority queue mechanism
The proposed AQM enable scheduling algorithm is deployed in each CBS. In Figure 3.4, a system diagram of proposed queue mechanism is illustrated. Two types of queue are considered where the FIFO queues for the critical high priority and a W-CB/F_Q for other priority devices. The incoming data flows from different devices are filtered by a priority classifier. The critical high priority data route to
56 high priority FIFO and rest of the data forwarded to another classifier where a weighted value is appended on the data packets. Afterwards, these packets are queued in W-CB/F_Q for transmission.
The proposed two stage queuing system is combined with first-in-first-out (FIFO) queue and weighted class base or fair queue (W-CB/F_Q). Data packets route from different medical and non-medical devices towards to CBS. The priority classifier (PC) separates the critically high priority packet (CHPP) and high/mid/low priority packet (HMLPP). We consider two types of queues, whereas the CHPP are queued in the high priority FIFO queue (HPFQ) and HMLPP devices in W-CB/F_Q. A weighted value (VW) is appended on HMLPP before it takes place in W-CB/F_Q. Because HMLPP contains different priority data so it is mandatory to rank theses packets considering the when they appears to W-CB/F_Q. If the high priority FIFO queue filled-up, the overhead CHPP routes to W-CB/F_Q with highest value (VW (MAX))
and waiting for next transmission in egress port. If the W-CB/F_Q is fully
occupied then lowest weighted packet (VW (MIN)) will drop, rest of packets of this shifted accordingly and VW
(MAX)
enabled CHPP from FIFO queue that places the
egress point of W-F/CB queue. On the other hand, overflowed VW enabled CHPP from HPFQ placed in W-CB/F_Q and waiting for next transmission. The proposed scheduling algorithm for CogMed is shown in Table 3.3.
57 Table 3.3: Proposed scheduling algorithm % W-CB/F_Q = Weighted Class Based/ Fair Queue % HPFQ = High priority FIFO queue % PC = Priority Classifier % CHPP = Critically High Priority Packet % HMLPP = High/Mid/Low Priority Packet % VW = Weight of data packet % VW (MAX) = Maximum weighted value % VW (MIN) = Minimum weighted value % Q1[i] = Number of Slot of HPFQ % Q2[i] = Number of Slot of W-CB/F_Q % N= maximum limit of queue % MD = medical device % NMD = Non-medical device 1. Begin 2. Data packet flow from MD and NMD to PC 3. PC checks the status of data packet 4. if (data packet = HMLPP) 5. VW assigned to data packet 6. Data packet queued to W-CB/F_Q according to VW 7. else 9. Data packer forwarded to HPFQ 10. if (Q1[i] ≤ N) 11. Overhead CHPP assigned to VW (MAX) 12. if (Q2[i] ≤ N) 13. VW (MIN) enabled packet drop from W-CB/F_Q 15. Rest of packet of W-CB/FQ shifted 1 step back 16. Overhead CHPP queued in W-CB/F_Q for transmission 17. else 18. Overhead CHPP queued in W-CB/F_Q for transmission 19. endif 20. else 16. data packet queued in HPFQ and waiting for transmission 17. endif 18. endif 19. Packet arrives at egress port and data transmission begins 20. End
In the above algorithm, the priority classifier separates high/mid/low priority data i.e. HMLPP. The high priority data is queued in high priority FIFO queue and other moves to weighted class based/fair queue W-CB/F_Q. Besides if the FIFO is fully queued, the extra packet routes to W-CB/F_Q with highest priority. Figure 3.5 represents the block diagram of proposed scheduling algorithm.
58
Figure 3.5 Block diagram of scheduling algorithm As shown in Figure 3.5, the main classifier categorizes the packets from incoming traffic (hospital and medical data). Critical high priority level devices such as MD_RT_LS, ET_RT (sub section 3.2.3) are nominated for high FIFO queue. Besides, rest of the traffic routes to W-CB/F_Q where a weighted value (VW) is
59 assigned to each traffic packet based on transmitted Device Priority (DP) and Location Priority (LP). Table 3.4 presents the DP value of different prioritize medical and non-medical devices. The categorization of devices is based on the definition of cognitive radio based hospital on sub section 3.3.3, sub section 3.3.4 and sub section 3.3.5. Besides, priority assignment is based on sub section 3.5.2 and scheduling algorithm scheme is illustrated in Figure 3.4. Based on the location priority (LP), cluster formation on CBS backhaul is formed that narrates in Chapter 4.
Table 3.4: Device priority assignment table Priority Level
Device Name
Device Ranking (DP)
ET-RT, MD-RT-LS
Critical
High Priority Device (HPD)
MD_RT_NLS, NMD_RT, ET_NRT
75
Middle Priority Device (MDP)
MD_NRT_LS, , CT-SAC_LS
50
Low Priority Device (LDP)
MD_NRT_NLS, NMD_NRT, CT-
25
Critically High Priority Device (CHPD)
SAC_NLS
Table 3.5 describes the priority ranking of location area (LA) for each CBS. CBSs are deployed in a hospital such that all devices of the hospital are within the range of CBSs. For example, if a CBS Location Area (LA) or vicinity covers 2 or 3 critical medical areas such as CCU, ICU and OT, then the LA is marked as high critical area with high priority. On the other hand, if no critical area exists in a LA, then it is defined as low priority area. Depending on the location information, location priority is set. For simplicity, high, middle and low location priorities are assumed 3, 2 and 1, accordingly as shown in Table 3.5.
Table 3.5: Location area priority assignment table Location Area Priority
Priority Level Ranking (LP)
High Priority Area
3
Middle Priority Area Low Priority Area
2 1
60
A hospital traffic value (VW) equation is calculated considering the device priority (DP) and location information (LP) from Table 3.4 and Table 3.6 accordingly.
Vw =
(Equation 3.1)
The condition of this equation 3.1 is considered to avoid the identical value formulation for hospital traffic. In the next stage, the valued traffic routes to a preassigned queue and waits for transmission. To mitigate the packet drop rates from high priority FIFO queue (Qi), a redundant approach is included. The overhead traffic from the Qi is routed to W-CBQ with maximum weight (VW (MAX)) and waits for immediate next transmission.
To validate the algorithm, the queuing period (QTS) for of different prioritize devices need to be calculated. Thus, the QTS is calculated from the below Equation 3.2 of node en-queue (Qin) and de-queue (Qout) time. The equation is:
QTS =
(Equation 3.2)
Where, H = CBShop OR NMDhop + CBShop (Medical device) (Non-medical device) H = Number of hop CBShop = Number of CBS hop NMDhop = Number of non-med device hop
It is assumed that for medical device, only one hop to CBS is considered. On the contrast, multi hop is considered for non-medical devices. As per the proposed algorithm, QTS for medical device should be lower than the non-medical device. Along with this phenomenon, packet drop rate and received time of high priority medical packets should be lower than the mid or low priority non-medical packets.
61 3.6
Concluding Remarks
In this chapter, the proposed system description cognitive radio based hospital is presented along with hospital device categorization. A medical priority mechanism policy and queuing based medical traffic scheduling algorithm is also introduced. The proposed scheduling algorithm aligned with IETF RFC 2309. This chapter also discusses a radio resource management for the cognitive radio based hospital system.
62
CHAPTER 4
NETWORK ARCHITECTURE
4.1
Introduction
In order to alleviate the shortcomings of the current cognitive radio based healthcare network architecture, are highlighted in Section 2.4 and Section 2.5.1, this chapter has introduced a novel hierarchical based architecture. Methodologies of this chapter are discussed in Section 4.2. In Section 4.3 of this chapter provides detailed explanation about the network model that is considered in the research. The descriptions of the proposed architecture and cluster formation technique have been discussed later in Section 4.4. A network maintenance scheme is presented in Section 4.5, where node joining and node moving algorithms are presented. Section 4.6 comprehends communication protocols for the proposed cognitive radio based healthcare system where three types of communication Protocols are described namely medical device to administration center, non-medical to non-medical device communication and CR sensor to administration center, Respectively.
4.2
Methodologies for Network Architecture and Communication
To ensure the sustainability of hospital network, architecture, model, maintenance and communication protocols are introduced in the proposed cognitive radio based hospital. Here, a network hybrid topology method is considered to confirm the robustness of proposed hospital system. Here, the base stations are creating a partial mesh cluster network and other medical and non-medical devices in
63 the hospital are connected to base station backhaul system in star topology manner. Moreover, non-medical devices are created mobile cluster network in multi hop manner within the vicinity of base station. The network maintenance protocols are confirming the reliability of proposed network architecture. Network maintenance protocols namely Node Move-In protocol is distinct as the joining process for a new node in the hospital network and on the other hand, the Node Move-Out protocol is defined as the leaving process of an existing node from the network. To smoother the communication process, three communication protocols. These protocols are developed to communicate with medical device to admin center and non-medical to non-medical devices.
4.3
Network Formation
The proposed CogMed network is structured and predefined. CBS, CT-SAC, CT enabled medical/non-medical devices, cognitive terminal enabled medical sensors are the components of the network. Figure 4.1 provides the network model of the proposed architecture. As shown in Figure 4.1, the CBS backhaul is the root of the hospital network. Positions of CBSs are fixed and create a wireless local loop. The CBSs are also wirelessly connected for redundancy purposes and form a triangular-shape mesh topology based CBS backhaul to avoid Single Point of Failure (SPF). A cluster based scheme for CBS is considered to ensure smooth data routing. Cognitive terminal enabled medical devices are wirelessly connected to the adjacent CBSs in a single hop manner, where most of these medical devices are static. On the other hand, the non-medical devices are connected to the adjacent CBSs backhaul either in a single hop or in a multi hop manner. Most of these non-medical devices are mobile nodes. CBS backhaul is wirelessly connected to admin center.
64
Figure 4.1 Generic Architecture of proposed hospital System The non-medical mobile nodes form clusters, where CH can be selected based on a cluster head selector value. The CH determination value is calculated based on parameters, such as mobility factor, device priority and channel capability of non-medical devices. It is assumed that all devices regardless medical and nonmedical are operated within the range of CBS backhaul network. CT-SACs are wirelessly connected to CBSs. A CT-SAC acts as an access point or root node in a CRSN. CRSN is connected to CT-SAC and forms a star topology.
4.4
Proposed Architecture
In the proposed architecture, it is assumed that all wireless medical or nonmedical devices and sensors are DSA capable nodes. As shown in Figure 4.2, an
65 architectural framework for proposed cognitive radio based hospital network is illustrated.
A three layer hierarchical model is proposed based on GSM architecture. On the top layer, a partial mesh topology based CBS backhaul system exists. In the middle layer, all cognitive terminals enabled medical, non-medical devices and CTSAC are considered. All these devices are wirelessly connected to the first layer (Backhaul) in a star topology manner. In the bottom layer, CRSN is considered where all sensors are connected to CT-SAC in a star topology manner. Proposed CogMed ensures an ideal heterogonous architecture and emergency medical friendly network for hospital. The architectures of CBS, medical, non-medial and CRSN are proposed in this section. The architecture for CBS is discussed in Subsection 4.4.1. Subsection 4.4.2 narrates the architecture for CT-SAC. The architectures for nonmedical and medical devices are discussed in Subsection 4.4.3 and Subsection 4.4.4 accordingly.
Figure 4.2 Hierarchical architecture of entire hospital system
66 4.4.1
Network Architecture of Cognitive Base Station (CBS) Backhaul System
CBS acts as an access point for all cognitive enabled medical/non-medical devices and CT-SAC that are in its vicinity. CBS is also considered as spectrum fusion center for all cognitive devices including CT-SAC. In Figure 4.3, base stations are equipped with DSA enabled MIMO TR. The network redundancy is assured through intra backhaul wireless connectivity. It is assumed that each CBS is deployed such that they are wirelessly connected (Figure 4.4) with each other and form a triangular shape. The CBS creates a partially connected mesh network to avoid SPF.
Figure 4.3 CBS backhaul network system
Figure 4.4 Triangular shaped partial mesh network based CBS backhaul
Figure 4.5 Assigned CBDV on CBS backhaul
Figure 4.6 Cluster formation on CBS backhaul
67 Let the CBS backbone network (CBSn) consists of |CBSn| nodes. Communications is considered in bidirectional manner between two nodes if they are within their vicinity. In the first step each active CBSi (where CBSi ∈ CBSn) senses free spectrum and try to listen from other CBS using the common control channel (CCC). The other CBS does the same procedure to find its neighbor. An undirected graph G (V, E) is assumed to represent connection instances of network CBSn, where each vertex in V presents nodes of CBSn and E presents network connection between nodes.
The triangular shape of CBS is considered to reduce the probability of SPF. To form triangular shape, it is assumed that a node receives a message from another node. Afterwards, the node checks that any neighboring nodes (apart from sender node) receive the same message from the same source. If the above condition is fulfilled, the message sender and receiver nodes establish a logical connection between themselves. Such as, connections are considered between CBSi , CBSj and CBSk (where CBSi , CBSj and CBSk ∈ CBSn). Suppose Ḡ(V`, E`) is sub graph of G(V,E) where V(Ḡ) ⊆ V(G) and E(Ḡ) ⊆ E(G). The sub graph Ḡ contains vertex V` and edge E` = {(i,j), (j,k), (k,i)}. It is considered that Ḡ (V`, E`) creates a triangular formation. To reduce the data congestion, CBS forms cluster after doing neighbor discovery.
The triangular format based mesh network is considered for CBS backhaul. After discovering all CBS in hospital area, a Cognitive Base Station Value (CBSV) is calculated from three parameters e.g. number of sensed channels within its range (denoted as Ch), number of physical connectivity (N) with base station node (CBS) and hospital location priority factor (LP). As described in Subsection 3.5.3, the priority area (location) is also categorized high priority, middle priority and low priority. To select the cluster head (CH) and logical network formation, we have formulated an equation (Equation 4.1). In this equation, we have emphasized on location priority and number of connection (cardinality) because the entire system scheme is based on medical priority. It is considered that the number of channels is almost the same in every CBS. Thus, channel number is not vital to formulate this equation. Logarithm of channel number is used to downsize the value of Equation
68 4.1and also reduces the possibility of identical value. The high CBDV enabled CBS could be part of inter cluster communication highway and the CBSV also depends on number of connectivity within the neighbor which ensures low communication cost and no single point of failure (SPF) exists that increases the redundancy of network. For any cognitive base-station CBSi, where channel (Chi), location priority (LPi) and number of physical connectivity (Ni) and the CBSV is calculated for node i using the following equation. The Equation (4.1) formulation is based on cluster head determination factor (CHDF) finding technique [72-73].
CBSVi = LPi * Ni * log Chi
(Equation 4.1)
It is considered that CBSs are physically connected in triangular arrangement before forming a cluster. Each node calculates the CBSV value using Equation 4.1 as shown in Figure 4.5. The highest CBSV enabled CBS to be elected as cluster head (CH) among its neighborhood and other neighboring CBS are nominated as pure cluster member (CM). Within two cluster heads (CH) there might be one or two border node that is denoted as gateway node (GW). Cluster heads (CHs) are connected with each other via GW. According to Figure 4.6, communication backbone CH-GW-CH and CH-GW-GW-CH is considered. CH assigns a CM node to GW, if that particular node is physically connected to GW of neighboring cluster. GW either has direct communication link with the neighboring CH or uses the neighboring GW to communicate the neighbor CH.
4.4.2
Network Architecture of CT-SAC
Figure 4.7 illustrates the network architecture of CRSN and the connectivity of CBS and CT-SAC. For hospital service purposes we divide the CT-SAC into two types. The medical CT-SAC that only communicates to medical sensor (such as EEG, ECG) and on the other hand, the non-medical CT-SAC communicates to nonmedical sensors (such as, rehabilitation purposes sensor, fire alarm sensors).
69
Figure 4.7 CT-SAC network architecture in star topology with redundancy
The CT-SAC wirelessly communicates to CBS backbone system in a single hop manner. Redundant CT-SAC is also considered when the primary CT-SAC stops working and provides support to CRSN within its vicinity. Moreover, redundant CTSAC also shares the traffic load of CRSN if the primary CT-SAC is overloaded. A wireless physical link exists between two CT-SACs. Thus the CT-SAC and CBS would form a triangular shape. That approach reduces the possibility of SPF. The CRSN are connected to a CT-SAC in star topology manner. CT-SAC ensures the medical telemetric data reliability and maintains medical QoS for CRSN. Both CTSAC and CBS keeps database of spectrum occupancy and generates spectrum map for low power CRSN. CRSN senses free space through local spectrum sensing technique and delivers spectrum report to adjacent CT-SAC.
70 4.4.3
Flat network Based Architecture for Non-medical Device
Clustering concept reduces the communication cost and wider the opportunity of inner cluster centric spectrum distribution for cognitive radio network. This approach is introduced to manage non-medical devices network in the proposed system. As shown in Figure 4.8, we illustrate the clustering scenario for non-medical CR devices. The cluster system consists of CH and CM. It is assumed that the nonmedical node forms a cluster and CH is selected by a Cluster Head Selector Value (CHSV) of each node. All non-medical nodes exist within the vicinity of CBS. The CHSV is calculated from device mobility factor, device priority and number of accessible channel. The non-medical devices are mobile so the formation of clustering is very dynamic. The selection of CH changes in the manner of time and when node moves. Therefore, to stabilize cluster and reduce the re-clustering attempt, it is very important to put emphasis on device mobility rate and priority of device when CHSV is calculated. The lowest mobility and high priority enabled devices have higher possibility to select CH. Number of channel is not vital to calculate the CHSV. CBS is considered as GW between clusters. If the CH moves from the cluster then second highest CHSV node became CH. To select the CH and logical network formation, an equation (Equation 4.2) is formulated to determine the CHSV of non-medical cognitive radio (NMCR). For any NMCRi, CHSVi computes based on accessible channel (Chi), Mobility rate (Mi) and priority of device (DPi). Therefore, CHSV value for a node i is calculated by the following equation.
CHSVi =
* log Chi
(Equation 4.2)
The formulation of above Equation 4.2 follows Nafees et. al. proposed CHDF value determination technique [72-73]. It is considered that adjacent CBSi acted as gateway (GW) between clusters. The CH fuses the spectrum decisions for its cluster member. However, the CBS keeps track of each sensing decision made by CH. CH sends spectrum sensing report including spectrum hole (SH) and presence the PU to CBS. According to Figure 4.8, communication backbone can be CH-GWCH is considered. Within a cluster, 1-hop communication exists between CH and CM.
71
Figure 4.8 Cluster based non-medical device architecture
4.4.4
Start topology based Architecture for Medical Device
The cognitive enabled medical devices are wirelessly connected to cognitive base station(s) in a single hop manner. In this architecture, CBS acts as a hub or access point for cognitive terminal enabled medical device. According to Figure 4.9, star topology based architecture is introduced for the cognitive terminal enabled medical devices.
72
Medical Device
Figure 4.9 Star topology based CT-medical network architecture
4.5
Maintenance Algorithm for the Network
To maintain the logical topology of the proposed medical and non-medical devices architecture (Subsection 4.4.3 and Subsection 4.4.4), two cluster maintenance protocols are developed, namely Node Move-In and Node Move-Out. This maintenance scheme is not considered for CRSN and CT-SAC since CT-SAC are connected to CBS in star topology manner, even the connectivity of CRSN and CT-SAC also follow the same star topology. In Move-In algorithm, a new node joining process is governed by Node Move-In protocol. On the other hand, when any device moves its position, this situation is termed as node Move-Out. The proposed nodes Move-In and Move-Out protocol are presented in Sub-section 4.5.1 and 4.5.2 respectively, where maintenance protocols are used when an existing node goes out from the network.
73 4.5.1
Node Move-In Algorithm
In the proposed cognitive radio based hospital network, node Move-In policy is defined as the joining process for a new node in the medical network. In node Move-In policy, a joining node new joins the network and gets a status. Table 4.1 represents the node Move-In process. Medical and non-medical CR devices are the candidates for node Move-In procedure in the proposed network.
Firstly, new node checks whether the CBS is within its vicinity or not. If not, no joining is possible. On the other hand, the node joining is allowed if the node within the range of CBS. If the joining node is a medical device new.MDj, the new node sends spectrum information and joining request to adjacent CBS. If admin center approves joining request, new.MDj attempts to join the network. Cognitive radio enabled medical devices are not allowed for multi hop communication.
When a non-medical device new.NMDj wants to join the hospital network, the joining node confirms any CBS exists within its vicinity. Next, new.NMDj checks from its neighbor list Nj, whether the neighbors of joining node new.NMDj is a medical (MD) or non-medical (NMD) device. If it is within vicinity of MD, then new.NMDj from a new non-medical cluster and start the joining process with hospital network via CBS. However, if it is NMD then the new non-medical node (new.NMDj) calculates its own CHSV value CHSVj. Afterwards, new.NMDj broadcasts CHSVj to share the CHSV with its neighbor‘s (new.NMDN).
Next, new.NMDj needs to check for the existence of cluster head in the neighborhood, so that new.NMDj can join the cluster as cluster member. Thus, new.NMDj checks for the presence of cluster head (CH) in the neighbor list of new.NMDN. If CH is not present within its vicinity, the new.NMDj selects as CH and attempt to join hospital network via CBS. Once new.NMDj finds CH in new.NMDN, again it needs to check the number of cluster heads in new.NMDN. If more than one cluster heads exist in new.NMDN, new.NMDj compares its own CHSV value CHSVj with the selected cluster head‘s CHSV value. Cluster head with the highest CHSV value is selected. If selected cluster head possesses higher CHSV value than CHSVj,
74 new.NMDj attempts to join the cluster as cluster member (CM). Moreover, if the highest CHSV value is equal for two or more neighboring cluster-heads, cluster-head with least mobility (MN) of new.NMDN is selected. On the contrast, if CHSVj > CHSVN, then new.NMDj forms a new cluster and nominated as cluster head (CH) and attempts to join the hospital network.
Table 4.1: Node Move-In algorithm % new = New node % new.NMDj = New Non-Medical Cognitive Radio Device % new.MDj = New Medical Cognitive Radio Device % Nj = Neighboring list of new.NMDj % MD = Medical Device % NMD = Non-Medical Device % new.NMDN = Neighbors of joining node new.NMDj % CBS = Cognitive Radio Base station % ADMIN= Administration Center % CM= Cluster Member % CH = Cluster Head % CHSVj = Cluster head selector value of new.NMDj % CHSVN = Cluster head selector value of new.NMDj % MN = Least mobility enabled neighboring CH of new.NMDj % EMI = EMI threshold level % Device location = DL % Device priority = DP
75
1. Begin 2. if (new within CBS range) then 3. if (new.MDj ) then // Medical device 4. new senses spectrum for channel Access List 5. new sends joining request including EMI, DL to ADMIN 6. ADMIN verifies DL value with RFID database 7. ADMIN determines DP and accepts the join request 8. else //Non-medical device 9. new.NMDj check from NJ that new.NMDN is MD or NMD 10. if (NMD ∈ new.NMDN ) 11. new.NMDj calculates CHSVj value 12.
13. 14. 15. 16.
17. 18. 19.
20. 21.
new.NMDj broadcasts CHSVj value to Neighbor new.NMDN
new.NMDj checks the presence of CHs from NJ if (CH ∈ new.NMDj) if (CHSVj > CHSVN )
new.NMDj form a new cluster and join l network as CH
Step 4 to 7 are performed elseif (CHSVj < CHSVN )
if(CHSVN same for more than one new.NMDN )
MN enabled new.NMDN selected as CH
new.NMDN selected as CM of that cluster and join
22. Step 4 to 7 are performed 23. else 24. Step 20 to 22 are performed 25. End if 26. else 27. Step 16 to 17 are performed 28. End if 29. else 30. Step 16 to 17 are performed 31. End if 32. else 33. Step 16 to 17 are performed 34. End if 35. End if 36. else 37. No node joining 38. End if 39. End
The primary task of the new node senses the common control channel of CBS or CR cluster network (for non-medical device). Afterwards, the joining node sends it‘s joining status, node ID, location of the device (DL), transmission data type and
76 EMI level to the administration center. Next, the administration center confirms the DL by verifying the information from the RFID database form admin center. It is also assigned a priority level (DP) considering the transmission type, location of the device DL and finally accepts the joining request. Figure 4.10 illustrates the flowchart of the proposed Node Move-In protocol.
Figure 4.10 Flowchart for node Move-In algorithm
77 4.5.2
Node Move-Out Algorithm
Node Move-Out algorithm is presented in Table 4.2, deals with the leaving process of an existing node from the proposed network. The node movement process starts with a broadcasted leaving message to CBS and CR enabled non-medical clusters by leaving node (l.node). CR enabled medical (l.MDJ) and non-medical devices (l.NMDJ) are considered for node Move-Out process. In the case of l.MDJ, if l.MDJ shifts to another place of the hospital and within the vicinity of any adjacent cognitive radio base station (CBSJ) and then l.MDJ starts the Move-Out process.
For the l.NMDJ, CH and CM movement is considered for node move. If the moving node (l.NMDJ) is CM, it informs the correspondent CH about the leaving status and just leave. Otherwise, the l.NMDJ considers as CH. In the next phase, two cases are considered, CH has no cluster member (l.CM.NMDJ) and l.CM.NMDJ exists. Let‘s consider, k represents the number of c l.CM.NMDJ of leaving cluster head (CH). If there is no l.CM.NMDJ (k =0) exists in next hop, CH just simply leaves.
If k=1, l.CM.NMDJ checks from its neighbor list, whether any other cluster head (CH) is available or not. If neighbor CH is not exists then l.CM.NMDJ forms a new cluster and CH of l .CM.NMDJ starts process for leaving. Else the l.CM.NMDJ recalculates its own CHSV (CHSVJ), and compares with neighbor cluster head CHSV (CHSVN) value. If CHSVN > CHSVJ then, l.CM.NMDJ joins as a CM of neighbor cluster and l.NMDJ leaves. Otherwise, l.CM.NMDJ
forms a new cluster
and elected as cluster head of that cluster.
If more than one (k>1) l.CM.NMDJ exists, two cases needs to be consider. In first case, If all of the l.CM.NMDJ within the range of each other in the cluster then highest CHSVJ enabled l.CM.NMDJ is selected as cluster head and rest l.CM.NMDJ are pure cluster member and CHJ leaves. Otherwise, all connected l.CM.NMDJ forms new clusters and CH is selected by highest CHSVJ. Rest of the disjoint l.CM.NMDJ compares their CHSVJ value to neighbor cluster head. If the CHSV value of l.CM.NMDJ is less than neighbor CH CHSV value, l.CM.NMDJ simply joins as
78 cluster member of that cluster. Or else l.CM.NMDJ forms a new cluster and l.node leaves.
Table 4.2: Node Move-Out algorithm % l.node = leaving node % l.MDJ = CR enabled leaving medical devices % l.NMDJ = CR enabled leaving medical non-medical devices % CH = Cluster head % CM = Cluster Member % k = Positive integer {k = 0, 1, 2, 3,.} % l.CM.NMDJ = Cluster member of CHJ % CHSVJ = CHSV of l.CM.NMDJ % CHSVN = CHSV of neighbor CHs of l.CM.NMDJ 1. Begin 2. l.node broadcasts leaving message 3. if ( l.node = l.MDJ ) then 4. l.MDJ leaves 5. else 6. if (l.NMDJ = CM) then 7. l.NMDJ informs it‘s CH and leaves. 8. else 9. l.NMDJ is a CH 10. k = | l.CM.NMDJ | 11. if (k=0) then 12. l.NMDJ leaves. 13. elseif (k=1) then 14. l.CM.NMDJ recalculates its CHSVJ value 15. if (neighbor CH ∈ l.CM.NMDN ) then 16. if (CHSVN > CHSVJ) then 17. l.CM.NMDJ joins as a CM and l.NMDJ leaves 18. else 19. l.NMDJ leaves and l.CM.NMDJ to be CH of a new cluster 20. end if 21. else 22. Step 19 performed 23. end if 24. else //( K >1 ) 25. 26.
27. 28. 29.
30. 31. 32. 33. 34. 35.
if (All l.CM.NMDJ within the range of each other ) The next highest CHSVJ becomes CH and the rest l.CM.NMDJ are CM.
CHJ leaves. else
All connected l.CM.NMDJ forms a new cluster and CH is selected by highest CHSVJ
For rest of l.CM.NMDJ : Step 15 to 19 performed end if end if end if end if End
Figure 4.11 has shown the data flow diagram on node Move –Out process. Figure 4.11 follows the node Move-Out algorithm that is described in Table 4.2.
79
Figure 4.11 Flowchart for node Move-Out algorithm
4.6
Communication Protocols
This section discusses the proposed communication protocols that are designed for CR based hospital network. In proposed hospital environment,
80 administration center is considered as centralized controlling and monitoring entity of an entire hospital. This administration center also acted as a data center. Normally the admin center requests any CR enabled medical devices for vital medical information. The admin center communicates to the all the medical and non-medical devices in hospital. In other way, admin center has capability to communicate any hospital devices regardless medical or non-medical. It is assumed that, communication between medical and non-medical devices is not allowed in the proposed cognitive radio based hospital. So if any non-medical device requires medical information, admin center creates an instance for non-medical devices. In the hospital, it is considered that a non-medical device only communicates to other non-medical devices. The admin center also requires the hospital information from different medical and non-medical sensors. Thus, it is very important to develop device to device (D2D) communication protocols for CogMed system. The development of communication protocols for CR enabled healthcare system, EMI immunity on medical device and priority of device needs to consider. In this section, the communication protocols for
CR enabled medical device to administration
center (Sub Section 4.6.1), non-medical to non-medical device communication (Sub Section 4.6.2) and communication between the cognitive added wireless medical sensors to admin center (Sub Section 4.6.3) are discussed.
4.6.1
Protocol for Medical Device to Admin Center
The admin center and medical devices communicate by following steps.
In the first step, the admin center sends a request message to medical device to fetch information from a patient. If the device is busy, the request waits in the adjacent CBS queue. When a medical device receives the request from admin center, the device sends its spectrum sensing information, current position, transmission mode, priority level, and distance from other devices to admin center via adjacent CBS. CBS fuses the spectrum decision considering the priority of device, sensed spectrum information and spectrum occupancy database from admin center. Admin center calculates the maximum transmittable power based on the EMI immunity
81 level for the device and distance from CBS. Afterwards, the admin center conveys the spectrum decision, allocated bandwidth and maximum transmittable power to medical device via CBS along with communication confirmation. After receiving the confirmation, the medical device starts communicating and sending data to admin center. If any PU appears during data communication, the current spectrum drops and another spectrum will be allocated by CBS.
Table 4.3 presents the communication protocol for medical device to admin center. If admin center wants to communicate with medical device (devicei) the following steps are considered. In the first step, admin center sends the request message (r.msg) along with device ID (IDi) and approximate position of devicei (Dn) to CBS backhaul system. The CBS Backhaul detects the devicei. If the devicei is busy, the r.msg waits in adjacent CBS of the devicei. Otherwise, devicei sends the spectrum sensing information (SHi), initial priority level (DPi), current location information (DLi), distance from other sensitive medical device in its vicinity (Di), EMI threshold level of node i (EMIi ) to its adjacent CBS. It is assumed that devicei continuously performs spectrum sensing and updates the Shi to the CBS.
CBS transmits
DPi , DLi , Di and EMIi to admin center to calculate the
maximum transmittable power (Ptxmi) considering the EMI issues of the devicei. Admin center also updates priority level (DPri) of devicei. After receiving the Ptxmi and DPri from admin center, the CBS formulates spectrum decision (SDi) based on SHi, spectrum occupancy database (SDB) and DPri. Therefore, CBS sends the SDi, Ptxmi and allocated bandwidth for devicei (BWi) to devicei along with communication confirmation. Afterwards, communication between devicei center started.
and admininstration
82 Table 4.3: Communication protocol 1: Medical device to admin center % devicei = medical device % r.msg = request message
% IDi = device ID % Dn = approximate position of devicei % SHi = spectrum sensing information % DPi = initial priority level % DLi = current location information % Di = distance from other device in its vicinity % EMIi = EMI threshold level of node i % Ptxmi = maximum transmittable power % DPri = updates priority level % SDi = Spectrum decision % SDB = spectrum occupancy database % BWi = allocated bandwidth for devicei % PU = Primary User 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.
Begin Admin center sends r.msg, IDi, Dn to CBS backhaul CBS locate devicei by broadcasting IDi if (devicei busy) then r.msg waits in adjacent CBS queue else while (devicei senses Spectrum ) devicei sends SHi, DLi, Di , EMIi to adjacent CBS CBS Transmits DLi, Di , EMIi to admin center Admin calculates Ptxmi and BWi Admin updates DPri and sends DPri to adjacent CBS of devicei CBS fuses SDi based on SHi, DPri and SDBi Admin sends Ptxmi and BWi to adjacent CBS of devicei Adjacent CBS of devicei sends SDi, Ptxmi and BWi to devicei
Communication starts between admin and the devicei if (PU interference) then
devicei hands-off the spectrum and CBS allocates a new SDi
else Transmission continue end if end while end if End
Figure 4.12 presents the pictorial view of medical device to admin center communication protocols. The Figure 4.12 is based on Table 4.3.
83
Figure 4.12 Communication protocol for medical devices to admin center
4.6.2
Protocol for Non-medical to Non-medical CR Device
In a hospital area, the communication between doctor and nurse, video conferencing for medical consultation and information of different hospital inventory plays a vital role. The above mentioned communication processes and devices are treated as non-medical devices. Admin center acts as system register that provides communication permission, determines transmission parameter and delivers location information, EMI threshold level of different device. To establish the connectivity between non-medical CR devices, following communication protocol is proposed.
84 Table 4.4 describes the non-medical to non-medical devices communication protocol for proposed cognitive based hospital system. Let‘s consider, a non-medical device NMDi wants to communicate with device NMDj. NMDi sends its own id (IDi) , spectrum information (SHi), initial priority (Pi), current location information (DLi), distance from other sensitive medical device in vicinity (Di ), EMI threshold level of node i (EMIi), destination non-medical device id (IDj) and request message (REQi j) to
adjacent CBS of device i (CBSi) though predefined global common control
channel. CBSi sends IDj, Pi , DLi , Di and EMIi to the admin center. Admin center calculates the maximum transmittable power NMDi (Ptxmi) considering DLi , Di and EMIi. Admin center updates priority of NMDi (DPri) and sends to CBSi. Admin center also broadcasts the IDj , REQi j ,and possible location of NMDj (Dn) to CBS backhaul to locate the device j.
CBSi determines the spectrum decision (SDi) for NMDi considering the SHi, DPri and spectrum occupancy database (SDB). Therefore, admin center locates the NMDj and provides instruction to the adjunct CBS of device j (CBSj) to convey the request REQi j to CRj. If NMDj is busy, the REQi j waits in CBSj queue. Otherwise, the CBSj delivers the REQi j to NMDj. Device NMDj sends spectrum information (SHj), initial priority (Pj), current location information (DLj), distance from other sensitive medical device in its vicinity (Dj ), EMI threshold level of node j (EMIj )to CBSj . It is considered that NMDi and NMDj continuously performing spectrum sensing and sends the report to adjacent CBS.
CBSj forwards Pj , DLj , Dj and EMIj to admin center. Admin center calculates the maximum transmittable power NMDj (Ptxmj) considering DLj , Dj and EMIj. Admin center updates priority of NMDj (DPrj) and sends to CBSj. CBSj determines spectrum decision for NMDj (SDj) considering the SHj , DPrj and SDB. NMDj issues a received acknowledgement (ACKji) on reply of REQi j. CRj sends ACKj to NMDi via CBS backhaul using common control channel.
Afterwards, admin center and adjacent CBSs (CBSi and CBSi) delivers the permitted maximum power levels (Ptxmi ,Ptxmj) and spectrum decisions(SDi , SDj) to NMDi and NMDj respectively. Moreover, required bandwidth (BWi, BWj ) are also
85 allocated for NMDi and NMDj accordingly. Admin center also issues communication confirmation message for both devices.
Table 4.4: Communication protocol 2: Non-Medical to non-medical % NMDi , NMDj = Non-medical device i and j accordingly % REQi j = request message (from device i to j) % IDi , IDj = device ID of i and j accordingly % Dn = possible location of NMDj(device j) % Shi , Shj = spectrum sensing information of device i and j accordingly % DPi , DPj = initial priority level of device i and j accordingly % DLi , DLj = current location information of device i and j accordingly % Di , Dj = distance from other device in its (device i and j accordingly) vicinity % EMIi , EMIj = EMI threshold level of device i and j accordingly % Ptxmi , Ptxmj = maximum transmittable power for device i and j accordingly % DPri , DPri = updated priority level of device i and j accordingly % SDi , SDj = Spectrum decision of device i and j accordingly % SDB = spectrum occupancy database % BWi, BWj = Bandwidth allocation for i and j accordingly % PU = Primary User % ACKji = received acknowledgement ((from device j to i)) 1. Begin 2. while (NMDi and NMDj sense spectrum) 3. NMDi send IDj , REQi j, Shi , DPi , DLi , Di and EMIi to CBSi 4. CBSi sends IDj , REQi j, DPi , DLi , Di and EMIi to admin center 5. Admin center updates DPri and sends to CBSi 6. Admin center calculates Ptxmi considering DLi , Di and EMIi 7. CBSi determines SDi for NMDi using Shi , SDB and DPri 8. Admin center broadcast IDj with Dn and REQi j to locate NMDj 9. if (NMDj is busy) then 10. REQi j is wait in CBSj queue 11. else 12. REQi j sends to NMDj 13. NMDj send Shj , DPj , DLj , Dj and EMIj to CBSj 14. CBSj sends DPi , DLi , Di and EMIi to admin center 15. Admin center updates DPrj and sends to CBSj 16. Admin center calculates Ptxmj considering DLj , Dj and EMIj 17. CBSj determines SDj for NMDj using Shj , SDB and DPri 18. NMDj sends ACKji to NMDj 19. Admin sends the Ptxmi , Ptxmj to NMDi and NMDj respectively 20. CBSi delivers SDi and BWi to NMDi 21. CBSj delivers SDj and BWj to NMDj 22. Admin center confirms the communication 23. Communication link established between NMDi and NMDj 24. end if 25. end while 26. End
Therefore, the communication link between NMDi and NMDj is established. If PU appears, the affected spectrum hands-off and transmission is paused. The
86 adjacent CBS assigns a new spectrum and transmission starts again. Figure 4.13 represents the pictorial explanation of communication protocols for non-medical to non-medical communication.
Figure 4.13 Communication protocol for non-medical to non-medical devices
87 4.6.3
Protocols for CR Aided Sensor, CT-SAC and Admin Center
In proposed cognitive radio based hospital system, CRSN is considered for medical and non-medical purposes. CRSN sensors are communicated to admin center via CT-SAC. CR medical sensor collects different telemetric, medical data and patient vital information and provisions to admin center. Sensor sends the connection request along with local sensing and device priority information to its adjacent CT-SAC .It is assumed that CT-SAC determines the spectrum decision and transmittable power for sensors. In that mean time, adjacent CT-SAC sends his sensing report, request message and other information to admin center and CBS to calculating the transmission parameter of CT-SAC. Therefore, admin center delivers the acceptable transmission power for CT-SAC and adjacent CBS provides the spectrum decisions to CT-SAC. CT-SAC allows sensor to communicate with admin center. The communications between CRSN and admin center is occurred in following steps:
Suppose CR sensor (CRSNi) wants to communicate with admin center and sends the medical information to store in EMR and other medical databases. CRSNi sends spectrum information (SHi), priority of CRSNi (Pi) to its adjacent CT-SAC (CT-SACj). CT-SACj determines the spectrum decision of CRSNi (SDi), allowable transmission parameter of CRSNi (TXi) considering SHi and Pi. CT-SACj generates a request message (REQji) for admin center that contains connection request and functional data of CRSNi. CT-SACj delivers the REQji along with its own spectrum information (SHj), initial priority level (DPj), current location information (DLj), distance from other sensitive medical device in its vicinity (Dj ) and EMI threshold level of CT-SACj (EMIj ) to the adjacent CBS. It is assumed that CRSNi and CT-SACj are both preformed spectrum sensing and deliver the information to their access point continuously. Adjacent CBS transmits Pj, DLj , Dj and EMIj to admin center and admin center calculates the maximum transmittable power (Ptxmj ) considering DLi , Dj and EMIj. Admin center updates the recent device priority (DPrj) based on DPj .
88 Adjacent CBS determines the spectrum decision (SDj) and allowable bandwidth (BWj) of CT-SACj considering the SHj, DPrj and spectrum occupancy database (SDB) . Admin center issues a connection acknowledgement (ACKji) for CRSNi. Afterwards, admin center and CBS delivers the ACKji ,Ptxmj ,SDj and BWj to CT-SACj . The CT-SACj is adjusted its transmission parameter according to admin center and CBS. CT-SACj sends the data transmission permission along with SDi and TXi to CRSNi. Therefore, a transmission link has been established between CRSNi and admin center via CT-SACj and CBS. Table 4.5 presents the communication protocol for CRSN to admin center.
Table 4.5: Communication protocol 3: CT-SAC to admin center % CRSNi = Cognitive radio aided sensor % SHi = spectrum sensing information of CRSNi % Di = priority of CRSNi % SDi = spectrum decision of CRSNi % DLi = current location information % TXi = allowable transmission parameter of CRSNi % CT-SACj = adjacent CT-SAC of CRSNi % REQji = CT-SACj generated request message % SHj = spectrum information CT-SACj % DPj = initial priority level of CT-SACj % DLj = current location information of CT-SACj % Dj = distance from other device in its vicinity % EMIj = EMI threshold level of CT-SACj % Ptxmi = maximum transmittable power for CT-SACj % DPrj = updates priority level of CT-SACj % SDi = Spectrum decision for CT-SACj % SDB = spectrum occupancy database % BWi = allocated bandwidth for CT-SACj % ACKji = connection acknowledgement % PU = Primary User 1. Begin 2. while(CRSNi sensing spectrum) 3. CRSNi sends IDi , SHi , Di to CT-SACj. 4. CT-SACj calculates SDi and TXi for CRSNi 5. CT-SACj generates REQji for admin center 6. while(CT-SACj sensing spectrum) 7. CT-SACj sends REQji , SHj, DPj , DLj , Dj and EMIj to CBS 8. CBS sends REQjij, DPj , DLj , Dj and EMIj to admin center 9. Admin center calculates Ptxmj for CT-SACj 10. Admin center updates DPrj using DPj 11. CBS determines SDj and BWj considering SDB , SHj DPrj 12. Admin center issues ACKji 13. Admin sends ACKji , Ptxmi to CBS. 14. CBS send ACKji , Ptxmi SDj and BWj to CT-SACj 15. CT-SACj sends permission ,SDi and TXi to CRSNi 16. Transmission begins between Admin center and CRSNi 17. end while 18. end while 19. End
89 The Figure 4.14 describes the communication protocols for CRSN and admin center. Figure 4.14 is based on Table 4.5.
S T E P S
EMI Inventory
Medical Monitoring Cell Medical Device Location from RFID
Cognitive Sensor Network (CRSNi )
1
Cognitive terminal- Sink Access Control (CT-SACj )
Cognitive Base Station (CBS)
3
CT-SACj generates REQji for admin center
4
CT-SACj sends REQji , SHj, DPj , DLj , Dj and EMIj to CBS
8
CBS sends REQjij, DPj , DLj , Dj and EMIj to admin center .
Admin center calculates Ptxmj for CT-SACj
Continuous SSF
7
Continuous SSF
CT-SACj calculates SDi and TXi for CRSNi
6
Admin center updates DPrj using DPj CBS determines SDj and BWj considering SDB , SHj DPrj
9
Admin center issues ACKji CBS send ACKji , Ptxmi SDj and BWj to CT-SACj
10
11
Doc
CRSNi sends IDi , SHi , Di to CT-SACj
2
5
Hosp Mon ital Adm in itorin g an istrativ Data d Differ e, ent base
CT-SACj sends permission SDi and TXi to CRSNi Transmission Started between Admin center and CRSNi
Figure 4.14 Communication protocols for CRSN, CT-SAC and admin center
4.7
Concluding Remarks
This Chapter opens with a hybrid and heterogonous topology based network model and architecture for cognitive radio based hospital. Afterward a brief
90 description of cluster based CBS and non-medical network model along with network maintenance protocols are stated. Moreover, the architectures medical devices and CT-SAC are also discussed. To maintain the device oriented architecture regardless medical and non-medical devices, two maintenance protocols namely node moving and node moving algorithm are proposed. This chapter ends with three D2D communication protocols for proposed system.
91
CHAPTER 5
SIMULATION RESULTS AND ANALYSIS
5.1
Introduction
In this section, a simulation environment is developed and simulation result is critically analyzed, discussed and compared with other results on queuing system. In the proposed system, all hospital devices (regardless of medical and non-medical) are cognitive radio enabled to avoid spectrum scarcity and EMI problems of recent trends in wireless healthcare. The devices are categorized and defined as hospital requirements and medical emergency service priority. A hybrid network topology based network model and architecture are also introduced in the proposed cognitive radio based hospital network. The network topology combinations of partial mesh cluster and star topology which ensures not only network robustness but also fully managed network framework for ideal wireless hospital environment.
In-order to confirm the integrity of simulator and simulation used in this study, verification and validation (V&V) of this approach is explained in Section 5.2. Section 5.3 discusses the simulation environment to study the performance of the proposed priority management and network architecture, as well as to compare the priority management with the existing ones by using NS2. Priority management evolution of cognitive radio based hospital is considered to evaluate the performance of the wireless healthcare system (Section 5.4). Simulation execution time is considered as the performance evaluation index for the maintenance protocols. This is discussed in Section 5.5 and consequently the comparison between different queuing methods with the proposed cognitive radio enabled hospital and a
92 comparison study of latency time for proposed cognitive radio enabled medical device with wireless medical device latency benchmark is explained in Section 5.6.
5.2
Simulation Verification and Validation (V&V)
The Verification and validation are two important steps in any system design. Verification refers to whether a model behaves as intended. Validation denotes to the agreement between a model and the real world. Thus, validation relates the real world with the conceptual model, whereas verification relates the conceptual model with the operational model. In order to develop a realistic model, an ideal simulation platform needs to be chosen very carefully considering the conceptual model requirements. Moreover, the developed simulation for CogMed has to be verified and validated. To certify the proposed system design, priority management and network architecture of CogMed system, NS2 is chosen. In this section, NS2 simulated queuing theory and model is verified and validated.
Verification is the formal process that checks, the implementation of the model is an accurate representation of the conceptual model. In this study, the methodology of priority management (Sub Section 3.2.2) is pretending to verify. Here we compare the NS2 simulated basic m/m/1 queue model and the theory of the m/m/1 queue. The measured parameters are considered mean utilization rate (ρ) and mean response time of system E[R] where, ρ= λ/ μ > 1 [71, 74].
In the simulation environment, bandwidth is set to 1 MB/s and the size of data packet is random value with mean 1KB, and negative exponential distribution (Marcovian process). Figure 5.1 presents a comparison between calculated result from theory and simulated result considering the above parameters.
93
Figure 5.1 Comparison of simulation and calculation from theory
Figure 5.1 shows the utilization rate v/s response of NS2 simulated scenario and theory. The presented graph pattern of simulation and theory is almost same. This study and outcome verifies our proposed simulation platform.
The aim of the validation process is to establish a competitive sketch between system conceptual model and a true picture of the real world. Validation is usually achieved through common sense and logic, by taking advantage of knowledge and insight available, by empirical testing, by paying attention to details, by debugging of the program, by input-output analysis and comparison with real-life data, and by checking predictions. The major guideline for validation is testing of input-output transformations of system.
In this study, we compare the NS2 built-in DropTail queue of our proposed simulator and real word simulator that developed by Koo et.at. [75]. To do so, the input parameters of proposed simulator are needed to tune with the real world simulator. In this validation process, bandwidth set to 10 MB/s, propagation delay tune to 10 ms and 1, 50,000 bytes queue size is considered. Packet size is considered 500 bytes. For DropTail queue no AQM is set. Simulation runtime is set to 70 seconds. Figure 5.2 presents DropTail queue average delay time comparison study for proposed simulator and real world.
94
Figure 5.2 Comparison of proposed Simulator and real world simulator It is observed from the Figure 5.2, the network delay pattern for proposed simulator and real world simulator of is almost same over the simulation period. That scenario validates our proposed NS2 simulator.
5.3
The Simulation Environment
To evaluate proposed CogMed system Design and priority mechanism, a NS2 simulation platform is considered. Network simulation 2 (NS2) is very popular simulation application among researchers [76]. To consider the possible real-time hospital network environment NS2 is preferred [77]. We compare our scheme with other queuing model such as drop tail and RED [77-78]. To validate the proposed scheduling algorithm we developed two cases.
In the first case, all devices are priority label enabled and have a priority value. The proposed queue class is developed with admission control. Table 5.1 describes the simulation environment. In simulation environment of first case, the proposed queue mechanism is injected in each CBS and network redundancy is also allowed. Priority on each medical and non-medical device is also imposed.
95 Table 5.1: Simulation information for proposed hospital system Properties
Number and Range
Med + nonmed device node CBS Node Number of primary user Admin Center Node Routing redundancy and Scheduling mechanism Topology Packet size Bandwidth Allocation
25+25 (Range 30 m) 3 (Range 100m) 5 (Range 30 m) 1 (Range 100 m) CBS end
Priority set Hospital Zone
Partial Mesh-CBS and Admin center, Star for device 1200(MAX) 30 mb CBS-admin , 20 mb CBS-CBS , 2 mb CBSNonmed real time device (NMD_RT, ET-RT-LS), 1mb CBS-Rest of device EH-100, H-80, M-60, L-20 Priority Location 1 , Non- Priority Location 1
In the simulation, twenty-five (25) medical nodes and twenty-five (25) nonmedical nodes with different priority level are considered. This consideration is made on 1:1 ratio basis to satisfy a possible cognitive radio based hospital scenario. Proposed priority classifier mechanism is deployed in each CBS for different incoming routes from medical and non-medical devices. In second case, all nodes are without any priority and a conventional FIFO or DropTail queuing is applied. This scenario is considered to observe the patterns of priority enabled or priority less network environment.
In both cases, we consider CBS architecture based on partial mesh network and star topology based medical and non-medical device nodes. Node position and device labeling in each case are same. We have simulated both cases and simulation time is 550 second and size of each queue for both cases in CBS is remaining same. For the comparative study, the simulation area is set to 10,000 sq. meters (100 X 100 meters) to evaluate the performance of the priority mechanism and network maintenance protocols in a wireless hospital area. Like other simulation environments in CR, communication range for each node is considered. In the network, five(5) PUs are also considered with transmission range of 30 meters, where the PU operates randomly. Five (5) pair real-time non-medical CRs are set as direct communication to each other and rest of the CR devices are set to device to admin center communication followed the proposed communication protocols.
96 Simulation results are compared with standard data rate and latency of medical and non-medical devices in wireless hospital. To evaluate the network maintenance protocols, we considered that eight (8) nodes are mobile. These nodes move to one CBS to another and multi hop communication with other non-medical devices is considered to perform if the leaving non-medical nodes are out of CBS vicinity. Proposed clustering concept of CBS and non-medical devices is also injected in simulation. Two location priorities are considered namely high and low priority for this simulation. Majority medical devices enabled CBS is considered as high priority location factor. On the other hand, opposite scenario is considered as low priority
5.4
Priority Management Evolution of Cognitive Radio Based Hospital
From simulation, we are achieving some results based on packet drop ratio, average receiving time and average queuing period in priority enabled and priority less test case. In addition, a comparative study also presented on proposed priority queuing mechanism and other established queuing algorithm.
Figure 5.3 Packet drop percentage Figure 5.3 represents the packet drop ratio in priority and priority less test cases. It is observed that packet drop ratio in priority enabled medical and nonmedical devices in lower without priority hospital devices. Packet drop pattern is almost same in priority less situation medical and non-medical device. The priority
97 enabled medical device packet drop ratio is negligible to other packet drop cases. The average packet drop rates for priority enabled medical device is 1.56% and priority enabled non-medical is 20.04 %. On the contrast, the packet drop percentage of without priority medical and non-medical are 25.03% and 25.20%, respectively.
Figure 5.4 Average queuing period (ms) Figure 5.4 shows the average queuing time of priority enabled medical, nonmedical and without priority medical and non-medical devices. It is observed that average queuing times for priority enabled medical devices are lower than other cases. The ratio of average queuing time for without priority medical and nonmedical devices is almost same. One observation is that priority enabled nonmedical devices have high queuing time rather than other cases due to low priority than medical devices. Figure 5.3 represents the drop rate and Figure 5.4 narrates the average queuing period. In general, average queuing time and the drop rate are inversely proportional [28]. Figure 5.3 and Figure 5.4 represent the facts.
Figure 5.5 Average network delay (ms)
98 Figure 5.5 represents the average network delay time of priority enabled and priority less medical, non-medical devices. The average received time for priority aided medical device is short in terms of other devices type. Priority enabled nonmedical devices have high average delay because of the queuing time is quite high and sometime the non-medical devices need to traverse more than one CBS. On the other hand, average time of without priority medical and non-medical devices have same pattern as shown in Figure 5.5. It is observed from Figure 5.4 and Figure 5.5, average queuing time and average delay period is proportional to each other. It is also observed from Figure 5.5 and Figure 5.2 that the DropTail queues delay and without priority medical and non-medical devices having almost same attributes. As the DroptTail queue is deployed in without priority medical and non-medical devices simulation.
5.4
Performance Evaluation for Network Maintenance Scheme
Node move-In and node move-Out are developed as the network maintenance protocols for the proposed cognitive radio driven hospital system. The performance valuations of node move-In and move-Out protocols are presented in Section 5.4.1 and Section 5.4.2, respectively. The joining and leaving execution time is considered to evaluate the performance for priority and non-priority enabled medical and nonmedical devices cases. It is assumed that maximum twenty (20) percent of medical and non-medical might be joining and leaving simultaneously. In developed simulation environment maximum eight (8) nodes can in or out from medical network as total number of nodes is fifty (50) in entire simulated hospital.
5.4.1
Node Move-In
The proposed node Move-In algorithm is launched in hospital simulation environment to study the joining time taken for both priority and no priority situation as shown in Figure 5.6.
99
120
Node Move-In
Period (Ms)
100 80 60
Med Device Priority
40
Non-Med Device No Priority Med Device No Priority Non Med Device Priority Device
20 0 1 2 3 Number of Node
4
5
6
7
8
Figure 5.6 Node Move-In algorithm For no priority case, medical and non-medical devices have taken almost same time because no process is allied for none priority case. On the contrast, for priory case, non-medical devices take longer time because of high time complexity and processes are associated with non-medical devices joining algorithm. However, the priority enabled medical devices take very less time because the time complexity of node joining algorithm for medical device is minor than non-medical devices. It is also observe that the pattern for priority enabled medical and no priority enabled devices is almost indistinguishable.
5.4.2
Node Move-Out
The proposed node Move-Out algorithm is injected in hospital simulation environment to study the leaving time taken for both priority and no priority situation as shown in Figure 5.7.
100
Node Move-Out
8
Period (Ms)
7 6
5 Med Device Priority 4 Non Med Device Priority Device
3
2
Non-Med Device No Priority
1
Med Device No Priority
0 1 2 3 Number of Node
4
5
6
7
8
Figure 5.7 Node Move-Out algorithm It is observed from Figure 5.7, the graph pattern for all type of devices is remaining same as the node leaving process is considered as local process. It is also noted that the leaving time taken for non-medical priority devices is slightly higher than the other category. As priority enabled non-medical devices have anticipated some logics in node Move-out algorithm.
5.5
Comparison between Proposed System with Other Standards
As per discussion in Section 2.4, it is very difficult to compare the simulation results with other literatures and research works. Because the priority based hospital system design is absent in contemporary CR based healthcare system and even the numbers of same type of research works are also very limited in wireless healthcare era. In this section, a comparison is drawn between the proposed hospital medical queuing and scheduling mechanism with other well establish active queuing managements. As well as, some transmission parameters of different hospital device based services are compared with wireless e-Health care standards. Subsection 5.5.1 and Subsection 5.5.2 discuss these comparisons accordingly.
101 5.5.1
Comparison of Queuing Management
To validate the proposed queuing management, it is important that the measure the overall performance of the system. In the basis of transmission model evolution, packet drop rate is the key factor for active queuing management and scheduling mechanism. To get an average drop percentage of proposed medical priority enabled queuing management, the simulation runs ten (10) times. Afterwards, we compare the result with other established queuing system such as DropTail and Random Early Detection (RED) as shown in Figure 5.8. We also simulate same hospital environment using DropTail and RED queuing mechanism. Then the drop percentage of DropTail and RED queue is found 22.92 % and 16.45%, respectively [61].
Figure 5.8 Comparison between proposed queue, RED and DropTail algorithm However, the proposed CogMed queuing packet drop rate is lower compared to two other algorithms as shown in Figure 5.8. The drop rate is low because the proposed system has more emphasized on reducing the high priority packets drop ratio. Moreover, the proposed queuing algorithm specially designed for CogMed Network. The other queuing algorithm is might be ideal for other conventional network. However, our proposed queuing system is sustainable for any wireless medical network.
102 5.5.2
Comparison of Latency Period (QoS Performance)
In the simulation environment, several types of test cases have been developed to validate the network performance and device QoS. The test cases are launched based on proposed communication protocols that are discussed in Section 4.5. In this simulation it is assumed that the spectrum decision and other operating parameter are predetermined. The developed test cases in simulation are non-medical device to device communication and medical device to admin center communication. As QoS performance indicator of proposed CogMed, latency delay of different hospital wireless devices is chosen from simulation. Subsequently, the simulated test performance results compare with latency of wireless medical RF device recommended by U.S. Food and Drug Administration (FDA) and the Association for the Advancement of Medical Instrumentation (AAMI) to petition the U.S. Federal Communications Commission (FCC) [5, 74-77]. The status of different devices also supports different service classes of RFC 4594 [57, 76].The results are illustrated in Figure 5.9.
Figure 5.9 Comparison between proposed device latency with Standards [79-81] In test case scenario, four circumstances are considered. Firstly, the latency delay is calculated from medical device to admin center communication process in real-time and non-real-time mode. Afterwards, the latency is calculated from nonmedical to non-medical communication process in both data transmission mode. The
103 latency delay is the combination of frame serialization period, link media delay, queuing delay, peripheral routing period and other network delay. The simulator NS2 itself calculates the latency using its default modules. To cope up with a realistic wireless hospital approach, we have mapped the different medical and non-medical hospital services with the simulated test cases scenarios based on data transmission mode. Where, life supporting real-time medical devices map with continuous emergency diagnosis, alarm and real-time critical status report from infusion pump. Moreover, life supporting non-real-time medical devices in-line with store and forward based medical transmission mode like EMR, medical imaging and medical critical alert. On the other hand, Non-life supporting real-time non-medical devices relate with hospital oriented voice and video translation and non-real-time devices compare with hospital oriented web service, email, general alert, RFID data, and clinician notifies. From the graph Figure 5.9, it is observed that medical real-time, medical non-real-time and non-medical non-real-time within the threshold level of FDA guided wireless hospital device latency delay [79-82]. However, non-medical real-time devices vigorously exceed prescribe latency boundary because of high end to end delay and longer queuing time. In VoIP era, latency above 200 ms but bellow 250 ms situation rarely degraded the quality of service for voice communication [8283].
5.6
Concluding Remarks
This chapter starts with simulation environment design. Afterwards, evaluations of proposed priority mechanism, queuing management and network maintenance protocols are narrated. The simulation results show that the proposed scheduling mechanism enabled system having low packet drop rate and delays are also less. The chapter ends with validating the queuing management and network QoS with established research and standards.
104
CHAPTER 6
CONCLUSION
6.1
Summary
In order to guarantee the QoS of medical emergency in cognitive radio based efficient hospital, robust hospital system design and hospital network model is important. This study has successfully developed a medical priority aware cognitive radio enabled hospital system. All the key objectives of this study are mentioned in the introduction chapter. The major contributions are addressed in this chapter.
A critical analysis on different cognitive radio based healthcare literatures have been presented in this thesis. This thesis also presents a novel priority aware system design and network architecture for cognitive radio based hospital with network maintenance and device to device communication protocols. As noted earlier in Chapter 2, a complete system design, medical priority management, hybrid network topology and D2D communication protocols are absent in almost all cognitive radio based healthcare literatures.
This situation leads to the development of the proposed priority aware system design, hospital devices categorization and active queuing management based transmission scheme for cognitive radio network based hospital (Chapter 3). In Chapter 3, the cognitive radio based hospital system design is defined and the hospital medical and non-medical devices are categorized as hospital location and different services. Device categorization is also mapped with a proposed medical priority scheme. The priority scheme cop with active queuing management based transmission mechanism. In the queuing management two types of
queues are
proposed, FIFO for critical emergency medical traffic and weighted CB/F queue for
105 other category devices. A formula for calculates weight of the hospital traffic packet is also introduced.
A network model and hybrid topology based hierarchical hospital is also proposed for cognitive radio enabled hospital along with network maintenance and hospital device to device communication protocols in Chapter 4. In this chapter, cluster based architecture is developed for CBS and non-medical device network. The cluster leader selection for both architectures is based on a value of nodes. Two formulas are developed to estimate the value for both architectures. Star topology based architecture is proposed for medical devices and CT-SACs. To maintain the logical reliability of the architecture, two maintenance protocols namely node MoveIn and node Move-Out are also developed and presented in this thesis. Three EMI and priority aware device to device communication protocols are also developed. The protocols are medical device to admin center communication protocols, nonmedical to non-medical communication protocols and CT-SAC to admin center communication protocol.
NS2 based hospital simulation environment has been piloted to evaluate the performance of the proposed priority enabled scheduling algorithm and the network protocols (Chapter 5). The proposed simulation is also verified and validated. Moreover, a comparative study on the simulation result is implemented to measure the performance of the proposed scheduling mechanism and network as compared to the other established method and standards. The simulation results ensure the medical QoS in proposed cognitive radio based hospital.
6.2
Research Findings
The findings from the research are as follows,
Cognitive radio network can play vital role to defuse ―medical spectrum scarcity‖ in future wireless healthcare system. CR can also resolve the EMI issue and escalate the throughput quality for wireless medical devices.
106 However, it is observed that medical grade QoS and system design is sporadic for most of the recently proposed CR based healthcare systems.
It is observed that fully CR based hospital need to be considered. It is also observed that, the location of hospital devices needs to be identified accurately.
The proposed hospital devices categorization and medical priority policy upholds the QoS level of wireless medical devices in a wireless hospital by a medical network friendly transmission mechanism.
The proposed system design and network architecture ensure communication integrity and provide an ideal medical network model for CR enabled hospital by considering hybrid heterogeneous network architecture and the priority level of location of devices.
Network maintenance protocols uphold the integrity of the medical and nonmedical device architecture. It has been found that the proposed node joining protocol is dependent to the number of joining nodes. On the other hand, it is also found that the node leaving protocol is independent process (Section 5.4.2) in proposed CogMed architecture.
Proposed priority and EMI enabled communication protocols ensure the transmission benchmarking for wireless hospital devices.
6.3
Suggestions for Future Research
The suggestions for future research in cognitive radio based hospital network system are as follows:
107 Development of an enterprise level cognitive radio based hospital system design solutions, where different hospital units are properly distributed with CR enabled medical and non-medical in considering a real fully wireless large hospital system. Network traffic loads also need to consider for different medical priority and no priority locations.
Intelligent networks (IN) based centralize medical and hospital monitoring, administration center and critical alarm management system need to develop. The administration center equips with different databases and centralized automated drag administration system (triggered by medical alarm). The suggested administration center should be accessible to other medical big data facility and facilitated with secured offline/online data retrieval, on demand monitoring and controlling system.
Development of an efficient routing algorithm for cognitive radio based hospital system. The algorithm could focus on the routing of the medical and critical emergency packet by selecting the best sensed channel (Section 3.3). The medical routing algorithm might also consider channel occupancy history from spectrum historical databased in admin center (Section 3.3) and traffic load to determine the route.
Development of an efficient medical QoS enabled and hierarchical hospital network supported centralized cooperative spectrum sensing algorithm for proposed hospital. The algorithm will focus on medical priory and radio resource management (Section 3.3).
Development of cognitive radio transmission modules and methods for wireless medical devices. In that case MIMO transceiver and cognitive network controller need to design that can ensure high throughput and capability of wide spectrum scanning. Miniaturization of cognitive MIMO transceiver also needs to develop for medical sensors.
108
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116
APPENDIX A
LIST OF PUBLICATIONS (JOURNALS)
1. Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Sabariah Baharun, VoIP based Telemedicine Call center-Issues, Challenges and Proposed Solution Jurnal Teknologi 74 (vol 1) 63-71, 2015 Indexed :Scopus 2. Ishtiak Al Mamoon, A.K.M Muzahidul Islam,Sabariah Baharun, Shozo Komaki, Priority Aware Cognitive Radio Driven Hospital Management System, Wireless Personal Communications. springer (Under reviewed) Impact Factor : 0.98 Indexed : ISI Thomson Router 3. Ishtiak Al Mamoon, A.K.M Muzahidul Islam,Sabariah Baharun, Shozo Komaki, A Survey on Cognitive Radio Based Healthcare System, IEEE Communications Surveys and Tutorials (Under reviewed) Impact factor: 6.73 Indexed: ISI Thomson Router
117
APPENDIX B
LIST OF PUBLICATIONS (CONFERENCE PROCEEDINGS)
1. Ishtiak Al Mamoon, A.K.M Muzahidul Islam, Sabariah Baharun , Shozo Komaki and Ashir Ahmed , Architecture and Communication Protocols for Cognitive Radio Network Enabled Hospital, 9th International Symposium on Medical Information and Communication Technology – ISMICT 2015, Kamasuka, Japan . 2015, index : SCOPUS 2. Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Ashir Ahmed, Baharun Sabariah, and Shozo Komaki, Cognitive Radio Network in Future Hospital: Design and Definition of CogMed, IEEE Conference on Biomedical Engineering and Sciences (IECBES-2014) 2014 index : SCOPUS & ISI 3. Shahrizal Ahmed, Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Sabariah Baharun and Shozo Komaki, A Framework for Remote Monitoring of Early Heart Attack Diagnosis System for Ambulatory Patient. , IEEE Conference on Biomedical Engineering and Sciences (IECBES-2014) 2014 index : SCOPUS & ISI 4. Kuheli Mondal, A.K.M. Muzahidul Islam, Ishtiak Al Mamoon, Shamsunnahar Khanam, and Sabariah Binti Baharun, and Megat Johari, An Information And Communication Technology Based Smart City, Malaysia – Japan Joint International Symposium MJJIS 2014 5. Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Ashir Ahmed, Baharun Sabariah, and Shozo Komaki ,A Wireless Tele-Healthcare System For Rural Inhabitants In Malaysia, Malaysia – Japan Joint International Symposium MJJIS 2014 6. Shahrizal Ahmed, Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Sabariah Baharun and Shozo Komaki, A Proposed Framework For Early Heart Attack Diagnosis System, Malaysia – Japan Joint International Symposium MJJIS 2014 7. Shahrizal Ahmed, Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Sabariah Baharun and Shozo Komaki, A Proposed Early Heart Attack Diagnosis System , ‖ 7TH AUN/SEED-NET INT‘L CONFERENCE ON EEE 2014 MJIIT, UTM, Kuala Lumpur, Malaysia.
118 8. Kuheli Mondal, A.K.M.Muzahidul Islam, Ishtiak Al Mamoon, Shamsunnahar Khanam, Sabariah Binti Baharun and Megat Johari ,A Future Smart City Using Information and Communication Technologies ‖ 7TH AUN/SEED-NET INT‘L CONFERENCE ON EEE 2014 MJIIT, UTM, Kuala Lumpur, Malaysia 9. Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Shahrizal Ahmed,Sabariah Baharun and Shozo Komaki ‖ Tele-Urology: Current Status and Future‖ 7TH AUN/SEEDNET INT‘L CONFERENCE ON EEE 2014 MJIIT, UTM, Kuala Lumpur, Malaysia 10. Ishtiak Al Mamoon, A.K.M Muzahidul-Islam, Sabariah Baharun and Shozo Komaki ―Cognitive Radio Network in Future Hospital: Design and Definition of COGMED‖ IEEE EMBS International Conference (ISC 2014), June 5-6, 2014, Malaysia. Index: Scopus 11. Ishtiak Al Mamoon, A.K.M Muzahidul-Islam, Sabariah Baharun and Shozo Komaki, Proposed Architecture of Cognitive Radio Based Hospital Management (CogMed) System, IEEE EMBS International Conference (ISC 2014), June 5-6, 2014, Malaysia. Index: Scopus 12. Ishtiak Al Mamoon, A.K.M. Muzahidul Islam, Sabariah Baharun, Shozo Komaki, ―A Priority Aware Cognitive Radio Based Hospital System Architecture, Priority Management and Communication Protocols‖ 8th International Symposium on Medical Information and Communication Technology – ISMICT 2014, Florence, Italy Index: Scopus 13. A.K.M. Muzahidul Islam, Ishtiak Al Mamoon, Nafees Mansoor, Mahdi Zareei, and Megat Johari, ―HajjNet: A Cluster-based HAJI Services Network‖, 11th International Conference on Engineering Education 2013 (ICEE2013) Madinah, KSA. 2013 14. Ishtiak Al Mamoon, Ahmad Shahrizal Sani, A.K.M Muzahidul Islam, Ooi Chia Yee, Fuminori Kobayashi, Shozo Komaki ‖ A proposal of Body Implementable early heart attack detection system‖, Malaysia – Japan Joint International Symposium (MJJIS 2013), Hiratsuka, Japan 2013 15. Ishtiak Al Mamoon and A.K.M. Muzahidul Islam ‖ International And Domestic Call Center In Same Premise/Platform Without Risk Of Illegal VoIP – Context Bangladesh‖, Malaysia – Japan International Institute of Technology (MJIIT) – Japanese University Consortium Committee (JUC) Joint Symposium (MJJS 2012), Kuala Lumpur, Malaysia, 21-23 November, 2012.
