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International Journal of Computer Science and Communication

Vol. 2, No. 1, January-June 2011, pp. 183-189

TRAFFIC AND PERFORMANCE MANAGEMENT FOR BIOMEDICAL SENSOR NETWORK Dheerendra S. Gangwar Department of ECE, G.L.A. University Mathura-281406, U.P. India, E-mail: [email protected] ABSTRACT This work examines the performance statics of event based data delivery model for a Biomedical Sensor Network (BSN) designed in accordance with IEEE 802.15.4/ZigBee wireless communication technology. A BSN consists of 5 to 10 invasive or noninvasive sensor nodes acquiring physiological signals from the subject body and transmitting it to the network coordinator through wireless channel. All the sensor nodes in the network share the same medium with different traffic characteristics and different Quality of Service (QoS) requirements. This work presents a simulation model for channel access mechanism and on demand awakening in Biomedical Sensor Network. On the basis of the BSN traffic specifications, we have used Castalia framework to validate traffic and performance parameters of prototype BSN model in order to meet desired QoS requirements. The performance metrics of the BSN architecture includes energy consumption, packet reception ratio, network capacity, connectivity and packet transmission delay. Keywords: IEEE 802.15.4, ZigBee, Physiological Signals, Sensor Node, Network Traffic.

1. INTRODUCTION Biomedical Sensor Network (BSN) is a new class of wireless networks which offers opportunities to new services for monitoring health, fitness and wellness of individuals. It offer prompt feedback for efficient and reliable patient monitoring, disease management and promotes self care [1]. A typical BSN consists of number of sensor nodes with different resource requirements like data processing capability, power requirements, bandwidth requirements and reliability features. Special design characteristics of sensor and their human centric application make such networks different from conventional wireless networks [2]. These characteristics pose different challenges for system architecture and protocol design. For example BSN nodes require low complexity computational resources and energy efficient communication to support efficient and reliable transmission of physiological parameters. A critical design issue for Biomedical Sensor Networks is limited availability of hardware resources within BSN nodes (shown in Fig. 1). Most of the sensor nodes are Reduced Functional Devices (RFD) having limited resources and few of them may be Full Functional Devices (FFD). Therefore making good use of these resources is an important design issue. These nodes are supposed to operate for longer duration, as in case of implanted nodes expected life span ranges from 5 to 10 years. Low power consumption and QoS requirements for reliable transmission of acquired parameters is must [4]. To satisfy all these requirements nodes are designed to operate with energy saving communication and data processing hardware resources [5] that places sensor nodes in sleep mode. On demand awakening and event

Fig. 1: Biomedical Sensor Network

driven data delivery mechanisms are beneficial to obtain longer life span for sensor node [6]. This model is designed on the basis of CC2420 base radio transceivers, operating on IEEE 802.15.4/ZigBee communication standard help in achieving these endeavors [7]. The proposed model uses similar design features for simulation as they are supported by the CC2420 transceiver. Remaining paper is organized as follows. Previous work is presented in Section 2, which describes some simulation studies carried out in this field. BSN system architecture is introduced in Section 3 followed by traffic and performance management issues of the BSN networks in Section 4. A simulation model for prototype system is described in Section 5. Performance analysis is presented in Sections 6 and 7 concludes entire discussion. 2. RELATED WORK Many of the researchers are putting their collective efforts towards the development of Health Monitoring Systems

International Journal of Computer Science and Communication (IJCSC)

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(HMS) to provide cost effective and efficient medical assistance to the people [8]. Technological advancements in the field of Biomedical Sensors and Wireless Communication have made possible development of new applications in the field of health care monitoring [9]. Protocol stack model for BSN network is shown in Fig. 2, depicting various aspects related to communication, process management, mobility management and power management. Biomedical Sensor Network based heath monitoring systems are no more the subject of curiosity for academic and research community. ZigBee enabled healthcare solutions have moved from the soft academic environment to the harsher world of commercial applications [10]. As power efficiency is very important issue to design a practical BSN solution and adequate attention has been paid in this direction. Chang and Huang [11], Lo and Yang [2], Chen and Dressler [12] and C. Mallanda [13] are major contributors for power efficient techniques for such type of systems.

advance Digital Signal Processors and other integrated application software platforms. Critical alarm signals and periodic diagnosis information is sent to medical professionals for further actions using extra-BSN communication link. This link is facilitated by existing public communication network. However this work is focused only for intra-BSN communication of physiological information. BSN network consists of three modules namely Sensor Node, Physical Process and Wireless Channel. However sensor node has some sub modules down the hierarchy. These modules are Application Module, Network Interface Module, Node Resource Manager, Mobility Module and Sensor Device Manager. Network Interface Module (shown in Fig. 4) is further divided into Network, Medium Access Control and Radio Module. This paper describes design issues associated with each design entities of BSN Network. Out of all the sub-modules of the sensor node, communication or network interface module is a compound one that comprises of three different modules representing communication protocol stack. These modules are Radio Module (corresponding to the Physical Layer), MAC Module (corresponding to the Medium Access Control Layer) and Network Module (corresponding to the network routing Layer). For modeling of this network prototype Castalia framework is used [14]. It is a simulation framework for Wireless Sensor Networks consist of low power embedded devices and uses OMNeT++ as basic simulation platform [15]. Castalia is a very good research vehicle for simulation protocols in realistic wireless channel and radio models, with a realistic node behavior especially relating to access of the radio link.

Fig. 2: Protocol Stack for BSN Network

3. BSN ARCHITECTURE A prototype BSN model is shown in Fig. 3, illustrating all physiological parameters and sensor nodes. This proposed architecture is supported by five physiological sensors acquiring Electrocardiogram (ECG), Blood Pressure (BP), Blood Oxygen Saturation, Temperature and Body Movement. This multi-parametric and multidimensional time series information is transmitted by each individual sensor node to the BSN coordinator. All detection decisions are taken at this stage with the help of

Radio module is a simple module which is defined with the help of C++ and NED files incorporating entire functional behavior of Physical Layer as defined for IEEE 802.15.4 wireless communication standard. The radio module tries to capture many features of a real generic low power radio, one that is likely to be used in wireless sensor network platforms. As such, it supports multiple states (transmit, receive/listen, sleep) with different power consumption and delays for transitions from one state to another. It supports multiple transmission power levels. It also supports carrier sensing (with help from the wireless channel module). The user can play with the data rate and other parameters that affect the probability of packet reception given a Signal to Interference Ratio. In the proposed model radio parameters for radio module corresponds to the Chipcon CC2420 transceiver [16]. The Medium Access Control protocol is an important part of the node’s behavior; therefore in the proposed model there is a separate MAC module that defines it. The traffic based MAC protocol for BSN accommodates the entire BSN traffic in a reliable manner. A beacon enabled IEEE 802.15.4

Traffic and Performance Management for Biomedical Sensor Network

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Fig. 3: BSN Architecture Model for OMNeT++

• CSMA-CA functionality (slotted); • Beacon-enabled network architecture; • Direct data transfer mode; and • Guaranteed time slots (GTS). BSN communication is supported by single hop star network topology and therefore routing is not a big issue. The network layer of BSN coordinator is responsible for start up of network and assigning network addresses to newly associated devices [1]. The underlying MAC layer adapts the BSN network address as 16 bit short address. Addresses are unique to a particular network and are assigned by BSN coordinator to the BSN nodes. ZigBee routing algorithm is designed to enable reliable, cost effective and low power monitoring and control operations.

Fig. 4: BSN Communication Module

MAC protocol based on slotted CSMA-CA satisfies BSN traffic requirements [17]. IEEE 802.15.4 MAC protocol is the standard for low power wireless networks. Proposed BSN model describes following features of MAC protocol:

To make sensing operation more relativistic physical process model is required which corresponds to real world physical parameters. Castalia supports a physical process model that is flexible enough yet have correspondence to real processes (e.g., spatial correlation of data, variability over time). The wireless channel is a notoriously difficult medium to model, especially when taking into account mobile nodes, a changing environment (e.g., in the BSN case: the body moving) and broadband communications [14].


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4. TRAFFIC AND PERFORMANCE MANAGEMENT Traffic management is required to offer sustainable end to end Quality of Service (QoS) support for efficient, reliable and cost effective data transport from the sensor node to the BSN coordinator [18]. Traffic management takes care of lost packets due to buffer overfolow and gauranties reliability for packets flowing through BSN. Ammount and type of clinical data is different for each sensor node associated with a specific physiological variablel [4]. Table 1 summarises BSN traffic specifications required for physiological signals [19]. Traffic management and data delivery methods are different for BSN in comparision to conventional wireless sensor netowrks, these networks support event based data delivery model ratrher than continuous or query based delivery model. Effective traffic management enhances network performance in terms of: • Throughput; • Message delay; • Energy efficiency; and • Buffering and bandwidth requirements. Table 1 BSN Traffic Specifications

Signal

Parameter Range

Traffic (kbps)

ECG

0.5 – 4.0 mV

Blood Pressure

10 – 400 mmHg

0.96

Oxygen Tension

80% – 100%

1.2

33 – 40

0.32

Body Temperature Body Motion

C

Ankle Movement

8

PRR =

Number of ACK packets Number of PRBpackets

... (1)

Single hop average delay using CSMA-CA MAC protocol is calculated with the help of timestamp difference between Probe (PRB) packet and Acknowledgement (ACK) packet received

 TimestampACK − TimestampPRB  Delay =   2  

... (2)

5. BSN SIMULATION MODEL BSN model, shown in Fig. 5, consists of modules that communicate by passing messages. BSN network consists of three modules namely Sensor Node, Physical Process and Wireless Channel. However sensor node has some sub modules down the hierarchy. These modules are Application Module, Network Interface Module, Node Resource Manager Module and Sensor Device Manager Module. Network Interface Module is the most significant module that plays a pivotal role in modeling of various communication and data handling protocols and processes. It is further divided into Network Layer Module, MAC Layer Module and Radio Module. For the sake of simplicity without compromising general functional behavior of the proposed model only necessary functions are taken into account.

0.32

Another key design challenge is low power which is essential to prolong sensor life time and it depends on QoS requirements for reliable data delivery [4], [9], [ 11] and [17]. Energy is wasted in case of any packet discard or packet collission. Energy efficient MAC protocols [17] and transport protocols help a lot in power saving process in BSN communication. Zigbee offers Link Quality Indication (LQI) mechanism to manage efficient and reliable communication link by measuring signal strength and quality of received packet. The Link Quality Information is exchanged with the help of Probe (PRB) packet. Performance metrics for BSN traffic includes throughput, latency, network connectivity and power consumption. In Biomedical Sensor Networks event reliability is used as a measurement to show accuracy of transmission of event from source to sink. For most of the applications, having tolerance for packet loss reliability is defined as Packet Reception Ratio (PRR). It is given as the ratio of successfully received packet over the total number of packets transmitted. Packet Reception Ratio (PRR) of BSN Network is given as

Fig. 5: BSN Simulation Test Bed

Modeling style for Reduced Functional Device (RFD) and Full Functional Device (FFD) coordinator is accomplished keeping all the design and power constraints in mind. Communication process and channel access is controlled by Medium Access Control Module. Network Layer Module is responsible for managing network resources for communication process. Node Sensor Device Manager Module takes care for sensing device and physical process associated with the target application. Node Resource Manager Module handles data processing and


power supply to all node modules and applications. Node Application Module is accountable for handling of application it is involved and makes it application specific. This Module divided lager data packets into smaller ones and interacts with Network Layer Module of Communication or Network Interface Module interfacing of various modules is also very important feature of this simulation model that provides a set of rules for interaction among all network entities for better device and process management. Traffic simulation parameters are given in Table 2, where each node corresponds to a physiological information signal. Simulation of this model (described in .ned file) after providing various simulation parameters for each node and module in the initialization file (*.ini file) results into the performance statistics is shown in Table 3. The file omnetpp.ini includes information regarding various simulation parameters. The ECG signal information is the most critical out of all these parameters and

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need to be given highest priority for packet delivery from ECG sensor node (Node (1)) to the BSN Coordinator (Node (0)). This model simulates only reception of physiological information for intra BSN communication and this model does not consider extra BSN communication and data mining for detection decisions. Table 2 BSN Traffic Simulation Parameters

Parameter

Value

BSN.node[1].nodeApplication.packet_rate

8 kbps


960 bps


1.2 kbps


320 bps


320 bps

Table 3 Delay and PRR for BSN Network

Sensor node

Transmitted packets

Received packets

Packet reception ratio (%)

Average delay (ms)

400

395

98.75000

17.7143

0.0143556

Blood Pressure Node (2)

48

47

97.91667

17.7143

0.0144865

SpO2 Node (3)

60

54

90.00000

17.7143

0.013255

Temperature Node (4)

16

15

93.75000

17.7143

0.013255

Body Motion Node (5)

16

15

93.75000

17.7143

0.0139856

ECG Node (1)

6. PERFORMANCE ANALYSIS This paper presents an empirical investigation on the performance of BSN network model using IEEE 802.15.4/ ZigBee wireless communication. Simulation is required to validate proposed algorithms and protocols before physical implementation to save engineering resources and time involved in the process. BSN traffic verification using OMNeT++ simulation environment is carried out with the help of various simulation parameters and simulation class libraries. Functional validation of BSN traffic and performance is based on IEEE 802.15.4/ZigBee protocol stack. Some related medium access control parameters are given as BSN.node[*].networkInterface.macModuleName = Mac802154Module. BSN.node[0].networkInterface.MAC.isFFD = True. BSN.node[0].networkInterface.MAC.isPANCoordinato = Truer. BSN.node[1].networkInterface.MAC.requestGTS = 3. After performing the simulation various performance statistics are generated. Packet delay, delay histogram, packet reception information, loss of packets caused by interference, low sensitivity and non Rx state are major to

Power consumption (mW)

quote here. This information helps in calculation of average delay for network nodes and overall throughput. Average packet delivery delay is calculated as

Delay _ avg =

Total _ Delay Packet _ recived

... (3)

Average delay for simulation is found as 17.71429 ms. The information for Total_Dealy and Packets_Received is drawn from the delay histogram generated for application level latency as shown in Fig. 6. Different packets reach the BSN coordinator with different packet delay profile

Fig. 6: Application Level Latency for Data Packets


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ranging from as low as 1ms and up to 600 ms. This Figure illustrates histogram values for packet delivery delay. Throughput for network is expressed as

Throughput =

Total _ Data _ Bits Simulation _ runtime

... (4)

Total_Data_Bits can be calculated from number of number of Packets_receive and packet size. For the given simulation model packet size is 1024 bits and for simulation, run time of 50 seconds. If number of packets received is 525 then BSN network throughput is 10.50 kbps. Packet Reception Ratio of all sensor node transmitting information to Node (0) or BSN coordinator is shown in the Fig. 7. Node (1) has highest PRR whereas node (3) is having the lowest one. Figure 8 presents analysis for power consumption at every node transmitting packets to the Node (0). Node (2) is consuming highest power as it is handling the greatest number of packets. Energy consumption for Node (0) is 0.158175 mW, which is considerably high in comparison to any of the sensor node.

transmission reliability and power efficiency is managed by connectivity of the nodes with the BSN coordinator. This simulation model also presents fading profile for the wireless channel. Fed depth distribution is shown in Fig. 10 that illustrates histogram for the range of – 50 dB to 10 dB values for different points. Channel characterization is a very critical issue because human body itself affects channel characteristics. An accurate estimate of fade depth is also of great importance for design of reliable communication. Simulation tracks record for packet id of transmitting node and received signal strength indicator (RSSI) values. The receiver sensitivity for BSN model is set as – 87 dBm.

Fig. 9: Fraction of Time without PAN Connection

Fig. 7: Packet Reception Ratio for BSN Nodes Fig. 10: Fade Depth Distribution for Wireless Channel

7. CONCLUSION

Fig. 8: Power Consumption for BSN Nodes

Network connectivity is an important performance metrics therefore Fig. 9 presents a statistical overview for connectivity information for all sensor nodes. The wakeup scheduling schemes at the MAC layer which wakes up sleeping nodes when they need to transmit/receive, thus avoiding degradation in network connectivity or quality of service provisioning. The tradeoff between

Technological advancements in the field of medical sensors, artificial intelligence along with information and communication technologies are opening new paradigm in the field of health monitoring process. A prototype BSN model is simulated in OMNet++ simulation environment to validate traffic characteristics that emphasizes channel access control to serve many purposes like packet loss reduction, sleep mode operation, power efficiency and packet delivery latency. Results for traffic characterization and performance evaluation are discussed. The prototype system uses power efficient event based data delivery model to report the physiological signal for reduction of communication power. Vital signs are acquired and passed on to the base unit on the basis of ‘on demand awakening’ of sensor nodes for transmission of sensor information. Power efficiency of the system was the major concern throughout entire discussion, which was addressed by on


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