2014 5th International Conference on Information and Communication Systems (ICICS)
An Efficient Big Data Collection in Body Area Networks Muhannad Quwaider
Yaser Jararweh
Department of Computer Engineering
Department of Computer Science
Jordan University of Science and Technology Irbid, Jordan
Jordan University of Science and Technology Irbid, Jordan
[email protected]
[email protected]
Abstract— In this paper we present an efficient big data collection model in Body Area Network (BANs) using cloudletbased system prototype. The novelty of the proposed work is to have the monitored data of BANs in a large scale and deliver it in reliable manner to the service providers. A prototype of BANs is proposed in this paper to include virtualized machines and Cloudlet in order to characterize the efficient BAN data collection. A scalable storage and processing infrastructure have been proposed to support large scale BANs system, which is efficiently capable to handle the big data generated by BANs users. The model supports effective cost communication technologies through Wi-Fi technology. Performance results of the proposed prototype are evaluated using advanced CloudSim simulator. The performance results show the consumed power and packet delay of the collected data is decreased by increasing the number virtualized machine and Cloudlets. Keywords— Body Area Networks, Cloud Computing, Mobile users, Big Data Collection, Virtualized Cloudlet
I.
INTRODUCTION
In Body Area Networks (BANs), a set of sensor nodes communicate among each other to collect different on body vital signs. These sensor nodes can be implanted or wearable [1] [2]. The collected data from these sensors are usually delivered in real time to the service provider like clinic of hospital. Healthcare applications are expected to be one of the most important fields in BANs, where collecting the vital signs data from patients’ needs to be in continuous and in real time manner, particularly, when BANs applications are used to monitor the elder care, patients who suffering from continuing diseases, like asthma, diabetes, blood pressure or heart attacks. BANs applications can be also seen in sports, social computing, schools, security, military and gaming. This technology can be extended to improve the communication between individuals themselves and between the individuals and machines. BAN is constructed by mounting several senor nodes on the human subject. The sensor node is capable of measuring, sampling, computing, handling and communicating with other sensor nodes. The vital signs include diabetes, heart rate, oxygen saturation, temperature, blood pressure, breathing rate, activity and ECG. In addition, environmental parameters can be collected, like humidity, temperature, location, direction, proximity, movement and light. Cloud computing is a new computing paradigm that is
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constantly evolving and spreading. Empowered by hardware virtualization technology, distributed computing, advance management framework and web services, cloud computing presents unprecedented revolution in the information and communication technology [3]. Cloud computing can be defined as “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [4]. There are several examples for publically available cloud computing infrastructures and platforms such as Microsoft Azure [4] , Amazon EC2, Google App Engine, and other on premising cloud, i.e. private cloud [5]. Furthermore, cloud computing helps companies to improve the IT services, reduce operational cost, development of applications to achieve unlimited scalability, automaticity on demand services of the IT infrastructure, and increasing their revenues [6]. Cloud computing service models include: Software as a service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Clients of cloud computing might be users in other Clouds, organizations, enterprises, or might be a single user [7]. In BANs applications like, military, firefighter or sport, there will be enormous amount of data collected by BAN nodes need to be on-demand, powerful, secure and scalable. Therefore, a cloud computing environment is needed to achieve the required goals. The diminished sensor nodes can be linked with ranged of different super computer through high cloud computing environment in order to deliver the context centric or people centric of the individuals and industries with the required data. The huge amount of collected data from numerous BANs can be integrated with cloud computing which will present mixture and practical platform. The massive amount of data collected from several BANs can be processed using this platform. The proposed model of BANs in conduct with cloud will enable the end users to worldwide access, and at inexpensive rates, with all required storage and processing infrastructure. Usually, the information sent to the cloud from BANs is very useful, lifecritical and on run time, therefore this information needs to have a secure mechanism to prevent malicious interactions to the storage infrastructure. The users and the cloud providers, both must protect the storage infrastructure by taking strong security measures.
2014 5th International Conference on Information and Communication Systems (ICICS)
A novel cloud infrastructure model that exploit cloudlet based computation for supporting large scale data collection in BANs will be discussed in this paper. A cloud system with high computing capability is integrated with big data collection from the BANs users. The cloud system will support the BANs users by connecting them directly using cheaper communication technologies to the cloud resources. In this paper we developed a large scale cloudlet-based data collection model for BANs system. The main objective is to reduce the packet communication delay from the end user to the cloud using cloudlet system. The idea is to send the end users data to the service provider in real time manner. The proposed framework is trying to reduce the end to end packet energy, as well as the packet delay. Moreover, we don’t consider the delay due to the packets congestion, because we seal with packets with low data rate, and usually the delay is caused by packet storage, which is usually much larger than the congestion delay. The rest of the paper is organized as follows. Section I presents an introduction to the proposed system. Section II has provided background and related work. Section III proposes the system prototype. The performance results have been discussed in Section IV. Lastly, Section V summarizes the conclusions and future. II.
RELATED WORK
The process of combining multiple packets from multiple nodes into one single packet in order to reduce the network energy is called data aggregation [8]. The goal of data aggregation is to decrease the number of data packets that are transmitted through the network, as well as to reduce the total network energy. The authors in [9]–[11] proposed energy efficient approaches for data collection by merging multiple packets collected from multiple nodes in the network and then forwarding only one packet to the destination. The multiple sensor nodes in these proposed approaches measure same event. Likewise, the relay nodes also measure same event and aggregate the data packets into a single packet. The proposed approaches are not suitable for BANs prototype because to make any decision in BAN, the measured data packets of the sensor nodes should be complimentary [12]. In this paper, however, the goal is to decrease the energy of the transmitted packet by using low energy Wi-Fi technology comparing with relatively other higher energy technology. MobiCloud [13] describes how cloud computing and mobile computing are integrated together to generate something called mobile cloud computing. The authors in this study develop the base-line and the research challenges of the MobiCloud. The authors in [14] studied the impression of using Cloudlet in cloud mobile computing in communicating applications. Two models are compared in the proposed work in terms data transfer delay and system throughput. New architecture called MOCHA was proposed by authors in [15] . The proposed architecture is used to face recognition applications by reducing the response time of the recognition during the face process. The idea of MOCHA is to integrate the Mobile device, cloud servers and cloudlet. The authors in admission control and resource allocation problems for the running mobile
application in the cloudlet have been studied in [16]. The authors [17] studied the technical obstacles that can be used for cloudlet in mobile computing, where the mobile users inside the cloudlet open the sessions. The authors in [7] provide a Cloudlet based Mobile Cloud Computing (MCC) system with the objective of reducing both the power consumption and the network delay while using MCC. The key novelty of the proposed work in this paper is coming from integrating the cloud computing and BANs by the ability of supporting the cloud system in continuity increasing the demands on BANs with high capacity computing and scalable storage. III.
SYSTEM PROTOTYPE
In Body Area Network (BAN), several sensor nodes are placed on a human subject to collect different body vital signs data, like, heart rate, blood pressure, diabetes, ECG, breathing rate, humidity, temperature, movement, direction, proximity, etc. The sensor nodes on the human subject can form a star topology. In star topology, the sink node collects the sensing data from the all body sensors. Then, the collected data is aggregated into one packet to reduce the cost and the communications in the network. Via Bluetooth, the aggregated packet is sent to a Personal Digital Assistant (PDA) or a smart phone for BAN monitoring application. Then, a web-service module is used to upload the Internet servers with the observed data using either Wi-Fi or cellular technology e.g. 3G or LTE for data communication. The Wi-Fi allows an electronic device to communicate and exchange data to the internet using radio wirelessly waves [18]. On the other hand, cellular network is a radio distributed network over areas, where each network functioned by at least one transceiver with a fixed location. A cellular network enables the mobile phones to communicate with fixed transceivers of internet and with each other. The following are the trades between using these two communication technologies. A BAN user can transmit the data packet to the cloud with low power and low delay using Wi-Fi technology compared with the 3G or the LTE cellular communication [19], but with transmission range with Wi-Fi does not exceed 100m [20]. The capability of Wi-Fi is essential to have efficient power consumption in BAN sensors and transmitting data to the cloud system in a successful manner. The Wi-Fi technology in our implementation should be available in the cloudlet area. Via Wi-Fi, it was shown that the transmission power of a data packet of size 46 Bytes will cost about 30 mw [7], [19], [21] and with data packet delay of 0.045 ms. On the other hand, cellular communication technologies, like 3G or LTE has longer transmission rage and the user is capable to transmit the data packet to the enterprise cloud from any position that is cover by cellular network. Therefore, cellular communication technology has usually wider geographic area compared with the Wi-Fi technology. While, the transmission power of data packet of size 46 Bytes and using Wi-Fi technology will cost 300 mw and with a delay of 0.45 ms [19], [21]. Therefore, the cellular communication is very expensive in terms of power and delay compared with Wi-Fi, which is mostly free of charge. The importance of using Wi-Fi
2014 5th International Conference on Information and Communication Systems (ICICS)
technology in BANs is the number of users that are involved in the application, as we will discuss in Section IV. One of the main contributions of the proposed model is supporting mobility of BAN users. In the prototype evaluation we conduct a random way point user mobility model, where for a given area and at any given point in time, the mobile user can be in one of following regions; Cloudlet Region (CR), Enterprise Region (ER) or Not-covered Region (NC). While in CR, a user is able to transmit the data packet using Wi-Fi technology to a cloudlet, but in ER the user can use only cellular technology to transmit the data packet to the cloud. Nevertheless, neither Wi-Fi nor cellular technology is available in NC region. In order to validate the ability of the proposed prototype model, CloudSim [22], [23] simulator tool is extended in order to realize our proposed Cloudlet-based BANs model. CloudSim was developed at the University of Melbourne and it is a well-known cloud-based simulator tool. The extended CloudSim simulator includes components of cloudlet and BANs prototype [24]. Cloudlet system is similar to cloud system capabilities but in small scale. Cloudlet uses a virtualization technique that translates its hardware components to a set of virtual components like in Virtual Machines (VMs). Each cloudlet is capable to support large number of BAN users with Wi-Fi and Wi-Max, where Wi-Max technology is used for long transmission range. Cloudlet can be connected with other cloudlets or with the enterprise cloud using a wireless or a wired communication. The cloudlet entity used in our CloudSim implementation has the following specifications. The Industry Standard Architecture (ISA) is x86 with operating system of Linux and Virtual Machine Monitor (VMM) of Xen. As Virtual Machine Allocation Policy, allocate VM to the host with lowest utilization is used with Storage Capacity of 1 Terabyte and 2 with 4 cores per CPU. Each core uses 2660 MIPS with memory capacity of 8 GB and virtual machine scheduler of space shared. The cloudlet communication bandwidth is 10 Mbps that is supported by Wi-Fi with 100 meter transmission range and Wi-Max with 15 km transmission range. In order to study the impact of using Conventional Server (CS) and Virtualized Cloudlet (VC) on the performance metrics, like power consumption and processing delay, simulations were carried out using extended version of CloudSim. Human subjects with BAN are moving within a circle area with a radius of 100m and centralized with a conventional or cloudlet server for data collection. Set of experiments were carried out, where each experiment lasts for 3600 sec. The number of users is ranged from 10 to 150. Users are moving with a speed of 2m/s and a pause time randomly chosen from 1s to 10s. The mobility of the human subjects corresponds to random way point mobility model, similar to the real life applications of moving firefighters, , soldiers or students in a target area [18], [25]–[28]. A data packet is sent from each user to the virtualized cloudlet (VC) using Wi-Fi with a time period of 10s and size of 46 Bytes. The speed of processing the received data packet at the VC is wide-ranging between 100 to 900 Million Instructions Per Second (MIPS). The consumed power and processing delay at the VC are shown in Figure 1. In the figure, the number of Virtual
Machines in the Virtualized Cloudlet (VM-VC) is changed to be 2, 4 or 8. From Figure 1 we can conclude the following the efficient data collection with VC system provides better opportunity system scalability by increasing the number of users and distributing different BANs tasks loaded by diverse MIPS per task. For a given task, the trends of consumed power and processing delay are increased when increasing the number of users. The reason of that is, increasing the task load at the VC which needs more power and processing time to complete the user task. While, by increasing the number of VMs, the slope of consumed power and packet delay is decreased because of reducing the processing time in the BS and increasing the number of VMs. On the other hand, CS shows the highest consumed power and processing delay because the machine uses only one VM. The final observation that can be made from Figure 1is, by using cloudlet system configured with 8 VMs comparing to using only one VM in CS, the processing time is decreased by roughly 85%. With increasing of the task size or MIPS, the delay results also exposed the scalability of cloudlet system. Figure 2 shows power and delay vs. number of users with 2 VM. As shown in the figure by increasing the MIPS by 200 MIPS, less than 20% of power and delay are increased. By sharing more number of VMs in the cloudlet resources, these results explain the slightly increasing the power consumption. If 8 CSs are used instead of one system 8 VMs, the consumed power will be increased 11 times. IV.
PERFORMANCE RESULTS
A. BAN and Cloudlet-based Data Collection System Figure 3 shows a high level overview data collection system of our proposed Cloudlet-based and BANs. The prototype composes sets of BANs. The BANs are composed of multiple users (each user is equipped with BAN), who are able to transmit the collected data by the BAN to the outside of the body, as described in Section III. A group of users with BANs can be virtually clustered around one cloudlet server that is representing cloud computing capabilities in a small scale. The cloudlet system is composed of set of VC servers with many cores and huge memory size. The cloudlet server system is equipped with one or more of the communication antennas that is supporting different physical layer capabilities (e.g.WiFi and Wi-Max). The most important part of the VC server is the storage system. The storage system should provide scalable and reliable environment for storing large data size. Processing of large scale data i.e. big data is also an essential feature of any cloudlet system. Different cloudlet systems could be connected with each other using wired or wireless communication links (e.g. Wi-Max). Furthermore, cloudlet system could be connected directly to an enterprise cloud system using wired or wireless communication links. The enterprise cloud system is a centralized management and storage point that can be accessed by different organizations that are interested in a certain type of data. Another important feature of the cloudlet system is that the ability of the bidirectional communications between many BANs users. In addition to its ability to receive data from multiple users, the
2014 5th International Conference on Information and Communication Systems (ICICS)
cloudlet system is also able to communiccate with multiple
users based on the usage scenarrio.
Figure 1: Powerr consumption and processing delay with respect to number of userss
Figure 2: 2--VM Power consumption and Delay with respect to number of users
2014 5th International Conference on Information and Communication Systems (ICICS)
speed of 2m/s and a random pause time of 1s to 10s and each user sends a packet to the cloudd with a period of 10s using WiFi or cellular technology. Thhe performance results of using cloudlet-based data collection are a shown in Figure 4. As show in the figure, the number of VC V in the given area is changed from No-VC to 6-VC, where 6-VC is the maximum number of VC can be installed in the area without any overlapping between them. For a given VC area of radius 100m, a user was able to transmit a data paccket to the VC using Wi-Fi technology; otherwise, the user u should use the cellular technology to transmit the dataa packet, as reported in Section III. The following clarificationss can be made from Figure 4. First, the average consumed power and delay are decreased Cs. By increasing the number of by increasing the number of VC VCs for a given area, the Wi-Fi W coverage zones will be increased in the area and the usser will have more chance to use Wi-Fi technology for transmiitting the data packet to cloud rather than of using cellular technology. t It is clearly shown that using Wi-Fi will significcantly reduce the transmission packet cost. The second obserrvation that can be made from Figure 4 is, for a given andd by VCs deploying, the VCs deployment was able to track the mobility of users in some Fi technology. sense in order to offer the Wi-F
Figure 3: BAN with Cloudlet-based data collectioon system prototype
B. Performance Metrics Two performance metrics are used to evaluate the data collection from BANs using cloudlet [29], [30], transmission power and packet delay. The transmission power and packet delay are directly measure of the communication energy and delay expenditure from the PDA device in BA AN to the cloudlet or the enterprise cloud. In this work, we do not n include the onbody power drainage and delay due to seensing and packet routing because they depend on the application and are beyond the scope of this paper. We quantify the needed n power and delay from BAN to cloud communication to monitor set of BAN users during particular period of time.. It is obvious that the variation of power and delay results comee from the number of transmitted packets within cloudlets coverrage to the number of transmitted packets within to the enterprisse cloud. The goal is to minimize the consumed power and trannsmission delay by having a user within cloudlet coverage. C. Cloudlet-based BANs Results o using updated The following simulations were carried out CloudSim [23] in order to study the impact of installing VC w need to see the and CS on the power and delay. The idea is we impact of increasing the number of VC in thhe monitored area on the performance results. Each simulatioon in these results lasted for 3600s in an area of 400x600m. 4000 human subjects are moving in the area with random way pooint mobility with
Figure 4: Power consum mption and delay vs. VCs
V.
CONCLUSION N AND FUTURE WORK
In this paper a large scale BANs B data collection system in the presence of cloudlet-based is presented. The objective was to minimize the user to clooud packet energy and delay dynamically choosing data com mmunication technology of WiFi in the cloudlet zone or celluular technology, otherwise. The goal is to deliver the monitoredd data from BANs to the service provider in real-time manneer. Performance results were evaluated using extended ClooudSim simulator. The results show that the average consuumed power and delay of the transmitted packet are decreased by increasing the number of VCs, because of increasing the coverage area with the Wi-Fi technology by deploying moree number of VCs in the area. As future work, we are going to develop a Kalman Filter based body movement prediction too predict the location of VCs deployments. RENCES REFER [1] [2]
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2014 5th International Conference on Information and Communication Systems (ICICS)
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