BEC: A Novel Routing Protocol for Balanced Energy Consumption in Wireless Body Area Networks

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1 : A Novel Routing Protocol for Balanced Energy Consumption in Wireless Body Area Networks Muhammad Moid Sahndhu, Nadeem Javaid, *, Muhammad Imran, Mohsen Guizani 3, Zahoor Ali Khan 4, Umar Qasim 5 COMSATS Institute of Information Technology, Islamabad, Pakistan King Saud University, Riyadh, Saudi Arabia 3 Qatar University, Qatar 4 CIS, Higher Colleges of Technology, Fujairah Campus, UAE 5 University of Alberta, Alberta, Canada nadeemjavaidqau@gmail.com, Abstract Wireless Body Area Networks (WBANs) are getting growing interest because of their suitability for wide range of medical and non-medical applications. These applications demand WBAN to stay functional for a longer time which requires energy-efficient operation. In this paper, we propose a new routing protocol for Balanced Energy Consumption () in WBANs. In, relay nodes are selected based on a cost function. The nodes send their data to their nearest relay nodes to route it to the sink. The nodes closer to the sink send their data directly to it. Furthermore, the nodes send only critical data when their energy becomes less than a specific threshold. In order to distribute the load uniformly, relay nodes are rotated in each round based on a cost function. Simulation results show that achieves 49% increased network lifetime than (On Increasing Network Lifetime) algorithm. Keywords: WBANs, balanced energy consumption, efficiency, network lifetime, throughput. I. INTRODUCTION Due to the advancement in medical healthcare, remote monitoring of patient s vital signs has gained attention these days. The increased cost in medical expenditures has urged technologists to develop economical and reliable solutions for medical health care. It is often difficult for patients to visit the medical center periodically. Furthermore, it is un-feasible for medical specialists to individually visit the large number of patients in their homes. It is evident that critical patients need immediate medical treatment as compared to normal patients. Therefore, remote health monitoring offers best solutions to the above mentioned problems. A Wireless Body Area Network (WBAN), which is a sub-field of Wireless Sensor Networks (WSNs), presents a solution to the problems related to health care. It consists of miniaturized, low power and intelligent sensor nodes deployed on, in or around the human body to monitor different body functions and the surrounding environment. In this paper, we /5/$3.00 c 05 IEEE use the term sensor, node and sensor node interchangeably. Each node has enough capability to process and forward information to the base station for diagnosis and prescription. A WBAN offers two main advantages for patients monitoring. The first benefit is location independent monitoring and the second is mobility of patients without any interruption. There are a number of applications of WBANs ranging from biomedical to soldiers and players monitoring in the field. The nodes placed on the human body sense vital parameters like pulse rate, body temperature, glucose level, electromyography (EMG), electrocardiography (ECG), electroencephalography (EEG), etc. Later on, this information is sent to the medical centre for diagnosis and treatment. Fig. shows the architecture of WBAN. In this paper, we propose a new routing protocol for efficient monitoring of patients in WBANs. utilizes intermediate nodes which collect the data of far distant nodes, aggregate it and route it to the sink. The proposed protocol routes only critical data when energy of nodes becomes less than a threshold. We define critical data as the abnormal data that demands immediate medical aid and treatment of the patient. The rest of the paper is organized as follows. Section II consists of related work, while section III discusses the motivation. Energy consumption analysis is shown in section IV. The proposed protocol is narrated in section V. Simulation results are discussed in section VI and finally, section VII gives the conclusion alongwith future work. II. RELATED WORK There are a number of routing protocols discussed in the literature. Authors emphasize on devising a mechanism to enhance the network lifetime. As nodes in WBANs (and in WSNs) have limited energy resources, therefore, it is necessary to utilize it for increased time. Authors in [] present fair data collection scheme for WSNs. As nodes are located at

2 Physician Medical server Ambulance Node Sink PDA PDA PDA Fig.. Architecture of WBAN different distances from the sink, so fair data distribution leads to extended network lifetime. In a WBAN, nodes are located close to each other and within the communication range of each other. Therefore, efficient Media Access Control (MAC) layer protocols are employed to avoid collision. Y. Zhang et al. [] proposed a priority-guaranteed MAC protocol for WABNs. In this protocol, control channels are separated from data channels. Priority-specific control channels are used for lifecritical applications. Authors also presented wakeup trigger mode to facilitate priority traffic. Authors in [3] proposed PLA-MAC for priority-based traffic in WBANs. The sensed data is bifurcated according to their Quality-of-Service (QoS) (i.e., delay, reliability and throughput) requirements and is assigned priorities. These priorities determine the transmission schedule of packets. The superframe structure also varies according to the amount of data causing minimum energy consumption. Cooperation at the MAC layer for data transmission of multiple nodes is presented in [4]. Multiple channels are used at the MAC layer to improve the throughput. This scheme supports multi-hop transmissions using cooperation between environmental and WBAN nodes. It ensures successful data delivery from the nodes to the gateway (sink). Due to continuous movements of the human body, the link between the sink and the node may not be connected all the time. The link breakages result in loss of data. P. Ferrand et al. [5] described a cooperative transmission scheme in WBANs to overcome the disconnected links. They used a multi-hop scheme to ensure good connectivity. In their proposed work, some sensors are elected to support the nodes having bad links. This way, data is efficiently routed to the sink. Authors in [6] proposed an obesity control framework using WBANs. They proposed software and hardware architectures for obesity control. In their proposed framework, sensors are placed on the human body. These sensors monitor different vital signs and compare them with the predefined thresholds. If the sensed value exceeds the threshold, the information is sent to a smart phone or a personal computer to allow taking the appropriate action to prevent body harm. Authors in [7] described a thermal-aware routing protocol; TMQoS, for implanted sensors in the human body. Heated sensors (also known as hot spots) can damage human tissues. TMQoS selects alternative paths if hot spots are detected and uses hot spot avoidance mechanism. It selects shortest path from source to destination to meet QoS constraints (delay and reliability). In [8], virtual groups are formed between devices of doctors, nurses and patients for remote health monitoring in WBANs. These groups are formed and modified according to requirements of patients. Authors also develop a new metric called Quality of Health Monitoring, which allows the doctors to provide feedback about the quality of WBAN s data. Authors in [9] placed wearable sensors on the human body and studied the link behaviour in dynamic conditions. They recorded the link quality, packet delivery and Received Signal Strength Indicator (RSSI) values in real-time. They also described the packet delivery and energy efficiency obtained

3 by using dynamic routing and adaptive transmission power schemes, respectively. Authors in [0] estimated the lifetime of Health Monitoring Network (HMN) using probabilistic analysis. Lifetime is defined as the time from the start to the depletion of a node. It is important to estimate the lifetime of the network to replace/recharge the batteries of nodes to continuously monitor the required parameters. Authors in [] used wireless accelerometer sensor to determine the link performance and lost packets for different runners and for different sensor locations. They concluded that sensors placed on the wrist give best results. In [], authors proposed a mechanism for fault detection in sensors placed on the patients in WBANs. The proposed mechanism finds the fault in the body sensors using the collected data. It helps in efficient patient s monitoring, especially in emergency. G. R. Tsouri et al. [3] presented a routing protocol called On Increasing Network Lifetime () in Body Area Networks Using Global Routing with Energy Consumption Balancing. It provided balanced energy consumption to nodes resulting in increased network lifetime. Authors also developed real-time experimental setup to validate the proposed technique. III. MOTIVATION In WBANs, balanced energy consumption of nodes helps to monitor the vital signs of the human body for increased time period. has increased network lifetime due to the balanced energy consumption of nodes. It collects linkcost periodically at the sink, where all routing decisions are performed. In, nodes send the data via routes that have minimum cost. The cost function of is given as: ( E k Cj,k i = RSSI i T + Ei min α j,k ) M () Where, RSSI T is the target RSSI value required to achieve reliable communication and α j,k is the channel attenuation for the link between j and k. Ei k is the accumulated energy of node i at round k and Ei min is the minimum accumulated energy across all nodes. In Eq., M 0 which shows the effect of imbalanced energy consumption. Eq. transforms to conventional cost function when M = 0, which is the power required to transverse a link regardless of the accumulated energy of nodes. However, one deficiency of is that it results in increased energy consumption of nodes in data reception and aggregation. As data is routed through shortest path, so intermediate nodes may be involved in data reception and aggregation. In this work, we propose a routing protocol that selects the route with suitable number of intermediate nodes. As all the nodes are in communication range of each other, so less nodes are burdened with the load of data routing. Moreover, in order to further enhance the network lifetime (for long term health monitoring), we propose reactive routing when the energy of nodes becomes less than a threshold. TABLE I ENERGY PARAMETERS OF TRANSCEIVERS Parameter nrf 40A CC40 Units DC current (TX) ma DC current (RX) ma Supply voltage (min.).9. V E TXelect nj/bit E RXelect nj/bit ε amp.97 7 nj/bit/m n IV. ANALYSIS OF ENERGY CONSUMPTION In WBANs, nodes consume different amount of energy in single-hop and multi-hop communications. Energy consumption in a single-hop communication is given as: E sh = E TX () Where, E TX is the transmission energy which is calculated as: E TX = (ε amp +E elect ) s d (3) Where, ε amp is the energy consumed by the amplifier and E elect is the energy consumed by the electronic circuit. The packet size is denoted by s and d shows the distance between the node and the sink. On the other hand, energy consumption in multi-hop communication is given as: [ E mh = s n E TX +(E DA +E RX ) (n ) ] (4) n In eq. 4, n is the number of hops and E DA is the energy consumed in data aggregation. E RX is the energy consumed in data reception and we assume that E TX = E RX. V. : THE PROPOSED PROTOCOL In this section, we discuss the proposed routing protocol. The detail is given in the following subsections. A. Radio Model A number of radio models are proposed in the literature. We use first order radio model [4] given as: E TX (s,d) = E TXelect (s)+ε amp (s,d) (5) E TX (s,d) = E TXelect.s+ε amp.s.d (6) E RX (s,d) = E RXelect (s) = E RXelect.s (7) In WBANs, the human body contributes attenuation to the radio signals. Therefore, a path loss coefficient parameter n is included in the radio model. The expression for the energy consumption is given as: E TX (s,d) = E TXelect.s+ε amp.s.d n (8) Different types of sensors are available for the monitoring of physiological parameters of human body in WBANs. Table I shows the energy parameters of two transceivers which are widely used in WBAN technology. Table II shows the distances between the nodes and the sink.

4 B. Placement of Nodes In, eight nodes are placed on the human body. All nodes have equal initial energy (i.e. nodes are homogeneous). The sink is placed on the chest of the human body as shown in fig.. C. Start-up Phase TABLE II DISTANCES OF NODES FROM THE SINK Node Distance (m) In this phase, the sink broadcasts a HELLO packet to all the nodes. Each node receives this packet and stores the location of the sink. Then each node broadcasts a packet which contains the ID of a node, its location and the value of the residual energy. In this way, all nodes are updated with the location of neighbouring nodes, position of the sink and possible routes to the sink. Fig. 3 depicts the format of the HELLO packet. Position of sink D. Routing Phase HELLO packet Neighbors information Routes to sink information Fig. 3. Format of the HELLO packet In this phase, nodes select their path to the sink. The nodes closer to the sink send their data directly to the sink. However, the nodes far away from the sink use intermediate (relay) nodes to route the data. The mechanism of the path selection for the proposed protocol is shown in fig.. selects the path with minimum attenuation and more nodes are involved (see fig. ) which results in more energy consumption in the form of data reception and aggregation, so nodes die quickly. On the other hand, the proposed protocol selects a path with suitable number of intermediate nodes and successfully routes the data to the sink. This way, less energy is consumed and nodes stay alive for a long time. The cost function used in the proposed routing scheme is given as: C(i) = R.E(i) Where, C(i) is the cost of node i. In Eq. 9, R.E(i) represents the residual energy of node i. In, a node having minimum (9) cost is selected as a relay node. In this way, balanced energy consumption results in increased network lifetime. There is a trade off between having only critical (emergency) data for long term and normal (continuous) data for small time period. As critical patients need immediate medical treatment, therefore, critical data is routed without any hindrance. In, we implement reactive routing when the energy of nodes decreases below a threshold (τ). E. Scheduling Phase In this phase, the sink assigns Time Division Multiple Access (TDMA) based time slots to all the nodes. All the nodes use the same frequency band and transmit their data in different time slots. The nodes send their data in their scheduled time slots to avoid any collision. F. Data Transmission Phase The initial energy of all nodes is the same (i.e. E o = 0.5 J). The nodes sense the vital parameters of the human body and send data to the sink continuously. However, after the nodes are left with energy less than τ, the proposed protocol uses reactive routing. Therefore, human vital parameters are monitored for long term. VI. EXPERIMENTS AND DISCUSSIONS In order to verify the performance of the proposed protocol, simulations are performed five times and average results are plotted. Table III shows the simulation parameters. We ignore the sensing energy consumed by the nodes in our simulation. Furthermore, we assume that 30% of the data is critical. We study the performance of the proposed protocol in comparison with. The following subsections contain the detail of different performance parameters. TABLE III SIMULATION PARAMETERS Parameter Value Units E RXelect 36. nj/bit E TXelect 6.7 nj/bit ε amp.97 nj/bit/m E DA 5 nj/bit τ 0. J d o 0. m s 4000 bits f.4 GHz E o 0.5 J A. Stability Period and Network Lifetime The stability period is the time from the start of the network till the death of the first node. On the other hand, network lifetime shows the time from the start of the network till the death of the last node. The proposed routing scheme selects relay nodes on the basis of cost value. The node having the minimum cost value is selected as a relay node for data transmission. Therefore, nodes exhibit a uniform energy consumption which increases the network lifetime. Our proposed protocol sends data to

5 Node Sink Fig.. Placement of nodes on the human body in and the sink by consuming less energy. has 49% improved network lifetime than. It shows that the energy of all the nodes is efficiently consumed. Due to efficient energy usage, the proposed protocol achieves increased network lifetime. Fig. 4 shows the comparison of the stability period and the network lifetime. It is evident that the achieves improved stability period and network lifetime. Therefore, has increased the network lifetime at the cost of lower throughput after 545 rounds (see figs. 4 and 5). Packets received at sink x Rounds Fig. 5. Comparison of network throughput Fig. 4. Comparison of stability period and network lifetime B. Network Throughput The throughput is the number of packets successfully received at the sink per unit time. The proposed protocol consumes energy efficiently resulting in longer network lifetime. The nodes are alive for longer time and send more packets that leads to increased throughput. In this work, we use a random uniformed model [4] for packet drop calculation. The status of the communication link can be good or bad depending upon the probability. We assume the probability of 0.7 for the link status to be good. offers increased throughput than as shown in fig. 5. The throughput of the proposed protocol decreases after 545 rounds. It is due to the fact that nodes are left with energy less than τ and only critical data is routed. C. Residual Energy The residual energy of the network in the proposed routing scheme is shown in fig. 6. The intermediate nodes receive the data of their corresponding nodes and route it to the sink. As nodes send critical data to the nearest forwarding nodes, so less energy is consumed and they can stay alive for longer time. Fig. 6 shows that initially and have the same residual energy. However, after 545 rounds the proposed scheme offers better residual energy curve than due to reactive routing strategy. D. Path Loss Path loss is the difference between the transmitted and received power represented in decibels (dbs). The posture of the human body affects the electromagnetic signals. As a result, the path loss shows different behaviours along different body parts. There are different models used to estimate the path loss which is a function of distance and frequency as: PL = PL o +0.n.log 0 ( d d o )+σ s (0) Where, PL o is the path loss at reference distance d o and n is the path loss exponent. The distance between the transmitter

6 Residual energy (J) Rounds Fig. 6. Comparison of residual energy and the receiver is d and σ s is the standard deviation [4]. The path loss at reference distance d o can be expressed as: ( ) 4.π.do PL o = 0.log 0 () λ Here, λ is the wavelength of the electromagnetic waves. In our simulation, we use a fixed frequency (f) of.4 GHz from Industrial, Scientific and Medical (ISM) radio band. We use the values of n and σ s as 3.38 and 4., respectively. Fig. 7 shows the path loss in each round for and. We observe that after 545 rounds the path loss exhibits continuous fluctuations. These fluctuations are due to reactive routing in which data is not sent if it is not critical (i.e. normal data). In this way, there is no path loss in some rounds and the path loss curve goes to zero (see fig. 7). Path loss (db) Rounds Fig. 7. Comparison of path loss The improvement in the percentage provided by as compared to is shown in table IV. TABLE IV IMPROVEMENT IN PERCENTAGE Parameter Improvement (%) in Stability period Network lifetime 49 Network throughput 0.6 Average residual energy 6.35 Average path loss 3.8 VII. CONCLUSION We have proposed a new routing protocol for WBANs which utilizes energy efficiently. Nodes send their data to intermediate nodes which route it to the sink. The nodes closer to the sink send their data directly to it. In, relay nodes are selected dynamically based on a cost function. The nodes send only critical data when their energy becomes less than a specific threshold. Therefore, nodes do not deplete their energy quickly and stay alive for a longer period of time. Simulations show that achieves improved stability period and network lifetime. REFERENCES [] M. A. Hamid, M. M. Alam, M. S. Islam, C. S. Hong, and S. Lee, Fair data collection in wireless sensor networks: analysis and protocol, Annals of Telecommunications, vol. 65, no. 7-8, pp , 00. [] Y. Zhang, and G. Dolmans, Priority-guaranteed MAC protocol for emerging wireless body area networks, Annals of Telecommunications, vol. 66, no. 3-4, pp. 9-4, 0. [3] I. Anjum, N. Alam, M. A. Razzaque, M. M. Hassan, and A. Alamri, Traffic priority and load adaptive MAC protocol for QoS provisioning in body sensor networks, International Journal of Distributed Sensor Networks, vol. 03, Article ID 059, 9 pages, 03. doi:0.55/03/059 [4] Ivanov, Stepan, Dmitri Botvich, and Sasitharan Balasubramaniam, Cooperative wireless sensor environments supporting body area networks, IEEE Transactions on Consumer Electronics, vol. 58, no., pp. 84-9, 0. [5] P. Ferrand, M. Maman, C. Goursaud, J.-M. Gorce, L. Ouvry, Performance evaluation of direct and cooperative transmissions in body area networks, Annals of Telecommunications, vol. 66, no. 3-4, pp. 3-8, 0. [6] N. A. Alrajeh, J. Lloret, and A. Canovas, A Framework for Obesity Control Using a Wireless Body Sensor Network, International Journal of Distributed Sensor Networks, vol. 04, Article ID , 6 pages, 04. doi:0.55/04/ [7] M. M. Monowar, M. M. Hassan, F. Bajaber, M. A. Hamid, and A. Alamri, Thermal-Aware Multiconstrained Intrabody QoS Routing for Wireless Body Area Networks, International Journal of Distributed Sensor Networks, vol. 04, Article ID 6763, 4 pages, 04. doi:0.55/04/6763 [8] S. Ivanov, C. Foley, S. Balasubramaniam, and D. Botvich, Virtual groups for patient WBAN monitoring in medical environments, IEEE Transactions on Biomedical Engineering, vol. 59, no., pp , 0. [9] R. H. Jacobsen, K. Kortermand, Q. Zhang, and T. S. Toftegaard, Understanding Link Behavior of Non-intrusive Wireless Body Sensor Networks, Wireless Personal Communications, vol. 64, no. 3, pp , 0. [0] F. A.-Ntim, and K. E. Newman, Lifetime estimation of wireless body area sensor networks using probabilistic analysis, Wireless Personal Communications, vol. 68, no. 4, pp , 03. [] H. A. Sabti, and D. V. Thiel, Node Position Effect on Link Reliability for Body Centric Wireless Network Running Applications, IEEE Sensors Journal, vol. 4, no. 8, 04. [] Y.-S. Jeong, H.-W. Kim, and J. H. Park, Visual Scheme Monitoring of Sensors for Fault Tolerance on Wireless Body Area Networks with Cloud Service Infrastructure, International Journal of Distributed Sensor Networks, vol. 04, Article ID 5480, 7 pages, 04. doi:0.55/04/5480 [3] G. R. Tsouri, A. Prieto, and N. Argade, On Increasing Network Lifetime in Body Area Networks Using Global Routing with Energy Consumption Balancing, Sensors, vol., no.0, pp , 0. [4] A. Ahmad, N. Javaid, U. Qasim, M. Ishfaq, Z. A. Khan, and T. A. Alghamdi, RE-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks, International Journal of Distributed Sensor Networks, vol. 04, Article ID 46400, 9 pages, 04. doi:0.55/04/46400.