Routing Protocol

Wireless Pers Commun (2015) 80:1063–1078DOI 10.1007/s11277-014-2071-x

A Relay Based Routing Protocol for Wireless In-BodySensor Networks

Nadeem Javaid · Ashfaq Ahmad · Yahya Khan ·Zahoor Ali Khan · Turki Ali Alghamdi

Published online: 13 September 2014© Springer Science+Business Media New York 2014

Abstract Efficient energy utilization of in-body sensors is one of the hot areas of currentresearch. In this paper, we present a relay based routing protocol for in vivo wireless bodyarea sensor networks. Firstly, we decrease the communication distance of in-body sensorsto its minimum value. Secondly, we do not allow multi-hopping of data among in-bodysensors. So, in our proposed protocol, relays along with one coordinator are deployed on theclothes of a patient. As placed on the patients clothes, relays could easily be recharged andreplaced. In-body sensors send the sensed data to nearby relay, which forwards the data tothe coordinator and finally to the end station. Moreover, the proposed protocol is providedwith linear programming based mathematical models for network lifetime maximization andend-to-end-delay minimization. Simulation results show that our proposed protocol performsbetter than the selected routing protocols in terms of energy efficiency.

Keywords Body sensor networks · Relay · Routing protocol · Health-care ·End-to-end-delay · Linear programming

1 Introduction

Due to rapid advancements in wireless communication systems, the applications of wirelesssensor networks (WSNs) are becoming more popular day by day [1–3]. Wireless body areasensor networks (WBSNs) are special purpose WSNs, designed to operate autonomouslyconnected diverse medical sensors and appliances located inside or outside the human body.Moreover, WBSNs provide flexibility and cost benefits to both health-care providers andpatients [4].

Since the previous decade, there is significant development in modern health-care moni-toring systems. The communication range of in-body sensors increased significantly duringthe current decade, thanks to the improvement in micro-system technology achieved in this

N. Javaid (B) · A. Ahmad · Y. Khan · Z. A. Khan · T. A. AlghamdiCOMSATS Institute of Information Technology, Islamabad, Pakistane-mail: [email protected]

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era [5]. As WBASNs are specifically limited to human body where few nodes are deployedas per deterministic manner. Thus, making data loss more significant as compared to terres-trial WSNs, where sensor nodes gather redundant information. Therefore, each sensor nodeshould provide accurate information, because every information is critical in WBSNs likeElectro-Cardio-Gram (ECG) [6].

There are several constraints in WBSNs. One of the major constraints is the limited batterypower of the in-body sensors. Transmission energy of sensors is directly related to distance(from source to destination). Routing protocols are used to discover the best possible route.Therefore, energy efficient routing protocols are needed for efficient communication. Forwearable WBSNs, different routing protocols have been designed to deal with two typesof data demands: on-demand and continuous. On demand data is provided on user requestwhereas continuous data is provided regularly to the end station. For example, M-ATTEMPTrouting protocol uses single-hop communication for on-demand data, where, multi-hop com-munication is used for continuous data delivery [7].

In this paper, we present a routing protocol for in-body WBSNs. The goal of our proposedprotocol is to save the energy of in-body sensors in such a way that the network lifetime isprolonged. We save energy consumption of in-body sensors by reducing the communicationdistance. So, the idea is to deploy relays on the patients’ clothes. This type of deploymentminimizes the overall length of the transmission path such that all in-body sensors directlycommunicate with the relays. We also save the energy of in-body sensors in terms of dataprocessing such that none of the two in-body sensors directly communicate with each other.Moreover, we use a linear programming based mathematical approach for network lifetimemaximization and E2ED minimization modeling. Simulation results show that our proposedprotocol performs better as compared to the other selected protocols.

Rest of the paper is organized as follows. Section 2 deals with related work. Section 3provides routing challenges and design issues in WBSNs. Section 4 describes motivationfor the proposed work. Detailed description of the proposed protocol is presented in Sect. 5.Section 6 discusses the simulation results and Sect. 7 concludes the paper. Finally, referencesare provided at the end.

2 Related Work

On large scale, WBSNs are categorized into on-body (wearable) and in-body (implanted)networks [8,9]. An on-body sensor network provides communication between the coordi-nator and wearable sensors and provides remote monitoring of the patient in terms of ECG,temperature, heat beat rate, blood pressure, etc. On the other hand, in an in-body sensor net-work, sensors are implanted inside the patient’s body with an external coordinator to monitorthe glucose level, gastrointestinal disorder, etc.

Authors in [10] propose augmented efficiency for global routing in WBSNs. Augmentedefficiency is a new link cost which is designed for balanced energy consumption acrossthe network. This causes a substantial increase in the network lifetime with minimal perbit energy consumption. Balanced energy consumption means all sensors equally consumeenergy and as minimum as possible.

Authors in [11] propose a new cross layer communication protocol for WBSNs calledCascading Information retrieval by Controlling Access with Distributed slot Assignment(CICADA). CICADA is a less energy consuming protocol designed for multi-hop and mobileWBSNs. Moreover, this protocol forms a network tree in a distributive manner. The tree is lateron used to guarantee collision free access to the medium and to route data to the end station.

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A Relay Based Routing Protocol 1065

Authors in [12] propose opportunistic store and forward packet routing protocol forWBSNs with frequent postural partitioning. A model for WBSNs has been presented forexperimentally qualifying on-body topology disjunctions in the presence of ultra short rangeradio links, unpredictable RF attenuation and human postural mobility.

Authors in [13] introduce a new data-centric routing model to maximize energy efficiencyby taking cooperative nature of signal processing for health care applications into consider-ation. Data sensed by different sensors is compressed into a large packet that consumes lessper bit transmission and reception energies.

Abebneh et al. [14] propose a routing protocol for WBSNs known as Energy-BalancedRate Assignment and Routing protocol (EBRAR). EBRAR is an energy efficient routingprotocol in which routing is based on the residual energy of the sensors. Therefore, insteadof one fixed path, data is intelligently sent via different routes which balances the load onsensors.

Authors in [15] present a ZigBee based routing protocol for patient monitoring. As, theexisting protocols use either broadcast or multicast transmissions for reliable communication,thereby the end to end transmission delay and network traffic increase. To cater for thisproblem, any-cast routing protocol is presented for patients’ vital sign(s) monitoring. Toreduce the network latency, the protocol chooses the nearest receiver to the patient. Thistype of wireless network enables fall detection, indoor positioning and ECG monitoring ofpatient(s). Whenever, a fall is detected, the network reports exact position of the patient tothe hospital crew. The scheme discussed in this paper can be seamlessly integrated with thenext generation technologies.

Authors in [16] use a localized multihop routing technique in WBSNs. This protocolensures homogeneous energy dissipation rate for all the sensors in the network through a multiobjective Lexicographic Optimization-based geographic forwarding. Moreover, the proposedprotocol facilitates the system with customized QoS achievement. Here, it is important tonote that the generated data of sensors is used to categorize the services.

Authors in [17] use a hybrid approach, to overcome the deficiencies in [7], for improvingenergy efficiency of the network. This hybrid approach is basically combination of the twowidely used communication modes; single-hop and multi-hop. The leading one is used for thetransmission of emergency data to sink, whereas the lagging one is used for the transmissionof normal data to sink. This protocol is also supported by path loss analysis and linearprogramming based mathematical model for throughput maximization.

3 Routing Challenges and Design Issues in WBSNs

WSNs and their applications are considered as emerging technologies. However, these net-works still pose challenges to research community which are related to their intrinsic proper-ties such as: low battery power, limited bandwidth, unstable wireless links, low computationalpower, limited memory, etc. Thereby, presenting a major obstacle to the development of reli-able and easy-to-implement routing protocols. WBSNs, as special class of WSNs, presentsome additional challenges which are described below,

– Wireless link: In WBSNs, interference due to obstacles severely degrades the signal tonoise ratio at the receiver end. The unstable nature of link leads to path instability whichcauses higher delay in propagating the data to the destination. Thus, reliable and robustwireless communication link(s) is(are) needed while proposing a protocol.

– Mobility of sensor: Human mobility is a natural phenomena. The position of the sensorsattached with the body, also gets changed. It could result in the isolation of sensors from

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the network or may degrade the link quality. The network should be smart enough to dealwith these issues at an earlier stage before the entire network gets out of the operation.

– Heterogeneity and types of data reporting: WBSNs normally comprise of heterogeneoussensors, carrying variable types of information. The method of reporting such informationcan be of any type such as:

– Time-driven requires continuous monitoring.– Query-driven responds to the queries generated by sensors.– Event-driven reacts to abrupt changes in sensed variables.– Hybrid means the combination of all.

– Computational power and memory: The heat generated by the sensors affixed on thehuman body and during communication phase is hazardous for the body tissues. So, aprotocol with low complexities and less memory is required.

– Bandwidth: As typical WBSNs have limited bandwidth. So, the protocol and networkshould be designed in such a way that the bandwidth utilization is kept at maximumachievable efficiency level.

– Network topology: Sensors’ deployment in WBSNs depends on its usage. The decision toplace a specific sensor at a specific position is of prime significance. It is to be consideredthat whether the sensors should be placed according to their data rates, energy levels oraccording to parameters they are sensing. Network topology must be smart enough toensure maximum possible efficiency in terms of energy consumption of the sensors.

– Network lifetime: WBSNs are typically composed of low-powered in-body sensors whichare required to work as long as possible. Thus, energy efficiency is the key requirementfor such networks to maximize the network lifetime. Routing protocols should select theminimum distant path for reliable data delivery.

4 Motivation

In the current research of WBSNs, several routing protocols have been proposed like single-hop BAN and multi-hop BAN [3]. However, these routing protocols are not as energy efficientas needed. In single-hop BAN, distant sensors to the coordinator die at a faster rate as com-pared to the nearer ones. Whereas, in multi-hop BAN nearer sensors consume more energyas compared to the distant ones. Furthermore, movement of body parts causes relative move-ment of some wearable sensors which in turn causes disconnection of already establishedlinks among the sensors and coordinator.

In view of the above discussion, we deploy relays on the clothes of patient to ensure reliablenetwork connectivity. The in-body sensors communicate with relays, which are responsible toforward the gathered data to the coordinator. Thus, minimization of communication distanceresults in extended network lifetime. Moreover, any dynamic change in the position of patientdoes not affect the protocol operation because the distance of sensors to their respective relaysremains the same.

5 The Proposed Protocol

Once the sensors are surgically implanted inside the human body, it is difficult to frequentlyreplace or recharge them. Thus, in order to maximize the lifetime of in-body sensors, weintroduce relays based solution. Detailed description of our proposed protocol is as follows.

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Fig. 1 In vivo WBSN model

5.1 Network Model

Figure 1 shows the in-body WBSN model in which 14 sensors are placed inside the humanbody to capture the electrical signals related to electrocardiogram, electroencephalogramand electromyogram. Hence, the information about the patient’s physical condition is easilyconveyed to the medical officer. We use deterministic approach; in-body sensors are deployedaccording to the information they are capturing. Distances between the sensors are labeledin Fig. 2. These sensors are assumed to deliver correct bio-feedback of the patient to thephysician.

5.2 Relays

In-body sensors constitute an important class of bio-sensors because of their ability to con-tinuously provide patient’s information (metabolite levels, pulse rate, etc) to the physician.Here, we provide a relay based energy efficient solution for the communication of in-bodysensors. Introduction of relays reduces the communication distance of in-body sensors, andwithout coordinator, none of the two sensors are allowed to communicate with each other.Figure 3 shows the effects of the number of relays on network lifetime. From this figure, itis clear that the network lifetime increases as we increase the number of relays. As statedearlier that none of the two in-body sensors are allowed to directly communicate either witheach other or with the coordinator. Thus, whenever the number of relays are increased, thecommunication distance decreases. This decrease in communication distance is directly pro-portional to the energy consumption of in-body sensors which has an inverse relation withthe network lifetime.

According to our proposed work, the relays receive data from in-body sensors and forwardthese data to the coordinator which is responsible for delivering data to the end station.Deployment of in-body sensors, relays and coordinator in the network are shown in Fig. 4.Left leg is shown separately in the figure for better understanding. There are two in-bodysensors and one relay on the left leg. A line represented by L is passing through the center of

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Fig. 2 Distance labeled diagram: all the distances are calculated, in meters, for an average human body

1 2 3 4 5 6 7 87700

7710

7720

7730

7740

7750

7760

7770

7780

7790

No. of relays

Ave

rage

net

wor

k lif

etim

e

Proposed−BAN

Fig. 3 Effect of the number of relays on network lifetime

the leg. Here, dist_sur f _ f ront_knee is the distance of sensor from knee to the central lineL , dist_sur f _ f ront_shin is the distance of relay placed on the front shin from central lineL , and dist_inside_back_shin is the distance of sensor placed inside the back shin fromthe central line L . Relay is placed at an equal distance from both the sensors such that energyconsumption of sensors is balanced. Rest of the relays are positioned according to the same

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Fig. 4 Network topology

rule. Distances between relays and in-body sensors are labeled by small alphabets as shownin Fig. 4 and their values are given in the table shown in the same figure.

5.3 Protocol Operation: The Communication Flow

The communication flow as per our proposed protocol is as follows:

– Coordinator checks the energy of an in-body sensor.– If the sensor is found dead, coordinator checks for another sensor and continues till an

alive one is found.– If the sensor is found alive, coordinator proceeds by checking the distance of the sensor

with each relay.– After calculating all the distances for a single sensor, coordinator selects the nearest relay.– Coordinator assigns time division multiple access (TDMA) slots to the sensor and its

respective relay.– Sensor transmits the data during its allocated time slot.– Relay receives the transmitted data, and forwards it to the coordinator during the allocated

time slot.– This process continues till the death of all sensors.

Here, we save the energy of sensors by reducing the communication distance. It is worthyto note that the set of relays as well as sensors are assigned unique IDs. Based on these IDs,the coordinator assigns TDMA schedules to sensors as well as relays. The flow diagram isshown in Fig. 5.

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Fig. 5 Flow chart

5.4 Maximizing the Network Lifetime

In this paper, we use the energy consumption model used in [18,19]. we choose these modelsbecause these are according to the assumptions of our protocol. If N represents the setof sensor nodes, then the main objective; network lifetime maximization, is formulated asfollows:

Max T (1)

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where,T =

∑

r

tr ∀r ∈ Z+ (2)

and,

tr = Ei∑i li (Ei

s + Eir x + Ei

da + Eip + Ei

tx + εiampndn

i j )∀i ∈ N (3)

Subject to:

Ei ≤ E0 ∀i ∈ N (4.1)

∑

i

li(

Eis + Ei

p + Eitx + εi

ampndni j

)≤ q Ei ∀i ∈ N (4.2)

∑

i

fi j −∑

r

f jc ≤ 0 ∀ i ∈ N (4.3)

∑

i

f ti j ≤ Ci j ∀i ∈ N (4.4)

di j −→ dmin ∀ i ∈ N (4.5)

The objective function in Eq. 1 aims for network lifetime, T , maximization. Equation2 defines the network lifetime as summation of rounds during which the sensor nodes areable to perform sensing and routing for the events before their energy ‘Ei ’ is depleted. If theper bit sensing, reception, aggregation, processing and transmission energies for a node arerepresented by Es , Er x , Eda , E p , and Etx , respectively. Then, Eq. 3 provides details aboutthe per round energy consumption cost of the network. Where, i represents node, j as itscorresponding relay, l the length of data packet, εamp the radio amplifier type and di j as thedistance between i and j . Path loss coefficient ‘n’ on the wireless path, varies from 3.38 to5.9, for different body parts. Constraint in Eq. 4.1 is the limited energy constraint i.e., eachnode is initially equipped with limited energy E0 and with the passage of time the currentenergy of node Ei steps down (Ei −→ 0). A given node ceases communication, if Ei = 0.Constraint in Eq. 4.2 jointly considers sensing, processing, transmission, and amplificationto ensure that these events respect their initial levels (where q = 1

T ). Here, it is importantto note that Er x and Eda of Eq. 3 are dropped in Eq. 4.2 because the proposed protocolconsiders these events at relays whereas constraint in Eq. 4.2 stands for nodes only. This isan important step towards the minimization of energy consumption. Constraint in Eq. 4.3ensures flow conservation when data is routed from node i to coordinator c via relay j ( f isthe flow variable). Violation of Eq. 4.3 leads to increased congestion which causes increaseddelay and ultimately to packets being dropped. In order to retransmit the dropped packets,surplus energy is consumed which leads to decreased network lifetime. Similarly, constraintin Eq. 4.4 states that flow of data on the link between i and j must respect the physical linkcapacity Ci j . Violation of Eq. 4.4 leads to increased packet drop rate causing surplus energyconsumption and thus leading to decreased network lifetime. Constraint in Eq. 4.5 meansthat the routing protocol should be capable to minimize the communication distance di j toits minimum possible value dmin . Our proposed scheme achieves this at the cost of relays.

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5.5 Minimizing the E2ED

A common problem, while maximizing the network lifetime; increased E2ED, is addressedhere. The problem is formulated as follows:

Min E2E Dsd ∀s ∈ N (5)

where,

E2E Dsd ={

Ds + E2E D jd if j �= dDs if j = d

(6)

and,

Ds = Dtxs + Dqueue

s + D prcs + Dcc

s ∀s ∈ N (7)

Subject to:

0 ≤ |N | ≤ x ∀ x ∈ Z+ (8.1)

ntxsnt ≤ nr x

cap ∀ n ∈ Z+ (8.2)

γ arri ≤ γ

depi ∀ i ∈ N (8.3)

nre−t xp −→ 0 ∀ n ∈ Z+ (8.4)

�i ≤ �th ∀ i ∈ N (8.5)

The objective function in Eq. 5 aims to minimize the E2ED whenever a source node stransmits to the intended destination d . Where Eq. 6 provides details; if s and d communicatevia j then the over all delay is the addition of nodal delay at s ‘Ds’ and the E2ED from jto d , and if s and d directly communicate then the E2ED is equal to the nodal delay at s.As our proposed protocol is designed in a way that the first part of (6) is applicable whichmeans increased E2ED, so we focus on the minimization of E2ED. Equation 7 defines thenodal delay as summation of transmission delay Dtx

s , queuing delay Dqueues , processing delay

D prcs , and channel capture delay Dcc

s . Constraint in Eq. 8.1 provides the lower and upperbounds for |N |; the network size in terms of the number of nodes. If the network is dense(value of |N | is greater) then more number of nodes would contend for channel access. Thisleads to increased Dcc and ultimately increased E2ED. As we know that every information isdelay sensitive in WBSNs, so the number of nodes should be very carefully chosen. In orderto cope with this issue, the proposed protocol considers proper number of nodes (fourteen).Constraint in Eq. 8.2 says that the number of packets sent by the transmitter ntx

snt shouldnot exceed the packet handling capacity at the receiver nr x

cap . Violation of Eq. 8.2 meanscongestion at the receiver end, causing the queue size to grow which leads to unboundedincrease in Dqueue. Similarly, constraint in Eq. 8.3 means that the packet arrival rate γ arr

should not exceed the packet departure rate γ dep at a given node i . Violation of Eq. 8.3 leadsto increased Dqueue. If the packets are dropped due to violation of Eqs. 8.1, 8.2, or 8.3 thenthese packets must be retransmitted because every data is critical in WBSNs. In doing so,surplus energy is consumed (i.e., network lifetime decreases) and E2ED is increased. As asolution for such situation(s), constraint in Eq. 8.4 emphasizes on the minimization of packetretransmissions nre−t x

p . Finally, constraint in Eq. 8.5 bounds the bit level errors at any node

�i to an acceptable level �th , otherwise more erroneous packets leads to increased D prci .

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6 Simulation Results

We evaluate the performance of our proposed protocol by using MATLAB. For simulations,we used fixed sensors’ deployment as shown in Fig. 4. The parameters of radio model usedin our simulations are given in Table 1. Each node is initially provided with 0.5 J energy asper constraint in Eq. 4.1. We run the simulation 5 times, take its average, and then calculateits 90 % confidence interval. Following are the detailed explanations of simulation results.

Figure 6 illustrates two types of behaviours in terms of load distribution i.e., uniformfor CH-Rotate-BAN and Proposed-BAN, whereas non uniform for single-hop-BAN andmulti-hop-BAN, respectively. The load is uniform for CH-Rotate-BAN because the CHs areselected after regular intervals, whereas, in proposed-BAN there is negligible variation in thecommunication distance of each in-body sensor from its respective relay. On the other hand,the non uniform load on sensor nodes for single-hop-BAN is due to high degree of variation inthe communication distance, whereas, in multi-hop-BAN the intermediate sensors consumemore energy as compared to the distant ones. In CH-Rotate BAN, there is balanced energyconsumption in the network. However, the network dies soon due to greater per round energyconsumption (as per Eq. 3).

As the network operations proceed from one round to another, sensors deplete energywhich ultimately causes their death. Figure 7 shows the stability period and network lifetimefor the labelled protocols, proposed-BAN shows maximum stability period and network life-time. In proposed-BAN and CH-Rotate-BAN, uniform energy consumption leads to the death

Table 1 Radio model parameters Parameter Value

ET x 16.7 nJ/bit

ERx 36.1 nJ/bit

εamp 7.79µJ/bit

l 4,000 bits

0 50 100 150 2000

1

2

3

4

5

6

7

Number of rounds

Rem

aini

ng E

nerg

y (J

)

Proposed−BANSingle−Hop−BANMulti−Hop−BANCH−Rotate−BAN

Fig. 6 Energy consumption comparison

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0 2000 4000 6000 8000 100000

2

4

6

8

10

12

14

Number of rounds

Num

ber

of d

ead

node

s


Fig. 7 Network lifetime comparison

of sensors with a uniform rate. In contrast, non uniform energy consumption causes variationin the death rate of single-hop-BAN and multi-hop-BAN. Our proposed protocol limits theenergy consumption of the in-body sensors by spreading relays on patients’ clothes. In-bodysensors communicate with their respective relays. The communication distance betweenrelays and in-body sensors is very small, therefore, the rate of energy consumption decreasesand network lifetime extends (refer constraints in Eqs. 4.2 and 4.5).

Whenever, sensors send sensed data packets to known destination(s), some of the packetsare dropped. There may be many reasons for this like: large packet size, variation in theroute’s length, nature of the route, violation of constraints in Eqs. 8.1, 8.2, and 8.3, etc. Insimulations, we use a probabilistic approach (i.e., Random Uniformed Model with packetdrop probability of 0.3) to measure the rate at which packets are dropped. Figure 8 showsvariation in packet drop rate. This is due to variation in the residual energy of sensors’ totransmit same-sized data packets. The proposed-BAN protocol shows the least packet droprate in comparison to the other three selected protocols because of minimum distance basedcommunication of the sensor nodes with the relay nodes. Moreover, as for each sensor nodethere is an explicit relay, thereby making the channel access more easier (less contention).Thus, agreeing the terms and conditions of constraints in Eqs. 4.1, 8.1, and 8.3. On the otherhand, nodes contend for channel access at the CH which results in somehow increased packetdrop rate. Furthermore, as single-hop-BAN violates constraint in Eq. 4.5 which means moreinterference leading to increased packet drop rate in comparison to the multi-hop-BAN.

Referring a stable system, the rate at which packets arrive the system is the rate at whichthese depart the system as well. In contrast, if the arrival rate exceeds departure rate of thesame system then the system is an unstable one where the average waiting time graduallytends to increase towards infinity. Little’s law tells us that the average number of packets ina system ‘N ’ at any time is equal to the product of average arrival rate ‘γ ’ and the averageper packet delay ‘D’ (N = γ D) [20]. Figure 9 tells us the same story. From this figure,we observe that the our proposed protocol is the most stable one as compared to Single-Hop-BAN, Multi-Hop-BAN and CH-Rotate-BAN protocols. Proper placement as well asproper scheduling of relays make the Proposed-BAN protocol more prone to the average per

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0 2000 4000 6000 8000 100000

1

2

3

4

5

6

7

8

Number of rounds

Num

ber

of d

ropp

ed p

acke

ts

Proposed−BAN

Single−Hop−BAN

Multi−Hop−BAN

CH−Rotate−BAN

Fig. 8 Packets dropped in the network

0 2 4 6 8 10 12 140

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Average arrival rate (packets/sec)

Ave

rage

no.

of p

acke

ts in

sys

tem


Fig. 9 Packet arrival rate

packet delay. From this perspective, the proposed protocol has less load in comparison to theother selected protocols. Thus, the Proposed-BAN protocol minimizes the per packet load orincreases system stability at the cost of relays.

7 Conclusion and Future Work

Network lifetime enhancement of in-body sensors is one of the major challenges in WBSNs.In order to cope with this challenge, we have proposed a relay based routing protocol inthis paper. As energy consumption is directly related with the communication distance, sowe deploy relays and a coordinator on the clothes of the patient in such a way that thecommunication distance between in-body sensors and relays is minimized. Furthermore,

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relays and coordinator could be frequently recharged. Simulation results justify that theintroduction of relays decreases the communication distance of in-body sensors which yieldsnetwork lifetime extension.

In future, we are interested to implement our proposed protocol on real experimentaltest bed. Moreover, we are keen to exploit medium access control (MAC) layer for energyefficiency like [21].

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http://www.odysci.com/article/1010112988313934

http://www.odysci.com/article/1010112988313934


19. Shirazi, G., & Lampe, L. (2011). Lifetime maximization in uwb sensor networks for event detection.IEEE Transactions on Signal Processing, 59(9), 4411–4423.

20. Hayes, J. F., & Babu, T. V. J. G. (2004). Modeling and analysis of telecommunications networks. NewYork: Wiley.

21. Javaid, N., Ahmad, A., Rahim, A., Khan, Z. A., Ishfaq, M., & Qasim, U. (2014). Adaptive mediumaccess control protocol for wireless body area networks. In International Journal of Distributed SensorNetworks, (Vol. 2014, Article ID 254397, p. 10). doi:10.1155/2014/2543972014.

Nadeem Javaid completed Ph.D. from the University of Paris-Est,France in 2010 with the thesis entitled, “Analysis and Design of Rout-ing Link Metrics for Quality Routing in Wireless Multi-hop Net-works”. Recently, he is working as Assistant Professor, AssociateDirector, Modeling and Simulation Lab Incharge, and head of Com-Sense Research Group in Center for Advanced Studies in Telecom-munications (CAST), COMSATS Institute of Information Technology,Islamabad, Pakistan. His research interests include, Ad-hoc Networks,Vehicular Ad-hoc Networks, Body Area Networks, Underwater Wire-less Sensor Networks, Energy Management in Smart Grids, etc. Heis serving as organizer and TPC member of several conferences. Hehas published more than 150+ research articles in reputed interna-tional journals and conferences, supervised 35 Master students andsupervising/co-supervising 8 Ph.D. students. He is IEEE and IEICEmember.

Ashfaq Ahmad is currently enrolled in MS Electrical (Networks)Engineering at COMSATS Institute of Information Technology Islam-abad, Pakistan. Previously, he did BS Electrical (Telecommunication)Engineering from the same university in 2013. His research interestsinclude; addressing fundamental flaws in routing for WSNs, energyoptimization in WSNs, routing and MAC protocol design for WirelessBody Area Sensor Networks in Healthcare, intra-body communication,etc.

Yahya Khan did BS Electrical (Telecommunication) Engineeringin 2013 from COMSATS Institute of Information Technology Islam-abad, Pakistan. His research interests include; wireless sensor net-works, wireless body area networks, etc.

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http://dx.doi.org/10.1155/2014/2543972014


Zahoor Ali Khan is currently working as an Assistant Professor(PT) (Department of Engineering Mathematics and Internetworking) &Postdoctoral Fellow (Internetworking Program) in the Faculty of Engi-neering at Dalhousie University and a part-time Professor of Comput-ing & Information Systems at Saint Mary’s University. He received hisPh.D. and MCSc degrees from Faculty of Engineering and Faculty ofComputer Science at Dalhousie University, respectively. He earned hisM.Sc. (Computer Engineering) degree from UET Texila, MSc (Elec-tronics) degree from Quaid-i-Azam University and B.Sc. from Univer-sity of Peshawar. Dr. Khan has 13+ years of research and development,academia and project management experience in IT and engineeringfields. He has multidisciplinary research skills on emerging wirelesstechnologies. His research interests include but are not limited to theareas of e-Health pervasive wireless applications, theoretical and prac-tical applications of Wireless (Body Area) Sensor Networks, and Soft-ware Defined Networks. He is interested in designing and implement-

ing the algorithms related to energy and Quality of Service aware routing protocols, fault management, secu-rity, privacy, etc. He is (co)-author of a book and 100+ peer-reviewed Journal and Conference papers. Dr.Khan serves as a regular reviewer/organizer of numerous reputed ISI indexed journals, IEEE conferences,and workshops. Dr. Khan is a member of IEEE, IEEE Communication Society and IAENG.

Turki Ali Alghamdi graduated with B.Sc. degree in computer sciencefrom the King Abdulaziz University, Jeddah, Saudi Arabia, in 2003. Hewas awarded M.Sc. degree in Distributed Systems and Networks fromthe University of Hertfordshire, Hatfield in 2006. In 2011 he receivedhis Ph.D. degree in Computer Science from the University of Bradford,Bradford, United Kingdom. In 2003, he joined the Department of Com-puter Science, University of Umm Al-Qura, as an Assistant Teacher.Since August 2011 he has been working for the Department of Com-puter Science as an Assistant Professor. He is a current administratorof recently established smart networks laboratory in the Computer Sci-ence Department. His current research interests include computer net-works and Wireless Sensor Networks.

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