1. Wireless Pers Commun (2015) 80:1063–1078
DOI 10.1007/s11277-014-2071-x
A Relay Based Routing Protocol for Wireless In-Body
Sensor 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 current
research. In this paper, we present a relay based routing protocol for in vivo wireless body
area sensor networks. Firstly, we decrease the communication distance of in-body sensors
to its minimum value. Secondly, we do not allow multi-hopping of data among in-body
sensors. So, in our proposed protocol, relays along with one coordinator are deployed on the
clothes of a patient. As placed on the patients clothes, relays could easily be recharged and
replaced. In-body sensors send the sensed data to nearby relay, which forwards the data to
the coordinator and finally to the end station. Moreover, the proposed protocol is provided
with linear programming based mathematical models for network lifetime maximization and
end-to-end-delay minimization. Simulation results show that our proposed protocol performs
better 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 wireless
sensor networks (WSNs) are becoming more popular day by day [1–3]. Wireless body area
sensor networks (WBSNs) are special purpose WSNs, designed to operate autonomously
connected diverse medical sensors and appliances located inside or outside the human body.
Moreover, WBSNs provide flexibility and cost benefits to both health-care providers and
patients [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 during
the 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. Alghamdi
COMSATS Institute of Information Technology, Islamabad, Pakistan
e-mail: nadeemjavaid@comsats.edu.pk
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2. 1064 N. Javaid et al.
era [5]. As WBASNs are specifically limited to human body where few nodes are deployed
as per deterministic manner. Thus, making data loss more significant as compared to terres-
trial WSNs, where sensor nodes gather redundant information. Therefore, each sensor node
should provide accurate information, because every information is critical in WBSNs like
Electro-Cardio-Gram (ECG) [6].
There are several constraints in WBSNs. One of the major constraints is the limited battery
power 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. For
wearable WBSNs, different routing protocols have been designed to deal with two types
of data demands: on-demand and continuous. On demand data is provided on user request
whereas continuous data is provided regularly to the end station. For example, M-ATTEMPT
routing 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 proposed
protocol is to save the energy of in-body sensors in such a way that the network lifetime is
prolonged. We save energy consumption of in-body sensors by reducing the communication
distance. So, the idea is to deploy relays on the patients’ clothes. This type of deployment
minimizes the overall length of the transmission path such that all in-body sensors directly
communicate with the relays. We also save the energy of in-body sensors in terms of data
processing 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 lifetime
maximization and E2ED minimization modeling. Simulation results show that our proposed
protocol performs better as compared to the other selected protocols.
Rest of the paper is organized as follows. Section 2 deals with related work. Section 3
provides routing challenges and design issues in WBSNs. Section 4 describes motivation
for 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, references
are 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 monitor
the glucose level, gastrointestinal disorder, etc.
Authors in [10] propose augmented efficiency for global routing in WBSNs. Augmented
efficiency is a new link cost which is designed for balanced energy consumption across
the network. This causes a substantial increase in the network lifetime with minimal per
bit energy consumption. Balanced energy consumption means all sensors equally consume
energy and as minimum as possible.
Authors in [11] propose a new cross layer communication protocol for WBSNs called
Cascading Information retrieval by Controlling Access with Distributed slot Assignment
(CICADA). CICADA is a less energy consuming protocol designed for multi-hop and mobile
WBSNs.Moreover,thisprotocolformsanetworktreeinadistributivemanner.Thetreeislater
on used to guarantee collision free access to the medium and to route data to the end station.
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3. A Relay Based Routing Protocol 1065
Authors in [12] propose opportunistic store and forward packet routing protocol for
WBSNs with frequent postural partitioning. A model for WBSNs has been presented for
experimentally qualifying on-body topology disjunctions in the presence of ultra short range
radio links, unpredictable RF attenuation and human postural mobility.
Authors in [13] introduce a new data-centric routing model to maximize energy efficiency
by 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 less
per bit transmission and reception energies.
Abebneh et al. [14] propose a routing protocol for WBSNs known as Energy-Balanced
Rate Assignment and Routing protocol (EBRAR). EBRAR is an energy efficient routing
protocol in which routing is based on the residual energy of the sensors. Therefore, instead
of one fixed path, data is intelligently sent via different routes which balances the load on
sensors.
Authors in [15] present a ZigBee based routing protocol for patient monitoring. As, the
existing protocols use either broadcast or multicast transmissions for reliable communication,
thereby the end to end transmission delay and network traffic increase. To cater for this
problem, any-cast routing protocol is presented for patients’ vital sign(s) monitoring. To
reduce the network latency, the protocol chooses the nearest receiver to the patient. This
type of wireless network enables fall detection, indoor positioning and ECG monitoring of
patient(s). Whenever, a fall is detected, the network reports exact position of the patient to
the hospital crew. The scheme discussed in this paper can be seamlessly integrated with the
next generation technologies.
Authors in [16] use a localized multihop routing technique in WBSNs. This protocol
ensures homogeneous energy dissipation rate for all the sensors in the network through a multi
objective Lexicographic Optimization-based geographic forwarding. Moreover, the proposed
protocol facilitates the system with customized QoS achievement. Here, it is important to
note 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 improving
energy efficiency of the network. This hybrid approach is basically combination of the two
widely used communication modes; single-hop and multi-hop. The leading one is used for the
transmission of emergency data to sink, whereas the lagging one is used for the transmission
of normal data to sink. This protocol is also supported by path loss analysis and linear
programming 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 computational
power, 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, present
some additional challenges which are described below,
– Wireless link: In WBSNs, interference due to obstacles severely degrades the signal to
noise ratio at the receiver end. The unstable nature of link leads to path instability which
causes higher delay in propagating the data to the destination. Thus, reliable and robust
wireless communication link(s) is(are) needed while proposing a protocol.
– Mobility of sensor: Human mobility is a natural phenomena. The position of the sensors
attached 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 deal
with 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 heterogeneous
sensors, carrying variable types of information. The method of reporting such information
can 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 the
human body and during communication phase is hazardous for the body tissues. So, a
protocol with low complexities and less memory is required.
– Bandwidth: As typical WBSNs have limited bandwidth. So, the protocol and network
should be designed in such a way that the bandwidth utilization is kept at maximum
achievable efficiency level.
– Network topology: Sensors’ deployment in WBSNs depends on its usage. The decision to
place a specific sensor at a specific position is of prime significance. It is to be considered
that whether the sensors should be placed according to their data rates, energy levels or
according to parameters they are sensing. Network topology must be smart enough to
ensure maximum possible efficiency in terms of energy consumption of the sensors.
– Network lifetime: WBSNs are typically composed of low-powered in-body sensors which
are required to work as long as possible. Thus, energy efficiency is the key requirement
for such networks to maximize the network lifetime. Routing protocols should select the
minimum 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 efficient
as 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 energy
as 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 established
links among the sensors and coordinator.
In view of the above discussion, we deploy relays on the clothes of patient to ensure reliable
network connectivity. The in-body sensors communicate with relays, which are responsible to
forward the gathered data to the coordinator. Thus, minimization of communication distance
results in extended network lifetime. Moreover, any dynamic change in the position of patient
does not affect the protocol operation because the distance of sensors to their respective relays
remains the same.
5 The Proposed Protocol
Once the sensors are surgically implanted inside the human body, it is difficult to frequently
replace or recharge them. Thus, in order to maximize the lifetime of in-body sensors, we
introduce relays based solution. Detailed description of our proposed protocol is as follows.
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5. A Relay Based Routing Protocol 1067
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 human
body to capture the electrical signals related to electrocardiogram, electroencephalogram
and electromyogram. Hence, the information about the patient’s physical condition is easily
conveyed to the medical officer. We use deterministic approach; in-body sensors are deployed
according to the information they are capturing. Distances between the sensors are labeled
in Fig. 2. These sensors are assumed to deliver correct bio-feedback of the patient to the
physician.
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-body
sensors. Introduction of relays reduces the communication distance of in-body sensors, and
without 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, it
is clear that the network lifetime increases as we increase the number of relays. As stated
earlier that none of the two in-body sensors are allowed to directly communicate either with
each other or with the coordinator. Thus, whenever the number of relays are increased, the
communication distance decreases. This decrease in communication distance is directly pro-
portional to the energy consumption of in-body sensors which has an inverse relation with
the network lifetime.
According to our proposed work, the relays receive data from in-body sensors and forward
these 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-body
sensors and one relay on the left leg. A line represented by L is passing through the center of
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6. 1068 N. Javaid et al.
Fig. 2 Distance labeled diagram: all the distances are calculated, in meters, for an average human body
1 2 3 4 5 6 7 8
7700
7710
7720
7730
7740
7750
7760
7770
7780
7790
No. of relays
Averagenetworklifetime
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 line
L, dist_sur f _ f ront_shin is the distance of relay placed on the front shin from central line
L, and dist_inside_back_shin is the distance of sensor placed inside the back shin from
the central line L. Relay is placed at an equal distance from both the sensors such that energy
consumption of sensors is balanced. Rest of the relays are positioned according to the same
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7. A Relay Based Routing Protocol 1069
Fig. 4 Network topology
rule. Distances between relays and in-body sensors are labeled by small alphabets as shown
in 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 worthy
to 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 is
shown in Fig. 5.
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8. 1070 N. Javaid et al.
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 models
because these are according to the assumptions of our protocol. If N represents the set
of sensor nodes, then the main objective; network lifetime maximization, is formulated as
follows:
Max T (1)
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9. A Relay Based Routing Protocol 1071
where,
T =
r
tr ∀r ∈ Z+
(2)
and,
tr =
Ei
i li (Ei
s + Ei
rx + Ei
da + Ei
p + Ei
tx + i
ampndn
i j )
∀i ∈ N (3)
Subject to:
Ei ≤ E0 ∀i ∈ N (4.1)
i
li Ei
s + Ei
p + Ei
tx + i
ampndn
i j ≤ qEi ∀i ∈ N (4.2)
i
fi j −
r
f jc ≤ 0 ∀ i ∈ N (4.3)
i
f t
i 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. Equation
2 defines the network lifetime as summation of rounds during which the sensor nodes are
able to perform sensing and routing for the events before their energy ‘Ei ’ is depleted. If the
per bit sensing, reception, aggregation, processing and transmission energies for a node are
represented by Es, Erx , Eda, Ep, and Etx , respectively. Then, Eq. 3 provides details about
the per round energy consumption cost of the network. Where, i represents node, j as its
corresponding relay, l the length of data packet, amp the radio amplifier type and di j as the
distance between i and j. Path loss coefficient ‘n’ on the wireless path, varies from 3.38 to
5.9, for different body parts. Constraint in Eq. 4.1 is the limited energy constraint i.e., each
node is initially equipped with limited energy E0 and with the passage of time the current
energy 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 amplification
to ensure that these events respect their initial levels (where q = 1
T ). Here, it is important
to note that Erx and Eda of Eq. 3 are dropped in Eq. 4.2 because the proposed protocol
considers these events at relays whereas constraint in Eq. 4.2 stands for nodes only. This is
an important step towards the minimization of energy consumption. Constraint in Eq. 4.3
ensures flow conservation when data is routed from node i to coordinator c via relay j ( f is
the flow variable). Violation of Eq. 4.3 leads to increased congestion which causes increased
delay and ultimately to packets being dropped. In order to retransmit the dropped packets,
surplus energy is consumed which leads to decreased network lifetime. Similarly, constraint
in Eq. 4.4 states that flow of data on the link between i and j must respect the physical link
capacity Ci j . Violation of Eq. 4.4 leads to increased packet drop rate causing surplus energy
consumption and thus leading to decreased network lifetime. Constraint in Eq. 4.5 means
that the routing protocol should be capable to minimize the communication distance di j to
its minimum possible value dmin. Our proposed scheme achieves this at the cost of relays.
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10. 1072 N. Javaid et al.
5.5 Minimizing the E2ED
A common problem, while maximizing the network lifetime; increased E2ED, is addressed
here. The problem is formulated as follows:
Min E2E Dsd ∀s ∈ N (5)
where,
E2E Dsd =
Ds + E2E Djd if j = d
Ds if j = d
(6)
and,
Ds = Dtx
s + D
queue
s + D
prc
s + Dcc
s ∀s ∈ N (7)
Subject to:
0 ≤ |N| ≤ x ∀ x ∈ Z+
(8.1)
ntx
snt ≤ nrx
cap ∀ n ∈ Z+
(8.2)
γ arr
i ≤ γ
dep
i ∀ i ∈ N (8.3)
nre−tx
p −→ 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 s
transmits to the intended destination d. Where Eq. 6 provides details; if s and d communicate
via j then the over all delay is the addition of nodal delay at s ‘Ds’ and the E2ED from j
to 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 which
means increased E2ED, so we focus on the minimization of E2ED. Equation 7 defines the
nodal delay as summation of transmission delay Dtx
s , queuing delay D
queue
s , processing delay
D
prc
s , and channel capture delay Dcc
s . Constraint in Eq. 8.1 provides the lower and upper
bounds 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. This
leads to increased Dcc and ultimately increased E2ED. As we know that every information is
delay sensitive in WBSNs, so the number of nodes should be very carefully chosen. In order
to 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 should
not exceed the packet handling capacity at the receiver nrx
cap. Violation of Eq. 8.2 means
congestion at the receiver end, causing the queue size to grow which leads to unbounded
increase 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 leads
to increased Dqueue. If the packets are dropped due to violation of Eqs. 8.1, 8.2, or 8.3 then
these 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 a
solution for such situation(s), constraint in Eq. 8.4 emphasizes on the minimization of packet
retransmissions nre−tx
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
prc
i .
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11. A Relay Based Routing Protocol 1073
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 used
in our simulations are given in Table 1. Each node is initially provided with 0.5J energy as
per constraint in Eq. 4.1. We run the simulation 5 times, take its average, and then calculate
its 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., uniform
for CH-Rotate-BAN and Proposed-BAN, whereas non uniform for single-hop-BAN and
multi-hop-BAN, respectively. The load is uniform for CH-Rotate-BAN because the CHs are
selected after regular intervals, whereas, in proposed-BAN there is negligible variation in the
communication 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 in
the communication distance, whereas, in multi-hop-BAN the intermediate sensors consume
more energy as compared to the distant ones. In CH-Rotate BAN, there is balanced energy
consumption in the network. However, the network dies soon due to greater per round energy
consumption (as per Eq. 3).
As the network operations proceed from one round to another, sensors deplete energy
which ultimately causes their death. Figure 7 shows the stability period and network lifetime
for 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 16.7nJ/bit
ERx 36.1nJ/bit
amp 7.79µJ/bit
l 4,000 bits
0 50 100 150 200
0
1
2
3
4
5
6
7
Number of rounds
RemainingEnergy(J)
Proposed−BAN
Single−Hop−BAN
Multi−Hop−BAN
CH−Rotate−BAN
Fig. 6 Energy consumption comparison
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12. 1074 N. Javaid et al.
0 2000 4000 6000 8000 10000
0
2
4
6
8
10
12
14
Number of rounds
Numberofdeadnodes
Proposed−BAN
Single−Hop−BAN
Multi−Hop−BAN
CH−Rotate−BAN
Fig. 7 Network lifetime comparison
of sensors with a uniform rate. In contrast, non uniform energy consumption causes variation
in the death rate of single-hop-BAN and multi-hop-BAN. Our proposed protocol limits the
energy consumption of the in-body sensors by spreading relays on patients’ clothes. In-body
sensors communicate with their respective relays. The communication distance between
relays and in-body sensors is very small, therefore, the rate of energy consumption decreases
and 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 packets
are dropped. There may be many reasons for this like: large packet size, variation in the
route’s length, nature of the route, violation of constraints in Eqs. 8.1, 8.2, and 8.3, etc. In
simulations, we use a probabilistic approach (i.e., Random Uniformed Model with packet
drop probability of 0.3) to measure the rate at which packets are dropped. Figure 8 shows
variation in packet drop rate. This is due to variation in the residual energy of sensors’ to
transmit same-sized data packets. The proposed-BAN protocol shows the least packet drop
rate in comparison to the other three selected protocols because of minimum distance based
communication of the sensor nodes with the relay nodes. Moreover, as for each sensor node
there 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 other
hand, nodes contend for channel access at the CH which results in somehow increased packet
drop rate. Furthermore, as single-hop-BAN violates constraint in Eq. 4.5 which means more
interference 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 which
these depart the system as well. In contrast, if the arrival rate exceeds departure rate of the
same system then the system is an unstable one where the average waiting time gradually
tends to increase towards infinity. Little’s law tells us that the average number of packets in
a system ‘N’ at any time is equal to the product of average arrival rate ‘γ ’ and the average
per 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 as
proper scheduling of relays make the Proposed-BAN protocol more prone to the average per
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13. A Relay Based Routing Protocol 1075
0 2000 4000 6000 8000 10000
0
1
2
3
4
5
6
7
8
Number of rounds
Numberofdroppedpackets
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 14
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Average arrival rate (packets/sec)
Averageno.ofpacketsinsystem
Proposed−BAN
Single−Hop−BAN
Multi−Hop−BAN
CH−Rotate−BAN
Fig. 9 Packet arrival rate
packet delay. From this perspective, the proposed protocol has less load in comparison to the
other selected protocols. Thus, the Proposed-BAN protocol minimizes the per packet load or
increases 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 in
this paper. As energy consumption is directly related with the communication distance, so
we deploy relays and a coordinator on the clothes of the patient in such a way that the
communication distance between in-body sensors and relays is minimized. Furthermore,
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14. 1076 N. Javaid et al.
relays and coordinator could be frequently recharged. Simulation results justify that the
introduction of relays decreases the communication distance of in-body sensors which yields
network lifetime extension.
In future, we are interested to implement our proposed protocol on real experimental
test bed. Moreover, we are keen to exploit medium access control (MAC) layer for energy
efficiency like [21].
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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, Associate
Director, 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. He
is serving as organizer and TPC member of several conferences. He
has published more than 150+ research articles in reputed interna-
tional journals and conferences, supervised 35 Master students and
supervising/co-supervising 8 Ph.D. students. He is IEEE and IEICE
member.
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 interests
include; addressing fundamental flaws in routing for WSNs, energy
optimization in WSNs, routing and MAC protocol design for Wireless
Body Area Sensor Networks in Healthcare, intra-body communication,
etc.
Yahya Khan did BS Electrical (Telecommunication) Engineering
in 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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16. 1078 N. Javaid et al.
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 his
Ph.D. and MCSc degrees from Faculty of Engineering and Faculty of
Computer Science at Dalhousie University, respectively. He earned his
M.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 engineering
fields. He has multidisciplinary research skills on emerging wireless
technologies. His research interests include but are not limited to the
areas 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 science
from the King Abdulaziz University, Jeddah, Saudi Arabia, in 2003. He
was awarded M.Sc. degree in Distributed Systems and Networks from
the University of Hertfordshire, Hatfield in 2006. In 2011 he received
his 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 administrator
of 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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