Efficient Cluster based Routing Protocol for Collaborative Body Sensor
Networks
Nadine Boudargham
1
, Jacques Bou Abdo
2
, Jacques Demerjian
3
, Christophe Guyeux
4
and Abdallah Makhoul
4
1
Faculty of Engineering, Notre Dame University, Deir El Kamar, Lebanon
2
Faculty of Natural and Applied Sciences, Notre Dame University, Deir El Kamar, Lebanon
3
LARIFA-EDST, Faculty of Sciences, Lebanese University, Fanar, Lebanon
4
FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comt
´
e, Belfort, France
Keywords:
CBSN, Routing, Delay, Energy Consumption, Packets Dropped.
Abstract:
Collaborative Body Sensor Networks (CBSNs) are collection of Body Sensor Networks that move in a given
area and collaborate, interact and exchange data between each other to identify group activity, perceive events
detected by group of individuals, and monitor the status of single and multiple persons. Even though some
routing algorithms were proposed for Wireless Sensor Networks (WSNs) and Body Sensor Networks (BSNs),
very few studies were found to cover routing in CBSNs. In this paper, we propose a robust cluster based
scheme that increases the routing efficiency through the three steps of the routing process: cluster formation,
cluster head election, and routing operation of data to the Base Station (BS). MATLAB simulations are per-
formed to compare the performance of the proposed algorithm to other existing routing schemes. Results
show that the proposed scheme outperforms others in terms of delay, energy consumption, and packet drop
percentage, and therefore succeeds in addressing CBSN challenges.
1 INTRODUCTION
Collaborative Body Sensor Network (CBSN) is a
network formed of multiple Body Sensor Networks
(BSNs) (Boudargham et al., 2016) moving in the
same area and able to collaborate and synchronize
among each other to reach a common objective. This
intercommunication between BSNs allows the devel-
opment of collaborative applications like interactive
games, social interactions between multiple persons,
as well as group status monitoring such as supervis-
ing rescue teams condition, sports team performance,
and employees status in industries etc., where instead
of single individual monitoring, exchanging data and
cooperative processing between many BSNs is a must
to detect the activity of a group, identify the events
perceived by many persons, and monitor the health of
many individuals at the same time.
Cluster based routing is known to be efficient in
extending WSN and BSN lifetime through spread-
ing the energy consumption among sensor nodes
(Ul Huque et al., 2013). In cluster based routing,
nodes are assembled into clusters (Farhat et al., 2016),
and every cluster has one Cluster Head (CH) that
is elected among the corresponding nodes. Nodes
within the cluster transmit their data to the CH, and
only the CH can forward this data to the sink. The
number of active nodes is therefore reduced, which
decreases the delay and energy consumption of the
network.
Even though many cluster based routing algo-
rithms were proposed for WSNs and BSNs, very few
studies were found to cover routing in CBSNs. For in-
stance, authors in (Tauqir et al., 2013; Watteyne et al.,
2007; Verma and Rai, 2015) propose schemes to route
data between many patients in a hospital, however
these articles do not account for mobility as they con-
sider sensor nodes to be fixed either on the bedside,
or in specific locations of the hospital. On the other
hand, Aminian et al. propose in (Aminian and Naji,
2013) a system capable of monitoring several patients
moving in a hospital; however, authors suggest using
relay nodes placed in predetermined places to route
data sent by the coordinator node of every patient to
the Base Station (BS), which may not be feasible in
all mediums.
94
Boudargham, N., Abdo, J., Demerjian, J., Guyeux, C. and Makhoul, A.
Efficient Cluster based Routing Protocol for Collaborative Body Sensor Networks.
DOI: 10.5220/0007385100940101
In Proceedings of the 8th International Conference on Sensor Networks (SENSORNETS 2019), pages 94-101
ISBN: 978-989-758-355-1
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

In this paper, we study and propose an efficient
cluster based routing scheme that is able to send data
reliably from many BSNs in motion to the BS, with-
out the need to add additional nodes to the network.
The following scenario is considered. The CBSN
is formed of several BSNs moving in a 400 m
2
indoor
area. Every BSN is a person who can be a patient in a
hospital, employee in an industry or a rescue team in
a building, equipped with medical sensors and a co-
ordinator node. The medical sensors capture physio-
logical data from the person’s body and send it to the
corresponding coordinator node who will transmit it
in return to the BS. Every BSN is considered to be
one node in the CBSN; and therefore, in this paper, a
new cluster based routing algorithm of data from the
different nodes in motion to the BS will be proposed
and compared to other existing schemes.
The remainder of the article is organized as fol-
lows: Related work and research contribution are dis-
cussed in Section 2. A new efficient cluster based
routing algorithm is then proposed in Section 3. Sim-
ulation of the suggested scheme is presented in Sec-
tion 4, to end up with the conclusion and future work
in Section 5.
2 RELATED WORK AND
RESEARCH CONTRIBUTION
Many cluster based routing schemes for WSNs and
BSNs are presented in literature. They differ from
each other by the way clusters are formed, the criteria
used to elect the CH of each cluster, and the routing
process for data to reach the BS.
For instance, LEACH protocol (Heinzelman et al.,
2000) elects CHs by random rotation to distribute the
energy consumption among all nodes in the network.
Non- CH nodes then join the nearest CH. However,
since CHs are randomly elected, non-CH nodes might
be located out of the communication range of the
elected CHs, and cannot therefore join a cluster; also,
the number of nodes joining a cluster can be large,
which would quickly exhaust the energy of the corre-
sponding CH. In addition, direct transmission of data
from nodes to the CH and from the CH to the sink is
used, which might not be efficient if the distance be-
tween the nodes and the CH or between the CH and
the sink is large (Boudargham et al., 2018).
Another cluster based scheme is the Improved
LEACH protocol for BSN presented in (Zhang et al.,
2015). Improved LEACH is an enhancement over
the LEACH protocol since CHs are not only elected
based on a certain probability, but also based on the
residual energy of the nodes and the nodes type; i.e.,
less important nodes with the highest residual energy
are chosen to be the CHs in a round. The prob-
lem with Improved LEACH is that even though it
elects CHs based on significant parameters, it ignores
other important criteria such as the nodes’ distance
to the sink, their mobility, and their communication
range. Also, like in LEACH, the number of cluster
members might be high, and direct transmission of
data is adopted which decreases the routing efficiency
(Boudargham et al., 2018).
In LEACH-TLCH (Fu et al., 2013), a secondary
CH is elected and used in case the energy of the pri-
mary CH decreases below the average energy of all
nodes in the network, or the distance between the pri-
mary CH and the BS increases above the average dis-
tance between the nodes and the BS. Non-CH nodes
send their data to the primary CH either directly, or
through the secondary CH that is the node with the
highest residual energy in the cluster. This algorithm
takes into account the energy of nodes in the election
of the secondary CH, but other important parameters
are disregarded. for instance if the distance between
the secondary CH and primary CH is large, or if the
connectivity between them is limited, the energy of
the secondary CH will drain very quickly and data
transmission will not be reliable.
Authors in (Arioua et al., 2016) propose a Multi-
hop Cluster Routing approach for WSN. This scheme
combines both LEACH and Minimum Transmission
Energy (MTE) protocols. In this scheme, CHs are
elected by random rotation, however unlike LEACH
where cluster members send their data via direct
transmission to the elected CH, data is sent indirectly
via multi-hop route from the nodes to the CH based
on the shortest path, in order to further minimize the
energy consumption of nodes. Even though this al-
gorithm succeeds in prolonging the network lifetime
through multi-hop routing, CHs are elected via ran-
dom rotation; inappropriate CHs might therefore be
selected, which would decrease the routing efficiency.
Authors in (Watteyne et al., 2007) propose the
Anybody protocol, in which nodes with the highest
density are elected as CHs, where the density is com-
puted as the ratio of the number of links to the number
of nodes within two-hop neighborhood. The data is
then sent from the cluster members to the elected CH
via a multi-hop intra-cluster path, and from the CH to
the sink through a multi-hop inter-cluster route. Since
the CH election is based on highest density, this algo-
rithm may not be efficient when the number of cluster
members is high, specially that other important crite-
ria such as energy of nodes, distance to the sink, etc.
are not considered in the CH election process.
The above discussion shows that none of the clus-
Efficient Cluster based Routing Protocol for Collaborative Body Sensor Networks
95

ter based schemes implemented for WSNs and BSNs
present a complete solution that addresses CBSNs re-
quirements. They either present shortcomings in the
CH election criteria, or in the cluster formation, or in
the routing operation.
Therefore in this paper, a robust cluster based
routing scheme is proposed for CBSN to provide bet-
ter QoS and reliable transmission of data. The main
aim of this algorithm is to increase the routing effi-
ciency in CBSN by decreasing the energy consump-
tion and delay through every step of the routing pro-
cess: the clusters formation, the CHs election, and the
routing operation of data to the sink.
3 PROPOSED ROUTING
ALGORITHM
As stated earlier, the CBSN is formed of many BSNs,
and in the proposed algorithm, every BSN (one per-
son) is represented by one node. Therefore in the fol-
lowing, a routing scheme of data from the coordinator
node of every BSN to the BS is suggested.
The proposed algorithm consists of three steps:
1. Clusters formation
2. Cluster head election
3. Routing operation: Intra and Inter cluster routing.
3.1 Energy Model
The first order energy model, was used in the simu-
lations. This model is widely adopted in many WSN
and BSN studies (Tauqir et al., 2013; Verma and Rai,
2015; Nadeem et al., 2013; Javaid et al., 2013; Sah-
ndhu et al., 2015; T
¨
umer and G
¨
und
¨
uz, 2010; Liaqat
et al., 2016). The corresponding transmitter and re-
ceiver energy are as follows:
E
T X
(L, d) = E
T x−elec
· L +ε
mp
· L ·d
n
(1)
E
RX
(L) = E
Rx−elec
· L (2)
E
T x−elec
and E
Rx−elec
represent the transmitter and
the receiver electronic circuits’ energy consumption
respectively, and ε
amp
represents the the transmit am-
plifier’s energy consumption (Sahndhu et al., 2015).
These values depend on the type of the transceiver
used. We consider using Nordic nRF24L01 2.4
GHz transceivers that are frequently used in BSNs
(Nadeem et al., 2013; Javaid et al., 2015). L indicates
the packet size, and n represents the average Path Loss
(PL) exponent of the network. Since the value of the
Path Loss (PL) exponent in indoor locations ranges
between 1.4 and 6 depending on the present obstruc-
tions (Perez-Vega et al., 1997), it is set to an average
of 3.5 to emulate an indoor environment with obsta-
cles causing diffraction and scattering of the signal.
3.2 Clusters Formation
To form clusters, we consider that the BS is located at
the center of MxM sensing area where persons form-
ing the CBSN are distributed. This sensing area will
be divided into a fixed number of clusters based on
the optimal number of clusters formula presented in
Equation (3). This formula is chosen since it evalu-
ates the best number of clusters that leads to minimum
total energy consumption in the network which is our
target (Amini et al., 2012).
k
optimal
=
s
N
s
· ε
f s
· A
2π(ε
mp
· d
n
toBS
− E
Rx−elec
)
(3)
where:
k
optimal
: Optimal number of cluster.
N
s
: Number of nodes distributed in the sensing area.
ε
f s
: Energy of amplifier in free space computed for n=2.
A : MxM sensing area.
ε
mp
: Energy of amplifier in multi-path fading.
d
toBS
: Average distance between nodes and BS.
n : Average PL exponent of the network.
Since CBSNs are sizeable networks and nodes in
CBSN are randomly distributed, nodes’ density can
be high in some parts of the sensing area. In this
case, the number of nodes joining a cluster can be
very large, which would quickly drain the CH energy.
Therefore to reduce the energy consumption in the
network, the BS distributes nodes evenly among the
K
opt
clusters. It limits the number of nodes in every
cluster to N
c
obtained through dividing the total num-
ber of nodes (N
s
) by the optimal number of clusters
(K
opt
). Every N
c
nodes will be therefore grouped in
one cluster having one CH.
Also, in order to reduce the computation over-
head, re-cluster formation only occurs when nodes
move outside their assigned cluster. Re-clustering
computation is therefore avoided as long as the nodes
remain in their assigned clusters even when they are
in motion.
SENSORNETS 2019 - 8th International Conference on Sensor Networks
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3.3 Cluster Head Election
To guarantee routing efficiency, it is essential to elect
the appropriate CH of every cluster. In the proposed
scheme, the CH is elected based on the following pa-
rameters:
• Distance between the nodes and the BS;
• Energy of nodes;
• Mobility of nodes;
• Transmission Scope (TS);
where TS of every node is defined as:
T S
nodex
=
1
PL exponent of node x
. (4)
TS reflects the connectivity and coverage strength
of a node. It is computed as the reverse of the PL ex-
ponent of every node since the PL parameter encloses
all the types of losses in the network: free-space
loss, reflection, absorption, refraction, and diffraction
losses. It also depends on the environment type (in-
door or outdoor, urban or rural, etc.) and the medium
of propagation (dry or humid air), along with the dis-
tance from the node to the BS.
Since nodes are mobile, the TS value of nodes is
not fix. It is dynamic and changes depending on the
current location of every node. Equation (4) suggests
that higher TS value is achieved for lower PL expo-
nent based on the node’s location.
For every node, the Selection Score (SS) of be-
coming CH is computed using the following formula:
SS
x
=
E
x
· T S
x
d
toBS
· M
x
, (5)
where:
SS
x
: Selection Score of node x to become a CH.
E
x
: Residual energy of node x.
T S
x
: Transmission Scope of node x as per Equation (4).
d
toBS
: Distance from the transmitting node to the BS.
M
x
: Mobility factor of node x.
The mobility factor of a node is computed based
on the relative direction of node mobility. In gen-
eral, mobility is either considered positive or negative.
Positive mobility implies that nodes are moving closer
to each other, which decreases the total energy con-
sumption of the network, whereas negative mobility
indicates that nodes are moving away from each other,
which increases the total energy consumption. In ev-
ery round, node i in a cluster evaluates the distance
to every node in the same cluster. If the difference of
distance between the current round and the previous
round is negative, then nodes are moving away from
each other, otherwise they are either moving closer or
are stationary relative to each other. The mobility fac-
tor of node i will be then computed as (Kumar et al.,
2010):
M
i
(t) =
Nb. of nodes moving away from i
N
c
(6)
Where N
c
represents the number of nodes in a cluster.
Equation (6) is therefore a measurement of nega-
tive mobility. For instance, if a node moves away for
the rest of the nodes in the cluster, the corresponding
mobility factor increases, and if it moves closer or re-
mains stationary with respect to the other nodes, then
its mobility factor decreases. Nodes with low mobil-
ity factor should therefore be selected as CHs since
they lead to low energy consumption.
Therefore, and as per Equation (5), in every clus-
ter, the node with the highest energy and the TS, along
with the lowest mobility factor and the shortest dis-
tance to the BS, will have the highest Selection Score
(SS), will be therefore elected as CH.
3.4 Routing Operation
The routing operation encloses both intra- and inter-
cluster routing. Intra-cluster routing refers to the pro-
cess of transmitting data inside every cluster, i.e., the
way to transmit data from the nodes belonging to a
cluster to the elected CH. And inter-cluster routing is
the process of delivering data to the final destination
(the BS) from the different CHs in the network.
In the proposed algorithm, multi-hop flat model is
used for both intra- and inter-cluster routing since it
increases energy efficiency compared to direct routing
models (Boudargham et al., 2018). In intra-cluster
routing, a Cost Function (CF) of every node inside a
cluster is computed as:
CF
x
=
d
toCH
E
x
· T S
x
(7)
where:
d
toCH
: Distance from node x, member of a cluster,
to the elected CH of that cluster.
E
x
: Residual energy of node x.
T S
x
: Transmission Scope of node x based on its
location.
Efficient Cluster based Routing Protocol for Collaborative Body Sensor Networks
97

Figure 1: Proposed Algorithm Flow Chart.
Equation (7) implies that the node that is closest
to the cluster elected CH, and possessing the highest
residual energy and TS, is selected as a forwarder, and
neighboring nodes send their data to this elected node.
The CF computation aims therefore to optimize the
routing operation by considering the dynamic envi-
ronment and restricted resources in CBSN in the sec-
tion of the forwarder.
Likewise, for inter-cluster routing, the CF of every
CH is evaluated, and the CH with the lowest CF is
chosen as the forwarder of data to the BS:
CF
CH
=
d
toBS
E
CH
· T S
CH
(8)
The flow chart and the illustration of the proposed
algorithm are presented in Fig. 1 and Fig. 2 respec-
tively.
4 SIMULATION OF THE
PROPOSED SCHEME
4.1 Simulation Parameters
To assess the performance of the proposed algorithm,
simulations of the delay, the energy consumption, and
the percentage of dropped packets were performed
using MATLAB R2014b. The suggested scheme is
compared to four algorithms:
Figure 2: Proposed Algorithm Illustration.
1. The LEACH protocol (Heinzelman et al., 2000).
LEACH is chosen since it is one of the most fa-
mous cluster based routing schemes, and was im-
plemented for many types of networks like BSN,
WSN, and mobile networks.
2. The Improved LEACH (Zhang et al., 2015). This
algorithm is chosen since it is a recent scheme
used in BSN.
3. LEACH-TLCH (Fu et al., 2013). This algorithm
is chosen for being recent and widely used in mo-
bile networks.
4. Multi-hop Cluster Routing (Arioua et al., 2016).
This algorithm is chosen since it is a recent algo-
rithm using multi-hop intra-cluster routing.
Simulation parameters are summarized in Table 1.
Table 1: Simulation Parameters.
Simulation Parameter Value
Area Indoor 400 m
2
Number of Nodes (N
s
) 50
Nodes Status Mobile
E
T x−elec
16.7 nJ/bit
E
Rx−elec
36.1 nJ/bit
ε
amp
1.97 nJ/bit
ε
f s
10.9 nJ/bit
PL Exponent 1.4 - 6
Average PL Exponent (n) 3.5
Packet Size 4000 bits
Clusters Density N
c
N
s
/ K
opt
Mobility Model Random Way Point
ε
f s
represents the energy consumption of the
SENSORNETS 2019 - 8th International Conference on Sensor Networks
98

transmit amplifier in free space. It is obtained by com-
puting ε
mp
in Equation (1) for n=2 and by using the
actual power consumption of the Nordic transceiver
as found in the datasheet (Semiconductor, 2007).
As explained earlier, the number of nodes inside ev-
ery cluster is obtained by finding the ratio of the total
number of nodes (N
s
) to the optimal number of clus-
ters (K
opt
). For instance, if K
opt
is found to be 5, then
every cluster will be formed of 10 nodes for 50 total
number of nodes.
In order to make simulations closest to reality, the
value of the PL exponent used to compute the TS of
every node is variable, depending of the node’s loca-
tion. It ranges between 1.4 and 6 for indoor sites as
per Table 2 (Perez-Vega et al., 1997).
Table 2: PL Exponent Values.
PL Exponent Propagation Medium
1.4-1.9 Wave Guidance
2 Free Space Loss (FSL)
3 FSL+multipath
4 Non-LOS, diffraction, scattering
4-6
Shadowing and complete obstruction
(Obstacles and walls)
4.2 Results and Discussion
To assess the performance of the proposed scheme,
the total delay induced by the proposed algorithm,
LEACH, Improved LEACH, LEACH-TLCH and
Multi-hop clustering is presented in Fig. 3. Re-
sults show that LEACH induces the highest delay
among the other protocols, since it elects CHs ran-
domly without accounting for important parameters
that guarantee correct CH election. Also LEACH
use direct intra- and inter-cluster transmission of data
which would induce higher delays due to transmis-
sion of packets over longer distances and through
many obstacles. Improved LEACH performs better
than LEACH since it considers the residual energy
of nodes to elect CHs. However, but it underper-
forms other protocols since it doesn’t consider other
important criteria and follows direct intra- and inter-
cluster routing operation. Multi-hop Clustering proto-
col induces lower delays than LEACH and Improved
LEACH since it adopts intra-cluster multi-hop rout-
ing; however it underperforms LEACH-TLCH and
the proposed scheme since it elects CHs by random
rotation which might lead to incorrect CHs selection.
LEACH-TLCH outperforms the other protocols since
it considers both the distance to the BS and the resid-
ual energy of nodes in the routing process and fol-
lows two-hop intra-cluster routing operation; how-
ever, this protocol induces higher delay than the pro-
posed scheme since it doesn’t account for the medium
condition surrounding the nodes, nor their mobility in
the CH election. Also, the algorithm chooses the sec-
ondary CH based on its residual energy only, ignoring
other important parameters like distance to CH and
transmission scope, which increases the transmission
delay. The proposed algorithm outperforms all the
other schemes since it works on minimizing the net-
work delay throughout the three steps of the routing
process: It divides the the network into optimal num-
ber of clusters which would decrease communication
overheads, leading to less delay. It also ensures the
election of appropriate CHs through taking into ac-
count the mobility and the transmission scope of the
nodes, in addition to the node’s energy and the dis-
tance to the BS. This is in addition to adopting multi-
hop intra- and inter-cluster routing and choosing the
appropriate forwarder based on nodes distance, resid-
ual energy and transmission scope. This guarantees
reliable transmission of data and therefore reduces the
delay induced by re-transmission of packets or trans-
mission of data through long paths.
Figure 3: Delay Performance of Different Routing
Schemes.
Fig. 4 and Fig. 5 illustrate the energy con-
sumed by the nodes in different rounds and the to-
tal percentage of dropped packets for the five com-
pared schemes. Both figures show that the proposed
scheme performs better than the other algorithms. For
instance, the LEACH protocol consumes the highest
energy since CHs are elected randomly, thus much
more energy is needed to transmit data to the BS.
Also, large size clusters can be formed in LEACH,
which will quickly drain the energy of the CH and in-
crease the total energy consumption of the network,
and will lead to high dropped packets percentage as
shown in Fig. 5. Improved LEACH performs better
Efficient Cluster based Routing Protocol for Collaborative Body Sensor Networks
99

Figure 4: Energy Performance of Different Routing
Schemes.
Figure 5: Percentage of Dropped Packets.
than LEACH protocol, since it accounts for the en-
ergy of nodes in the CH election, which would bet-
ter distribute the energy consumption between nodes
and lead to lower packet drop percentage. As for
Multi-hop Clustering scheme, it consumes less energy
than LEACH and Improved LEACH due to its multi-
hop inta-cluster routing operation that will guarantee
transmission of data through shorter paths. LEACH-
TLCH performs better than the above protocols since
it considers both the energy of nodes and the dis-
tance to the base station in the selection of the CHs,
and adopts two-hop intra-cluster routing, which de-
creases the network overall energy consumption lead-
ing to less percentage of dropped packets. The pro-
posed scheme outperforms all the compared scheme
in terms of energy and packet drop rate since it works
on saving energy throughout the cluster formation,
CH election, and routing operation. Dividing the net-
work into optimal number of clusters leads to mini-
mum energy consumption, and limiting the number
of nodes within clusters avoids the formation of large
clusters that will quickly drain the energy of CHs;
electing appropriate CHs fairly distribute the energy
between nodes, and following muti-hop routing op-
eration within every cluster and between clusters en-
sures reliable transmission of data to the BS, mini-
mizing therefore the re-transmission of packets which
will further decrease the network’s energy consump-
tion and packets’ drop rate.
The obtained results prove therefore that the pro-
posed algorithm succeeds in providing efficient rout-
ing and reliable transmission in CBSN.
5 CONCLUSION AND FUTURE
WORK
In this paper, a new robust and efficient cluster based
routing algorithm is proposed to guarantee fast trans-
mission of data while maintaining high energy effi-
ciency. The proposed scheme works on increasing the
delay and energy efficiency through the three routing
phases: cluster formation, CH election and routing
operation. The suggested protocol was compared to
many other existing schemes. Results showed that it
induces less delay and energy consumption than the
other algorithms and leads to fewer packets drop, and
thus succeeds in helping CBSNs to face many chal-
lenges such as mobility, limited resources, and cov-
erage range. Future work includes testing the perfor-
mance of multi-level cluster based scheme rather than
flat model in inter-cluster routing for large number of
nodes, as well as testing the proposed scheme under
different scenarios such as underwater and hostile en-
vironments.
ACKNOWLEDGEMENTS
This work has been supported by the EIPHI Gradu-
ate School (contract ”ANR-17-EURE-0002”), and by
the Lebanese University Research Program (Number:
4/6132).
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