Wireless Sensor Network, 2011, 3, 275-282
doi:10.4236/wsn.2011.38028 Published Online August 2011 (http://www.SciRP.org/journal/wsn)
Performance Analysis of an Enhanced Load Balancing
Scheme for Wireless Sensor Networks
Adeniran Oluwaranti, Dauda Ayanda*
Department of C omput er Science & Engineering, Obafemi Awol owo University, Ile-Ife, Nig er ia
Received June 20, 2011; revised July 22, 2011; accepted July 31, 2011
Abstract
Research interest in sensor networks routing largely considers minimization of energy consumption as a
major performance criterion to provide maximum sensors network lifetime. When considering energy con-
servation, routing protocols should also be designed to achieve fault tolerance in communications. Moreover,
due to dynamic topology and random deployment, incorporating reliability into protocols for WSNs is very
important. Hence, we propose an improved scalable clustering-based load balancing scheme (SCLB) in this
paper. In SCLB scheme, scalability is achieved by dividing the network into overlapping multihop clusters
each with its own cluster head node. Simulation results show that the proposed scheme achieves longer
network lifetime with desirable reliability at the initial state compare with the existing multihop load
balancing approach.
Keywords: Wireless Sensor Networks, Energy Consumption, Scalable Clustering-Based Load Balancing
Scheme, Reliability
1. Introduction
Sensor network research evolves as a result of the ad-
vances in computing and communication that has taken
place in the late 1990s and early 2000s which has resulted
in a new generation of sensor network technology. These
sensor networks represent a significant improvement over
traditional sensors through a number of high-density
technologies, including micro electro-mechanical system
and nanoscale electro-mechanical systems [1-3].
Historical evidence shows that the essential function
of a sensor network is to identify the state of an envi-
ronment using multiple sensors. Other aspects of sensor
network design, such as communication, power, and
computational resources, are evaluated by their ability to
facilitate this function.
Wireless sensor nodes are deeply embedded into the
physical surroundings, gather and process information
such as temperature, humidity, light characteristics,
seismic activities or images and sound samples from the
physical worlds. Networked systems of such sensors
were expected to be used in a variety of applications in-
cluding habitat monitoring, precision agriculture, disaster
recovery operations, health care and supply chain man-
agement [2,4].
Many studies were carried out in WSNs such as en-
ergy efficiency, load balancing and reliability. However,
these design goals are generally orthogonal to each other.
For example, most of the loads balancing schemes are
not robust to high link failure rate [5].
On the other hand, reliable routing in wireless sensor
networks (WSNs) is a challenging issue firstly, because
of the absence of global addressing schemes; secondly,
because of the problem of data source from multiple
paths to single source; and thirdly, because of data re-
dundancy and of energy and computation constraints of
the network. The performance of the existing routing
algorithms, such as shortest path routing algorithm, for
WSNs varies from application to application because of
the diverse demands for different applications for which
they were deployed.
In this study, an enhanced load balancing algorithm
for reliable routing in WSNs is proposed. This algorithm
aims to achieve both load balancing and reliability for
large-scale WSN. The use of load balancing scheme can
be expected to provide significant lifetime benefits:
rather than always using the nod es with the best channel,
traffic is redistributed over a l arger number o f relays.
The paper is organized as follows: Section 1 introduces
the readers to WSN background. Section 2 discusses the

276 A. OLUWARANTI ET AL.
related works on load balancing algorithm. Research
method is discussed in Section 3. The simulation results
and discussion are detailed in Section 4. Section 5 draws
the conclusion and proposes future work.
2. Related Works
Clustering-based routing is an energy efficient routing
model for achieving load balancing as compared with
direct routing and multihop routing protocol in WSNs
but there are some issues in clustering-based routing as
well. Authors in [6] discussed the problem of load balanc-
ing in cluster-based routing and introduced a novel idea
of rotation of cluster head role inside the cluster named
Low-Energy Adaptive Clustering Hierarchy (LEACH).
The protocol assumes that all cluster heads can direct-
ly communicate with the central base station of the
network; therefore it is not applicable in large regions.
The major drawback is that the resultant set of cluster
heads may be unevenly distributed, which causes varia-
ble cluster sizes and higher intra-cluster communication
cost. Cluster heads far away from the base station have to
transmit data over a long distance and suffer a high energy
consumption rate. In a large network, such a disparity
will cause nodes in the far corners of the sensing area to
die quickly.
Authors in [7] proposed Power-Efficient Gathering in
Sensor Information Systems (PEGASIS) which is an
improvement on the LEACH approach. Instead of form-
ing multiple clusters, PEGASIS forms chains of sensor
nodes so each node communicates only with its closest
neighbours and takes turns in transmitting data to the
base station. The protocol assumes that all nodes are to
communicate directly with the base station, so the role of
the leader is rotated among all the nodes forming the
chain in order to balance the energy consumption.
In PEGASIS, a rotation scheme is used to share the
cost of communication with the base station, however un-
even energy dissipation still exists due to the difference
in cluster head positions. In addition, this approach
assumes global data aggregation, that is, sensor data
from all nodes can be aggregated into a single packet.
This is an assumption that is not always true. When it is
not true, the cost of passing each packet along the entire
chain will cause a very short network lifetime.
These major drawbacks were addressed in a centralized
optimization approach proposed by [8]. In this paper, the
authors presented an AntChain method where Ant
Colony Optimization algorithm (ACO) was applied as a
centralized optimization tool to form the near lowest cost
chain for a particular area. Unlike the PEGASIS, sensors
nodes do not have the pr ior global know ledge. The ACO
approach was designed to deal with the dynamics of the
sensor nodes which can ensure the algorithm response to
any network changes in a time.
The ACO algorithm allows all the complicate compu-
tation, optimization and set-up overhead to be performed
by base station which was assumed to have unlimited
energy recourse, much stronger communication and
computation ability. The drawback of this protocol is that
the base station requires information about all the nodes
in a network before the selection of cluster heads. In a
larger network, this approach would not work well since
it uses a centralized approach for the management of the
clusters.
Authors in [9] proposed a Hybrid Energy-Efficient
Distributed Clustering (HEED) that periodically selects
cluster heads according to a hybrid of their residual energy
and a secondary parameter, such as node proximity to its
neighbours or node degree. In this approach, a probabi-
listic algorithm was employed to form a dominating set
in a fixed number of rounds, with a penalty of slightly
large dominating set size. This scheme builds a higher
quality clusters than LEACH and PEGASIS that uses
random selections and single chain, which results in a
longer network lifetime.
In HEED, all cluster heads send aggregated data to the
base station via the shortest path. This scheme min imizes
the total energy consumption. However, the energy
consumption is still unbalanced since neighbours of the
base station are responsible to relay all packets to the
base station and have higher load. A hot spot is formed
in the area surrounding the base station, which is
congested with data traffic and consumes energy much
faster than other areas of the network.
A multilayer multi hop routing algorithm for inter
cluster communication was presented by [10]. The alg ori -
thm worked on the principle of divide and conquers and
performed better in terms of load balancing and energy
efficiency than LEACH. The algorithm was aimed at
exploiting the redundancy property of the WSNs. It
selected a small percent of nodes from the network and
marked them as temporary cluster heads and used these
nodes to make the inter cluster communication multi hop.
The problem with the algorithm was that it was selecting
the temporary cluster heads randomly thus compromi-
sing occasionally on the area coverage of the network
which it is monitoring.
A data aggregation algorithm for WSNs was proposed
in [11]. The algorithm selected a cluster leader that can
perform data aggregation in a partially connected sensor
networks. The proposed scheme is only limited to a par-
tially connected network within an intra-cluster network.
Authors in [12] proposed adaptive energy aware intra
cluster routing (EAICR) in order to address the problem
of load balancing associated with the existing single hop

A. OLUWARANTI ET AL.
277
and multihop routing protocol. Some nodes were con-
sidered in close region and they performed direct routing
and outside the region nodes adopted multihop routing.
In this way, the closer nodes are not having extra load on
them unlike HEED where the closer nodes to the base
station exhaust energy very quickly because they perform
the task of sensing their own data and routing the data of
the other nodes. Thus closer nodes have extra load with
them and there is no concep t of load balancing as well.
The major drawback of this alg orithm is that the scope
is only limited to intra cluster routing algorithm and not
adaptive to inter cluster routing algorithm. Also, the load
is still unbalanced due to hot spots that exist at the cluster
head close to the base station as a result of data relay
through multiple hop .
3. Research Method
This study proposes an improved scalable clustering-
based load balancing routing algorithm (SCLB) that is a
hybrid of the existing inter-cluster and intra-cluster rout-
ing approach. Scalability is achieved by dividing the
network into overlapping multihop clusters each with its
own cluster head node. Each cluster head is responsible
for building a local relative map corresponding to its
cluster using intra-cluster node’s range measurements.
3.1. Cluster Communication
Three major issues within the intra-clusters region were
considered: cluster forming, cluster head re-election and
cluster head canceling. In cluster forming, when a node
is powered on, it marks itself clusterless and sets up a
random waiting timer Tt and starts to monitor the radio
channel for signal request. If no request is heard within
Tt, the node marks itself as a cluster head. Once a node is
notified as cluster h ead, it sends a beacon immediately in
form of a contention packet to the neighbouring nodes
within the cluster set up.
Every node has their budget variables for cluster head
selection which is usually in terms of residual battery
power or elapsed time for acting as cluster head. When a
node is set as cluster head, it starts a timer Tm for acting
as cluster head. After the Tm has expired, it selects the
node that has the highest budget variable as the next
cluster head and sends it a packet fo r notification.
Cluster head canceling occurs when a cluster head
hears a probe request from another cluster head and
thereby set itself as a slave. Subsequently, the node sends
a beacon message to the cluster head for association.
After the cluster head formation, the nodes transmit
their packets to the cluster head in their scheduled time
slot. The cluster head receives all packets from its cluster
members, comp r ess th e data in to one packet and send the
packet to the closest cluster head in another cluster
neighborhood in a peer-to-peer (P2P) communication.
Based on the respective outputs of the sensor readings,
the inter-cluster communication is formed where each
cluster head has to produce an output to be transmitted
over a wireless communication link. The packet outputs
are finally received by the base station. Thus, the scheme
is model as a distributed function of the observations
given the state since the sensors have chance to co-ope-
rate to some extent.
3.2. Simulation Platform
The simulation platform was developed using Java
programming language within a 200 m × 200 m region.
The network comprises of 250 sensor nodes and these
sensor nodes are randomly deployed with different levels
of density. The communication range of each sensor is
set to 20 meters. Two nodes have a wireless link between
them if they are within the commu nication range of each
other.
3.3. Model Assumption
The model was assumed to be of homogenous sensor
nodes where all the nodes are of equal sizes, have
identical hardware capabilities and uniform energy
consumption, uses the same IEEE 802.11 radio protocol.
None of the sensor nodes is resource-rich than the others
and they have equal chances of cluster head selection.
Each of the cluster head is dedicated to both intra-cluster
and inter-cluster traffic and a way to distribute the radio
resources for the two types of traffic was considered.
3.4. Problem Formulation
In SCLB, WSN with n nodes and k number were con-
sidered which is denoted as n1, n2, n3, n4, ···, nk and a
base station b as shown in the Figure 1. A network of
sensors is considered to be connected only if there is at
least one path between each pair of nodes in the network.
Connectivity, therefore, depends primarily on the exis-
tence of paths.
A SCLB model is formulated as a deterministic
directed graph. Th e nodes of the graph correspond to the
transmitting and receiving units, and the edges of the
graph correspond to the connections that link the nodes
in the network and describe its topology.
A directed graph is a graph G = G(V, E) that consists
of ordered pairs of distinct nodes with two sets V(G) and
E(G) where, V = V (G) is the set of p > 1 nodes or
vertices of the graph.

A. OLUWARANTI ET AL.
278
2
1
S
2
2
S
2
3
S
1
1
S
3
1
S
1
2
S
3
2
S
1
3
S
3
3
S
n
2
n
5
n
6
2
1
S
2
2
S
2
3
S
1
1
S
3
1
S
1
2
S
3
2
S
1
3
S3
3
S
n
9
2
1
S
2
2
S
2
3
S
1
1
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1
S
1
2
S3
2
S
1
3
S3
3
S
50
m
n
1
n
3
n
4
n
7
n
8
Figure 1. Annotated graph model of SCLB for WSNs.
E = E (G) is a set of q > 0 pairs of nodes or the edges
or links of G, and Edge E = (u, v) is said to join nodes u
and v in G.
This graph is said to be k-connected if there are at
least k disjoint paths between every pair of nodes u, v

V. If we set d (u, v) as the physical distance between
nodes u and v, and Rc is the radius of communication,
then E is defined as follows:
 

2
,,
c
EuvVduvR 

(1)
The cluster head (CH) nodes n1, n2, n3, ···,nk were
equally spaced over a distance d and each of the nearest
CHs to the base station was 50 m away from the base
station b. Each of the sensed data from sensor nodes i
j
S
within the cluster region of n1, n4 and n7 were sent to
their respective CHs. The aggregated data were com-
pressed and the greedy search algorithm was used to
select and transmit l1, l2 and l3 bits of data from these
nodes to the next CHs n2, n5 and n7 respectively where
they were compressed join tly with the data at that node.
Greedy search algorithm selects the closest node from
the next tier of CH. When one node n1 is closest to
multiple nodes in th e current tier, the node that is farthest
to b wins and marks itself as selected. The other com-
peting nodes have to select from the remaining unmarked
nodes. In this case, we assume idealized entropy coding
that achieves the maximum compression possible.
The l2 bits of packet were transmitted fro m the second
CHs to the third CHs and so on till the last node within
the cluster. Then the jointly compressed lk bits of data
from each cluster were routed to the base station b in a
peer-to-peer approach. SCLB incorporates a cluster-based
joint routing and compression strategies that span from
one extreme where no compression is performed and
each node routes its information in a single-hop routing
to the CH and to the other extreme where data from
every source was compressed sequentially before routing
to the base station.
3.5. Algorithm Development
SCLB algorithm comprises of three phases, namely: the
setup phase, the steady phase and the forwarding phase.
In setup phase, the sensor nodes begin with initializatio n
state where the nodes are at sleep. At a particular thre-
shold function say 5 seconds, the sensor nodes wake up
and enter listening state where the nodes receive protocol
data unit from other neighboring nodes to determine its
role in the topology. After listen ing state, th e sen sor nod e
enters learning state where the node can transmit and
receive but not forwarding packets. This state compares
the medium access control (MAC) address of the sensor
nodes within the same radio signal to its own MAC
address in its routing table to determine whether the MAC
address of the incoming signal is higher than its own
MAC address or not and subsequ ently drop its attempt to
participate in CH election, otherwise the node drops the
MAC address of the incoming signal and elects itself as
the CH.
The sensor nodes enter contention state where the
node with the highest residual energy among the nodes
within a particular cluster becomes the CH. The node
broadcasts its status to the other sensor nodes in form of
probe request and listens for beacons (probe responses)
from the nodes. After the remaining nodes send the
probe response, th e CH initiates association and allocates

A. OLUWARANTI ET AL.
279
a specified time slot for each of the sensor nodes to
transmit to the CH.
The sensor nodes start transmitting in their allotted
time slot using time division multiple access (TDMA)
and this marks the beginning of the steady phase. When
all nodes within an intra-cluster region finish the tran-
smission of aggregated data to the CH, the forwarding
phase begins.
The forwarding phase marks the beginning of the
inter-cluster communication where CH performs compu-
tation on the received data. CH also transmits the data
using multiple-chain multihop approach to the neigh-
boring CH until the data is received at the base station.
Once all the CHs finish the transmission, the control
returns to the steady phase again. This is depicted in
Figure 2.
4. Results and Discussion
In order to carry out the analysis of the simulated outputs,
some performance parameters are taken into consideration
for evaluating the proposed clustering scheme with the
existing multihop lo ad balancing approach.
start
end
Listening
init
Packet
forwarding?
Learning
{MAC>self}
Sleep?
Contention
CH Election
Aggregation &Compression
Steady p hase
Base station
Packet transmission
Slave modes
YES
N
O
YES
YES
N
O
N
O
Figure 2. Execution model of SCLB algorithm.
4.1. Performance Evaluation
Performance metrics can be described as a measure to
analyze or grade an activity or event.
They act as yardsticks on which decisions about such
activity can be made. For this study, energy consumption,
network lifetime and reliability would be the metrics.
4.1.1. Energy Consumption
The study considered the amount of energy the radio
dissipates to run the transmitter or receiver circuitry as
Eelec = 50 nJ/bit and the amount of energy consumed by
the transmit amplifier as Eamp = 100 pJ/bit/m2. The
corresponding transmission power Etx and receiving
power Erx by a l-bit packet over a distance d are:


2
,
txelec amp
EEEldlld (2)
and


,
rx elec
EEld l (3)
The total energy Eij(l, d) for transmitting l-bit data
from source node i to destination node j within a d istan ce
of d is:




,,, ijtx rx
EEEldld ld

, (4)
In HEED, the energy consumed for the shortest path
routing from n7 till n1, that is, the energy consumed to
deliver a packet to the neighbouring clusterhead is given
as:



 
1:
1
11
rx tx
HEED
rx tx
nEnED k
EknE nkELk
n
1
:1

 




 



(5)
In SCLB, a greedy search algorithm is used to form a
straight-chain model. The study adopted the algorithm of
the research conducted by [13] where the total energy
consumed per round at node k is calculated thus:


 
 

1
11 :1
22 2
12
1:
2
11:1
rx txrx
tx
tx
nknk
nn
EED Ek
n
nk nk
EkE L
nn
nk k
ED Lk
nn






 





 







(6)
4.1.2. Networ k Lifetime
Network lifetime is crucial to a large-scale sensor net-
work since it is undesirable or infeasible to replace or re-
charge sensors once the network is deployed. In model-
ing the sensor network lifetime, we obtained formula
applies to any definition of the network lifetime [14] that

280 A. OLUWARANTI ET AL.
is derived based on Strong Law of Large Numbers
(SLLN). This is given by:




Ow
cr
E
PE



 (7)
where,
O

= Total non-rechargeable initial energy;


w
E= Expected wasted energy (that is, total unused
energy in the network when it dies);
Pc = Constant continuous power consumption over the
whole network.

= Average sensor reporting rate (defined as the
number of data collections per unit time);


r
E = Expected reporting energy consumed by all
sensors in a randomly chosen data collection.
Applying equation (7) to the current research study
with an assumption that

= 1, network lifetime
becomes:




O
r
SE E
E


w
(8)
where O = initial energy of a non-rechargeable
battery, and = size of homogenous sensor network.
ES
4.1.3. Reliability
Reliability implies the extent to which a measurement
instrument is said to yield a consistent result. In other
words, reliability points to the degree in which a system
measures the same way each time it is used under the
same condition with the subjects. The reliability of a
system is not measured rather it is estimated.
In wireless sensor networks, reliability is used as a
measure to show how reliable the sensed event can be
reported to the base station and the study uses the metrics
RSCLB to measure the reliability of the system as,
t
SCLB
Re


 (9)
where system failure rate
no offailure
=noofcomponenthours


(10)
and, toperatingtime
4.2. Simulation Results and Discussion
The results of the simulation based on the equations
discussed earlier in the study are presented for the
existing model (HEED) and the proposed model (SCLB).
4.2.1. Energy Consumption
The energy consumed by SCLB at maximal single node
starting from 2 to 8 clusterhead nodes, that is, each of the
closest clusterhead node to the base station is 0.475,
0.650, 0.825, 1.000, 1.175, 1.350 and 1.525 milliJoule
while HEED are 0.650, 1.000, 1.350, 1.700, 2.050, 2.400
and 2.750 milliJoule respectively as shown in Figure 3.
This implies that SCLB achieves an average lower
energy consumption of 39% as compared with HEED.
4.2.2. Networ k Lifetime
The network lifetime is an important metric which the
study consider for SCLB and HEED as shown in Figure
4. Starting from 2 single node clusterhead to 8 single
node clusterhead, SCLB decreases with values: 4.211,
3.077, 2.424, 2.000, 1.70 2, 1.481 and 1.311. On th e other
hand, HEED decreases with values: 3.076, 2.000, 1.481,
1.179, 0.976, 0.833 and 0.727. This implies that SCLB
has an improved network lifetime with an average value
of 65.29% as compared with HEED. The appreciable
increases in network lifetime of SCLB is accounted for
by the paradigm shift from the multihop load balancing
scheme to cluster-based load balancing approach.
Figure 3. Energy consumption for HEED and SCLB.
Figure 4. Netw or k lifetime for HEED and SCLB.

A. OLUWARANTI ET AL.
281
4.2.3. Reliability
The result from Figure 5 shows that RSCLB drops
gradually from 100% to 2% as the operation time of the
system increases. This is due to the fact that at first
transmission of the aggregated data from clusterhead n7
which is the farthest single chain clusterhead load
balancing assumed for the research, almost all the data
were transmitted to the next clusterhead. The next
clusterhead n6 accepts the data and aggregates it with
those sent to it from the nodes within its cluster region.
The total aggregated data were computed and sent to the
next clusterhead.
Clusterhead n5 also collects the data with the sensing
data from the clustered nodes within its intra-communi-
cation domain. These processes continues until the last
clusterhead node close to the base station. Optimal
reliability was attained at 75.6% when the clusterhead
node is at n5. The decrease in reliability was accounted
for by the increase in failure rate of the aggregated data
which resulted in packet loss.
5. Conclusions and Future Work
In this study, we presented a SCLB that is an extension
of the existing studies on WSNs. The proposed scheme
considered a balanced inter-cluster sensor network rout-
ing where all nodes are location aware and have the same
initial energy capab ilities. Simulation results sho wed that
the network lifetime of the proposed scheme improves
with a balanced energy consumption.
Furthermore, we also incorporated reliability into the
protocol for WSNs due to dynamic topology and random
deployment. Obviously, the reliability deteriorates as the
operation hour approaches the clusterhead node close to
base station due to increased number of failure rate in a
single round of transmission of data. In order to improve
the reliability of the clusterhead nodes, more than one
Figure 5. Reliability result of RSCLB.
retransmission will be required to ensure that the data
sent from one clusterhead will reach the next clusterhead.
However, this would pose a challenge of delay in
delivery data to the base station which can be considered
for further study.
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