Wireless Sensor Network, 2009, 1, 340-349
doi:10.4236/wsn.2009.14042 Published Online November 2009 (http://www.scirp.org/journal/wsn).
Dynamic Hierarchical Communication Paradigm for
Wireless Sensor Networks: A Centralized,
Energy Efficient Approach
Suraiya TARANNUM1, S. Srividya2, D. S. Asha2, K. R. Venugopal2
1Department of Telecommunication Engineering, AMC Engineering College, Bangalore, India
2Department of Computer Science and Engineering, Bangalore University, Bangalore, India
Abstract
A Wireless Sensor Network (WSN) consists of a large number of randomly deployed sensor nodes. These
sensor nodes organize themselves into a cooperative network and perform the three basic functions of sens-
ing, computations and communications. Research in WSNs has become an extensive explorative area during
the last few years, especially due the challenges offered, energy constraints of the sensors being one of them.
In this paper, the need for effective utilization of limited power resources is emphasized, which becomes
pre-eminent to the Wireless Sensor Networks. Organizing the network to achieve balanced clusters based on
assigning equal number of sensors to each cluster may have the consequence of unbalanced load on the clus-
ter heads. This results in an unbalanced consumption of energy by the nodes, cumulatively leading to mini-
mization of network lifetime. In this paper, we put forth a Sink administered Load balanced Dynamic Hier-
archical Protocol (SLDHP) to balance the load on the principal nodes. Hierarchical layout of the sensors en-
dows the network with considerable minimization of energy consumption of nodes leading to an increased
lifespan. Simulation results indicate significant improvement of performance over Base station Controlled
Dynamic Clustering Protocol (BCDCP).
Keywords: Wireless Sensor Network, Sink, Principal Node, Superior Node, Network Lifetime
1. Introduction
A Wireless Sensor Network (WSN) is an ad-hoc wireless
telecommunication network which embodies a number
of tiny, low-powered sensor nodes densely deployed
either inside a phenomenon or close to it [1]. The multi-
functioning sensor nodes operate in an unattended envi-
ronment with limited sensing and computational capa-
bilities. The advent of wireless sensor networks has
marked a remarkable change in the field of information
sensing and detection. It is a conjunction of sensor, dis-
tributed information processing, embedded and commu-
nication techniques. WSNs may in the near future be
equally prominent by providing information of the
physical phenomena of interest and ultimately being able
to detect and control them or enable us to construct more
meticulous models of the physical world.
WSNs are easier, faster and cheaper to deploy than
other forms of wireless networks as there are no prede-
termined positions for the sensors. They have higher de-
gree of faulttolerance than other wireless networks and
are self-configuring or self-organizing [2]. Sensors are
deployed randomly and are expected to perform their
mission properly and efficiently. Another unique feature
of sensor networks is the co-operative effort of sensor
nodes to achieve a particular task.
A WSN is envisioned to consist of a large number of
sensors and many base stations. The sensors are equipped
with transceivers to gather information from the environ-
ment and pass it on to one of the base stations. A typical
sensor node consists of four major components: a data
processor unit; a sensor; a radio communication subsystem
that consists of transmitter/receiver electronics, antennas, an
amplifier; and a power supply unit [3]. The sensors are
compact in size which make them extremely energy re-
strained. Further more, replacing batteries in large scales in
possibly harsh terrain becomes infeasible. Hence, it is well
accepted that the key challenge in unlocking the potential of
such networks is maximizing their post-deployment active
lifetime. The lifetime of the wireless sensors may be pro-
longed by ensuring that all aspects of the system achieve
energy efficiency. Since communications in wireless sensor
networks consume significant amount of energy, the de-
signed algorithms must ensure that nodes expend minimum
amount of energy for transmitting and receiving data.

S. TARANNUM ET AL. 341
A web of sensor nodes can be deployed to gather
productive information from the sensor field. Some of
the benefits of using WSNs are extended range of
sensing, fault-tolerance, improved accuracy and lower
cost. As a consequence, the sensor networks are ex-
pected to find extensive use in a variety of applications
including remote climate monitoring, seismic, acoustic,
medical and intelligence data-gathering [4,5]. Hence,
they are suitable for a wide range of applications like
military, health, education, commerce and so on. Mili-
tary applications may range from tracking enemy
movement in the battlefield to guiding targeting system.
Bio-sensors are used for monitoring patients blood
sugar level. Commercial applications may range from
tracking postal packages or office equipment to moni-
toring product quality on an assembly line. Environ-
mental applications include forest-fire detection, flood
detection, tracking movements of birds etc. Sensors are
also used to simulate home automation and to build
smart environments.
Efficient utilization of energy is crucial to the WSNs.
Wireless microsensor network protocols should therefore
be selfconfiguring to enable ease of deployment of nodes,
latency aware, qualitative, robust and to extend system
lifetime. The sensors are extremely energy bounded,
hence the network formed by these sensors are also en-
ergy constrained. The communication devices on these
sensors are small and have limited power and sensing
ranges. A routing protocol coordinates the activities of
individual nodes in the network to achieve global goals
and does it in an efficient manner. The simplest is the
Direct Communication Routing Protocol, where each
node transmits the sensed information directly to the
base station. The nodes consume considerable amount of
energy if the communication path is long. This results in
the early death of distant nodes. To overcome this draw-
back, the technique of Minimum Transmission Energy
utilizes a multhop routing scheme. In this scheme, the
nodes that are close to the base station drain their energy
rapidly as they are involved in transmission of messages
on behalf of others.
Hierarchical routing groups sensors in the entire net-
work into clusters. It aims at reduction of energy con-
sumption by localizing data communication within a
cluster and aggregating data to decrease transmissions to
the base station. The first attempt in this regard, was
made by Low Energy Adaptive Cluster Hierarchy
(LEACH). The operation is framed in iterations and each
iteration comprises of a setup and a data transmission
phase. During the setup phase, nodes organize them-
selves into clusters with predetermined number of
nodes serving as cluster heads. In the data transmission
phase, the self-elected cluster heads aggregate data re-
ceived from the nodes in their cluster before forwarding
to the base station. The role of cluster heads is randomly
rotated among all the nodes in the network. This tech-
nique serves as a basic model for other hierarchical rout-
ing protocols. A centralized version of the adaptive ap-
proach comprises a hierarchical structure in which the
base station has control over the cluster formation. The
base station uses the location and energy information
sent by the nodes to select the predetermined number of
cluster heads. Efficient clustering is achieved as the base
station possess the global knowledge of the network.
This technique exhibits improvement over the adaptive
approach.
In Power Efficient Gathering in Sensor Information
Systems (PEGASIS), the nodes function co-operatively
to optimize network lifetime. A greedy algorithm is used
to configure the network into chains. In each iteration, a
randomly chosen leader node directs the aggregated data
to the base station. A centralized energy efficient routing
protocol called Base Station Controlled Dynamic Clus-
tering Protocol (BCDCP), was proposed which widened
the area for research in hierarchical routing. Here, much
of the functionalities like formation of clusters and rout-
ing paths are performed by the high energy base station
which lightens the load of sensor nodes. This protocol
configures the network into balanced clusters where each
cluster head serves an approximately equal number of
member nodes. Cluster head-to-cluster head multihop
routing is employed in this protocol to transfer the data
to the base station.
Motivation: Efficient management of energy deserves
much of the attention in the WSNs. Routing protocols
designed for WSNs must therefore effectively tackle this
issue in order to enhance the lifetime of the network.
Hierarchical routing techniques are preferable in this
direction. The arrangement of the nodes in the form of a
load balanced hierarchy proves to be beneficial.
Contribution: In our paper, we propose a energy effi-
cient hierarchical routing protocol, SLDHP to increase
the lifetime of homogeneous as well as heterogeneous
WSNs. SLDHP achieves a load balanced hierarchical
arrangement of nodes in the network which performs
better than the other hierarchical routing protocols.
Organization: The rest of the paper is organized as
follows. In section II, we discuss the related work. In
section III, the underlying model is described and the
problem is defined in section IV. Our proposed algorithm,
SLDHP is presented in section V. Performance analysis
is presented in section VI and section VII contains the
conclusion.
2. Related Work
Hierarchical routing aims to efficiently maintain the en-
ergy consumption of sensor nodes by involving them in
multihop communication within a particular cluster and
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342 S. TARANNUM ET AL.
Figure 1. Three main topologies of hierarchical routing
protocols.
by performing data aggregation and fusion to decrease
the number of transmitted messages to the base station.
Heinzelman et al. [6] proposed an adaptive clustering
protocol. This approach employs the technique of ran-
domly changing the role of cluster head among all nodes
in the sensor network. The operation of this protocol is
organized into different iterations where each iteration
consists of a setup phase and a transmission phase. Dur-
ing setup phase, nodes organize themselves into clusters
in which cluster head is elected locally within each clus-
ter. During transmission phase, the self elected cluster
heads aggregate data received from all the nodes within
its cluster, applies a data fusion technique before sending
it directly to the base station. In this method, the decision
is made per iteration and it is assumed that we have a
knowledge of the total residual energy of the network.
In [7], a centralized algorithm for routing is described.
This protocol uses the base station for centralized com-
putation of cluster heads. The base station upon receiving
the location and energy level information from the sensor
nodes during the setup phase, locates a predetermined
number of cluster heads and configures the entire net-
work into clusters. The cluster heads are chosen in such a
way that nodes consume minimum energy for transmit-
ting their data. The shortcoming may be that they drain
their energy rapidly as they have to communicate di-
rectly with the base station irrespective of their positions.
The results in [7] show improvement over [6].
A chain based protocol is presented in [8]. In this pro-
tocol, each node communicates only with a close
neighbour and takes turns to transmit to the base station,
thus reducing the amount of energy spent per iteration.
For constructing a chain, it is assumed that all nodes
have global knowledge of network. A greedy algorithm
is employed to ensure that nodes already on the chain
need not be revisited. Here, even though the forwarding
node has capability of taking more load, it is not assigned
if it is already on the chain.
A centralized clustering based routing protocol is dis-
cussed in [9]. According to this protocol, energy inten-
sive tasks such as cluster setup, cluster head selection,
routing path formation and TDMA schedule creation are
performed by the base station which is assumed to have
unlimited power supply. This protocol configures the
network into balanced clusters, i.e., the number of nodes
in each cluster are same. Such equal clustering results in
an unequal load on the cluster head.
Guangyan et al. [10] have reviewed the energy
efficiency of cluster based routing protocols with ex-
tended conditions of general complexity of data fusion
algorithm, general data compressing ratio and long dis-
tances. They present three discoveries, first of which is
that data fusion algorithm is computed based on applica-
tions. Secondly, multihop scheme not used by earlier
works could sometimes prove beneficial. Thirdly, when
network area is larger than 200mx200m, the number of
high energy dissipating nodes are more which accelerates
the death of nodes. These findings guide in improving
the routing protocols and hence to extend their applica-
tion ranges.
Geographic and energy aware routing algorithm de-
veloped by Yan Yu et al. [11], propagates a query to the
appropriate geographical region without flooding. The
protocol uses energy aware and geographically informed
neighbor selection to route a packet towards the target
region. To disseminate the packet inside the destination
region, a recursive geographic forwarding or restricted
flooding algorithm is used. The protocol exhibits no-
ticeably longer network lifetime as compared to non-
energy aware geographic routing algorithms.
A novel algorithm proposed by Andrea in [12], per-
forms three main functions of configuring the network
into optimum number of clusters, decentralized cluster
head selection and cluster formation. The value of opti-
mum number of clusters depends on total number of
sensors in the network, on the path-loss exponent (α), on

S. TARANNUM ET AL. 343
dimensions of the network and distance of the broadcast
packets. They use an adaptive strategy for cluster head
selection. The algorithm for cluster formation uses total
path energy dissipation instead of energy lost in path
from the node to its cluster head. The algorithm opti-
mizes system lifetime in a large range of applications and
situations.
A cost based comparison of homogeneous and het-
erogeneous clustered sensor networks has been presented.
It first considers single hop clustered sensor networks
and use adaptive clustering protocol. It also takes into
account sensor-network with two types of nodes as rep-
resentative single hop heterogeneous networks. For mul-
tihop homogeneous network Vivek et al. [13] propose
and analyze a multihop variant of the adaptive approach.
They consider communication radius for in-cluster
communication and size of clusters. This algorithm ex-
hibits better energy efficiency in many cases, but does
not give expected performance if the heterogeneity is due
to the operation of the network.
An energy efficient distributed clustering approach for
adhoc sensor network is developed in paper [14]. In this
approach, cluster heads are chosen randomly based on
their residual energy and nodes participate in cluster op-
eration such that the communication cost is minimized.
The protocol does not make any assumptions regarding
the distribution density of nodes. The clustering process
takes a fixed number of iterations and does not depend
on network topology. This protocol acheives only a
two-level hierarchy.
Alan et al. [15] have derived a load balancing heuristic
for wireless adhoc networks in order to extend the life-
span of a cluster head to as large an extent as possible
before another node becomes the cluster head. Two
cluster head load balancing heuristics are described. The
first approach is for cluster election heuristics that favour
the election of cluster head based on node-id, and the
second approach is based on the degree of connectivity.
In [16], a cluster based query protocol is illustrated for
wireless sensor networks using self-organized sensor
clusters to register queries, process queries and dissemi-
nate data within the network. This protocol uses cluster
heads as data storage and aggregation points. Instead of
sending large amounts of raw data over a network to
reply to a query, each cluster head collects and filters
data from its member sensors. This is achieved using the
information about the cluster location. With this protocol,
energy efficiency is achieved by reducing the number of
data transmissions over the network during the course of
the data collection and query processing.
A stable election protocol is described in [17] which is
a heterogeneous-energy-aware protocol. It is based on
weighted election probabilities of each node to become
cluster head based on the remaining energy of each node.
In this approach every sensor node in a heterogeneous
two-level hierarchical network independently elects itself
as a cluster head based on its initial energy relative to
that of other nodes. The protocol does not demand any
global knowledge of energy at every election iteration
and also does not consider as to how nodes could be as-
signed optimally to cluster heads.
In [18], a balanced k-clustering algorithm, for cluster-
ing sensor nodes into k number of clusters is described.
Each cluster is balanced and the total distance between
sensor nodes and the head nodes is minimized. Mini-
mizing the total distance helps in reducing the commu-
nication overhead and hence energy dissipation. The
algorithm demonstrates that the balanced k-clustering
problem can be solved optimally using network flow, but
assumes the number of nodes as a multiple of k at all
times, which may not be practical.
A cluster based routing algorithm is depicted in [19] to
extend the lifetime of the sensor networks and hence to
maintain a uniform consumption of the energy by the
nodes. This is obtained by the addition of a slot in a
frame, which enables the exchange of residual energy
messages between the base station, cluster heads and
nodes. The algorithm takes into account the residual en-
ergy of the nodes during cluster head selection, resulting
in balanced energy consumption of the sensor nodes. The
protocol performs better than the adaptive approach.
In [2], the authors focus on the design criteria for
routing protocols and issues and challenges of clus-
ter-based routing in WSNs. The characteristics and the
general routing models for protocols in sensor networks
are studied here. Yunfeng et al. [20] have devised a pro-
tocol called energy balancing multipath routing, the basic
idea being that instead of source-initiated or destina-
tion-initiated route discovery, it is the base station that
finds multiple paths to the source of the data and selects
one of them to be used during communication. It is based
on the assumption that the base stations are typically
many orders of magnitude more powerful than common
sensor nodes. It adopts a scheme similar to the well
known software architecture client server model.
Energy aware routing that uses sub-optimal paths oc-
casionally to provide substantial gains is designed by
Rahul et al. [22]. It emphasizes that using lowest energy
paths may not be always optimal from the point of view
of network lifetime and long-term connectivity. The
protocol is suitable for low energy and low bitrate net-
works. The key concept is to send traffic through differ-
ent routes which helps in using the node resources more
evenly. It sends the traffic on multiple paths without
adding much complexity by using a probabilistic for-
warding technique. According to this method, the nodes
burn their energy uniformly across the network ensuring
a more graceful degradation of service with time.
The problem of energy-aware routing in networks with
renewable energy sources is adressed by Longbi et al.
[21]. They present a simple, static multi-path routing
approach that is optimal in the large system limit. The
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344 S. TARANNUM ET AL.
proposed static routing scheme utilizes the knowledge on
the traffic patterns and energy consumption, and does not
demand the instantaneous information about the node
energy. For the distributed computation of the optimal
policy, they outline the possible approaches and propose
heuristics to build the set of pre-computed paths. This
scheme outperforms leading dynamic routing algorithms,
and is close to an optimal solution when the energy
claimed by each packet is relatively small compared to
the battery capacity.
3. Model
3.1. The Nomenclature
The terminology used in our study are,
HmNt Homogeneous Network consists of sensors
possessing a uniform initial energy.
HtNt Heterogeneous Network comprises of sensors
with different initial energies.
Set of all the sensor nodes deployed in the
sensor field of the network.
Eavg This is defined as the average energy of the
wireless sensor network.
1
1n
avg k
k
E
n
E (1)
where n is the number of the sensors and Ek is theenergy
of the kth sensor.
Set consisting of sensor nodes with energy
equal to or greater than Eavg, and is a subset of set ,
which is a set of all the sensor nodes deployed in the
network.
PrNd Principal Node is a node which receives the
sensed data from other nodes in its hierarchy, aggregates
it to forward either to another principal node or to the
Superior Node. This functions as the root of the hierar-
chy and sends the aggregated message to the sink.
nmin Minimum energy node.
3.2. Radio Power Model
A typical sensor node is depicted in Figure 2 and con-
sists of four major components: a data processor unit; a
micro-sensor; a radio communication subsystem that
consists of transmitter/receiver electronics, antennas
and an amplifier; and a power supply unit. [23]. Al-
though energy is dissipated in all of the first three
components of a sensor node, energy dissipations as-
sociated with the radio component is considered since
the core objective of this study is to develop an en-
ergy-efficient network layer protocol to improve the
network lifetime. In addition to this, energy dissipated
during data aggregation in the cluster heads is also
accounted.
Figure 2. A typica l se nsor node.
The radio energy model [9] employed in our study is
described in terms of the energy dissipated in transmit-
ting k-bits of data between two nodes separated by a dis-
tancer meters and also the energy spent for receiving at
the destination sensor node and is given by,


,
TTxamp
EkrEkEr k


  (2)


2
amp FS
Erεr

 (3)
The energy cost incurred in the receiver is given by,


RRx
EkE k

 (4)
where denote energy dissipated in the transmitter
of the source node is required to maintain an acceptable
signal-to-noise ratio for reliable transfer of data messages.
We use free space propagation model and hence the en-
ergy dissipation of the amplifier is given by:
amp
E


2
ε
amp FS
Er r (5)
where FS

denotes the transmit amplifier parameter
corresponding to free space.
The assumed values for the various parameters is as
given below.
50n
Tx Rx
EE J/bit


2
10 //
FS pJbitm
 
The energy spent for data aggregation is,
5nJ / bit/ message.
DA
E

4. Problem Definition
A sensor network is described by means of an edge-
weighted graph, (
WSN
G, , Sink),


1
n, 2n n ....n
is a set of sensor nodes and

1
d, d .

2n
...d contains
the inter-node distances.
The objectives of our work are:
1) To develop an energy-efficient hierarchical routing
algorithm which minimizes the energy consumption of
the network.

S. TARANNUM ET AL. 345
2) To maximize the network lifetime.
4.1. Assumptions
 WSN consisting of a fixed sink with unlimited sup-
ply of energy and n wireless sensor nodes having
limited power resources.
 The wireless sensor network can be either homo-
geneous or heterogeneous in nature.
 The nodes are equipped with power control capa-
bilities to vary their transmitted power.
 Each node senses the environment at a fixed rate
and always has data to send to the sink.
 The sensor nodes are aware of their geographic
position as each of them are equipped with a Global
Positioning System (GPS).
5. Sink Administered Load Balanced Dynamic
Hierarchical Protocol (SLDHP)
This section focuses on design details of our proposed
protocol SLDHP, which is a hierarchical wireless sen-
sor network routing protocol. Here the sink with unre-
strained energy plays a vital role by performing energy
intensive tasks thereby bringing out the energy
efficiency of the sensors and rendering the network
endurable. The pattern of the hierarchy varies dynami-
cally as it is based on energy levels of the sensors in
each iteration.
SLDHP functions in two phases namely:
1) Network Configuring Phase
2) Communication Phase.
The algorithm steps are described in Table 1.
5.1. Network Configuring Phase
The goal of this phase is to establish optimal routing paths
for all the sensors in the network. The key factors consid-
ered are balancing the load on the principal nodes and
minimization of energy consumption for data communica-
tion. In this phase, the sink probes the sensors to send the
status message that encapsulates information regarding their
geographical position and current energy level. The sink
upon receiving this, stores the information in its data struc-
tures to facilitate further computations. To construct the
routing path, first the sink traces the node with minimum
energy, from the set N. The minimum energy node
will be alloted to the principal node, which will be
selected based on the following criteria:
min
n
min
n
 The sink reckons the set , that contains nodes
with energy above Eavg, which is a subset of set .
 It then computes the distance between nmin and each
of the nodes in . Consider any two nodes with
respective x and y positions given by (x1, y1) and (x2,
y2). The Euclidean Distance between these two
nodes is given by:

2
12 12
| - | | - |xx yy2 (6)
 The node in the set P which has minimum distance
to min
n is selected as the principal node.
To aid further calculations, the amount of energy spent by
the principal node on receiving and aggregating message
sent from is reduced virtually. The minimum energy
node is then removed from the set
min
n
. This phase repeats
until all the nodes in the network are assigned to principal
nodes. The last node that remains in set is the node with
maximum energy which serves as a superior node and has
the job of sending the aggregated message to the sink.
The protocol gives prime importance to balance the
load on the principal nodes. The minimum energy nodes
will be assigned to a principal node as long as it has the
capability to handle them. Once the energy of the princi-
pal node falls below Eavg, it will be treated as a normal
node and hence will be assigned to another principal
node. In this way, multihop minimal spanning tree is
constructed without a need for running a separate mini-
mal spanning tree algorithm. Figure 3 depicts the hier-
archical setup of our protocol.
SLDHP eliminates the necessity of knowing the opti-
mum number of clusters in the network. The load is
evenly balanced depending upon the capacity of the
principal nodes. The protocol starts with a chaining setup
and ends in a hierarchical model. In this way, multihop,
load balanced network is achieved. The concluding task
of this phase is to determine the Time Division Multiple
Access (TDMA) slots for all the nodes within the hier-
archy. Once all the computations are over, the sink sends
messages to all the sensors indicating their principal
nodes and the TDMA slots.
5.2. Communication Phase
The sensors send their sensed data to their respective
principal nodes. Each of the principal nodes gather data
from the nodes down in their hierarchy, fuses and then
forwards either to another principal node or to the sink.
This phase inturn comprises of three activities.
 Data gathering: utilizes a time-division multiple
access scheduling scheme to minimize collisions
between sensor nodes trying to transmit data to the
principal node.
 Data fusion or aggregation: Once data from all
sensor nodes have been received, the principal node
combines them into a target entity to greatly reduce
the amount of redundant data sent to the sink.
 Data routing: Transfers the data along the principal
node-to-principal node routing to the superior node,
which transmits the fused data to the sink.
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346 S. TARANNUM ET AL.
Table 1. SLDHP algorithm.
6. Performance Analysis
6.1. The Simulation Test-Bed
A homogeneous sensor network was set up with the
simulation environment comprising 100 nodes, with all
nodes possesing the same initial energy of 2J. The simu-
lations were carried out using the Objective Modular
Network Testbed in C + + (OMNeT++) simulator [24].
The sensor nodes were deployed randomly in a sensor
field of a grid size of 500mx500m. The simulations were
carried out several times, for different network
configurations in order to obtain consistent results. The
performance metrics considered are Average Energy
Consumption by the nodes and Network Lifetime. The
proposed protocol was compared with BCDCP and it
was found that SLDHP performed significantly better in
all simulation runs.
6.2. Average Energy Consumption of the Sensor
Network
Figure 4 shows the Average Energy Consumption of the
sensor network, as a variation with reference to number
of iterations of the network. The simulation environment
is setup with the initial battery energy of all nodes being
2J and a message length of 4 kbits/packet. We observe
that the protocol greatly reduces the energy consumed
and hence outperforms others in terms of battery
efficiency. This is due to the minimum-spanning tree
hierarchical structure formed by SLDHP as compared to
the cluster-based structure which consists of equal num
Figure 3. Hierarchical setup of SLDHP.
ber of member nodes with unequal distribution of energy.
BCDCP achieves balancing by assigning equal number
of nodes to each of the clusters which results in over-
loading the already overloaded cluster-heads to drain out
much of their energy on receiving, aggregating and
transmitting the data at a much faster rate. In comparison,
our proposed algorithm comprises of unequal member
nodes within the hierarchy, but load balanced in terms of
energy resources, which contributes significantly to the
increased energy efficiency of the algorithm. Hence the
packet transmission time in our algorithm is predomi-
nantly short as compared to others. From the plot, it is
observed that initially when the number of iterations is
less, energy consumption in both the schemes is found to
be almost the same, with no conspicuous results. This is
due to the fact that the hierarchical structure at this point
of time seems almost the same. The real advantage
comes to light when the number of iterations increases,
with the hierarchical structure adapting itself dynami-
cally to the changing scenario. The superior performance
offered by SLDHP enables to achieve a reduction of en-
ergy consumption by about 21% as compared to the ear-
lier algorithms.
6.3. Sensor Network Lifespan
The energy consumption rate can directly influence the
lifes-pan of the sensor nodes as the depletion of battery
resources will eventually cause failure of the nodes.
Hence the wireless engineer is always entrusted with the
task of prolonging the lifespan of the network by im-
proving the longevity of the sensor nodes. The simula-
tion results of number of nodes alive over a period of
time are presented in Figure 6. The simulation environ-
ment is the same, i.e., initial energy of nodes being 2J,
message length being 4 kbits/packet and the initial node
density being 100. Both the protocols are based on a hi-
erarchical structure in which all the nodes rotate to take
responsibility for being the cluster-head and hence no
particular sensor is unfairly exploited in battery con-
sumption. Due to the hierarchical structure, it is found
that till the 806th iteration, the number of nodes that are
alive is almost the same in both schemes and equals 100.

S. TARANNUM ET AL. 347
This implies that the time duration between the first ex-
hausted node and the last one is quite short or the differ-
ence in energy levels from node to node does not vary
greatly for lower number of iterations. After this critical
point, both the curves in the Figure drop indicating the fall
in the number of alive nodes. It is evident from the plot that
the number of alive nodes is significantly more in our pro-
tocol as compared to other and which agrees with the re-
sults obtained in the previous simulation. This algorithm
can extend the lifespan of the network by about 34% as
compared to the earlier algorithm. It is observed that the
number of alive nodes in earlier algorithm is a maximum of
100, dropping at a steady rate till none of the nodes are
found to be alive at the 1800th iteration. In comparison, the
nodes of SLDHP are very much live and active even for a
little beyond the 2000th iteration, once again indicating the
superior performance of the algorithm. The reason for this
is again the same, the difference in hierarchical structure,
plus the added advantage of dynamically having a load
balancing scheme.
6.4. Average Energy Consumption for Varying
Message Lengths
Figure 5 shows the average energy consumption of the
network when SLDHP is run with the data communication
phase transmitting data at varying message lengths of
4kbits/packet and 8kbits/packet respectively. From the
plot, it is observed that when the message length is 4
kbits/packet, the behaviour is exactly similar to the one
depicted in Figure 4 for SLDHP due to the similarities of
the simulation environment set up. When the message
length is doubled, the average energy consumption of the
sensor network is much more as observed from the simu-
lation results. This is quite obvious because of greater
overhead involved in aggregating and transmitting a larger
sized message. From the plot, it is seen that at the end of
the 2000th iteration, the energy consumed for transmitting
a smaller message is close to 2J while the same energy
level is reached in the 1620th iteration itself, for a larger
message transmission. A message length of 4 kbits/pkt
seems ideal as lesser length message may not be in a posi-
tion to carry out the desired task and a larger length may
unnecessarily contribute to additional overhead which can
degrade the performance of the network.
The plots in Figure 7 show the average energy con-
sumption of the network with proposed algorithm run for
two different message lengths. The simulation environ-
ment is set up with all the nodes equipped with a uniform
initial energy of 2J. The node density is varied to account
for scalability of the WSN and at the same time will aid
in understanding the behaviour of the network especially
in terms of energy management of the network for vary-
ing node densities. For comparatively lower value of
node density, the average energy consumption of the
network is smaller being a little less than 0.06J for a
Figure 4. Comparison of average energy consumption.
Figure 5. Average energy consumption (SLDHP) with vari-
able size of the packet.
smaller message length, increasing steadily to about
0.09J for a node density of 100. In comparison, it is
found that the energy consumption is relatively more for
a larger sized message, varying from 0.078J for 40 nodes
reaching a value of 0.12J for 100 nodes. This behavior is
much the same as for a smaller message, the difference
being that obviously more energy is consumed for a lar-
ger message size. As the number of nodes increase, the
complexity of the network configuring phase also in-
creases proportionately leading to an increased overhead
on the sink to dynamically form load balanced hierar-
chical structures. The complexity of the data communi-
cation phase is no less, with more number of nodes being
involved in data communications and with the complex-
ity increasing with increasing nodes. The energy con-
sumption of the network increases in proportion to the
number of nodes and the same analogy holds good for
C
opyright © 2009 SciRes. WSN

348 S. TARANNUM ET AL.
Figure 6. Comparison of lifespan of the wireless sensor
network.
different message lengths, the consumption being much
more for larger sized messages.
6.5. Network Lifespan for Varying Size of Packet
Figure 8 shows another performance run when com-
munications in SLDHP, take place by transmitting
varying length messages of 4 kbits/packet and 8
kbits/packet. The simulations are carried out under
similar conditions. As seen from the plot, when the
message length is 4 kbits/packet, larger number of
nodes are alive and the same is confirmed by the results
obtained in Figure 6. When the message length is dou-
bled, the saturation of the network takes place at a
faster rate due to increased overhead on the sensor
nodes and the principal nodes in particular. This mani-
fests in nodes consuming larger energy, resulting in a
larger transmission cost, leading to a shorter lifespan of
the network. The smaller the message length, greater is
the lifespan of the network with the number of live
nodes prolonging the network lifespan to as long as the
2000th iteration. Till the 1400th iteration, the number of
alive nodes in both cases seems exactly the same, but
drops abruptly to zero at the 1635th iteration, for a
larger message length. The reason for this is the same
as described for Figure 6 and hence the same inference
can be drawn here as well. Hence it is inferred that
4kbits/packet is apt for the present scenario.
7. Conclusions
A WSN is composed of tens to thousands of sensor
nodes which communicate through a wireless channel for
information sharing and processing. The sensors can be
Figure 7. Average energy consumption (SLDHP) for dif-
ferent packet lengths.
Figure 8. Lifespan of the wireless sensor network (SLDHP)
with variable size of packet.
deployed on a large scale for environmental monitoring
and habitat study, for military surveillance, in emergent
environments for search and rescue, in buildings for in-
frastructure health monitoring, in homes to realize a
smart environment. SLDHP manages to balance the load
on the principal nodes and hence the sensor nodes are
relieved from the energy intensive tasks such as forma-
tion of hierarchy and scheduling of slots to send their
sensed data. This job is effectively accomplished by the
high powered sink. The simulation results indicate that
the network lifetime is elevated to a large extent when

S. TARANNUM ET AL. 349
compared to other hierarchical routing protocols. The
future work includes applying our protocol to a distrib-
uted wireless sensor network and hence to improve the
network performance as in present scenario.
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