Wireless Sensor Network, 2011, 3, 329-333
doi:10.4236/wsn.2011.310036 Published Online October 2011 (http://www.SciRP.org/journal/wsn)
Copyright © 2011 SciRes. WSN
Energy-Efficient Data Collection in Wireless Sensor
Mohammad Hossein Anisi, Abdul Hanan Abdullah, Shukor Abd Razak
Department of Computer Systems and Communications, Universiti Teknologi Malaysia, Skudai, Malaysia
E-mail: anisi@live.utm.my
Received July 30, 2011; revised A ugust 22, 2011; accepted September 28, 2011
Wireless Sensor Networks (WSNs) are usually self-organized wireless ad hoc networks comprising of a
large number of resource constrained sensor nodes. One of the most important tasks of these sensor nodes is
systematic collection of data and transmits gathered data to a distant base station (BS). Hence network life-
time becomes an important parameter for efficient design of data gathering schemes for sensor networks. In
this paper, we benefit both cluster and tree structures for data gathering. In our proposed energy-efficient
mechanism, the most appropriates hops for data forwarding will be selected and the lifetime of the whole
network will be maximized. The simulation results show that by using the proposed approach, the lifetime
and the throughput of the network will be increased.
Keywords: Wireless Sensor Networks, Data Aggregation, Lifetime, Residual Energy, Data Correlation
1. Introduction
Wireless Sensor Networks (WSN) consists of several
sensor enable nodes which are distributed in an envir-
onment and use batteries as energy resource. These tiny
sensor nodes, which consist of sensing, data processing,
and communicating components, result in the idea of
sensor networks based on collaborative effort of a large
number of nodes. Such sensor nodes could be deployed
in home, military, science, and industry applications such
as transportation, health care, disaster recovery, warfare,
security, industrial and building automation, and even
space exploration. Among a large variety of applications,
phenomena monitoring is one of the key areas in wireless
sensor networks and in such networks, you can query the
physical quantities of the environment [1-3].
In fact, a typical wireless sensor network is composed
of a large number of sensor nodes, which are randomly
dispersed over the interested area, picking up the signals
by all kinds of sensors and the data acquiring unit,
processing and transmitting them to a node which is
called sink node. The sink node requests sensory
information by sending a query throughout the sensor
field. This query is received at sensor nodes (or sources).
When the node finds data matching the query, the data
(or response) is routed back to the sink. For example, if
the sensors nodes be in a tree like structure, the base
station roles as the root of the tree and each node will
have a parent [4,5]. Therefore, the data items can be
transmitted hop by hop from the leaf nodes to the root.
In WSNs, data compression refers to the use of
compression techniques to reduce the amount of bytes
required to code the different pieces of information and,
thus, the traffic load which needs to be processed within
the network.
As the sensor nodes are small and battery enable
devices, they have limited energy which should be used
precisely. Thus, the scarce sensor resources (in particular,
the battery power) are easily over consumed. Thus, the
key challenge in such phenomena monitoring is conser-
ving the sensor energy, so as to maximize their lifetime.
Most of the approaches tried to response to this challenge
and this will be continue to gain a better solution.
In this paper, we propose an energy-efficient in-
Network data aggregation approach in WSN. The pro-
posed approach uses the advantages of both cluster based
and tree based approaches. In this approach, the whole
network consists of some clusters with the same size.
Each node is related to a routing sub tree and each sub
tree overwhelms a cluster and the root node of each sub
tree is the head node of the related cluster. The energy
consumption in wireless transmissions is equal to the
square of distance between two nodes in communication.
In the proposed approach, all the nodes transmit their
data to their neighbor instead of their cluster head.
Therefore, the communication distance is reduced and
the energy consumption of each node, each cluster and
the average energy consumption of the whole networks
is reduced and the network lifetime is increased. Also, in
the proposed approach, the most appropriate parent
according to some benchmarks will be selected for each
node which can balance the network load.
2. Related Works
There are several approaches which use tree structure for
collecting and aggregating data. The presented approach
in [6], with combining Clustering and Directed Diffusion
Protocol [7], could process, collect, and aggregate data of
sensor nodes without any dependency to the related
environment. This paper, with presenting a dynamic
clustering structure, could enable the nodes to join to the
nearest head cluster while sending data to the gateway
Most of data gathering algorithms focus network life-
time and saving energy [4,8-11].
In the TAG (Tiny Aggregation) approach [4], each
epoch divides to some time slots and these time slots
specify to different levels of routing tree in reversal form.
In this manner, each node depends on its situation in the
tree, and in its related time slot will send its data. The
node synchronization of this approach for sending and
receiving data could effectively reduce the average
energy consumption.
In Directed Diffusion Approach [12,13] receivers and
resources using some attributes for recognizing the
produced or required information and the goal of this
approach is finding an efficient multi way route between
senders and receivers. In this approach, each task is
represented as an interest and each interest is a set of
attribute-value pairs.
The LEACH (Low-Energy Adaptive Clustering Hiera-
rchy) protocol [14] uses a random approach for dis-
tributing energy consumption among the nodes. In this
approach, the nodes organize themselves as local clusters
and one node roles as a local base station or a cluster
head. If the cluster heads can be selected base on a
priority permanently and they also can be permanent in
the whole life time of system, it is obvious that the bad
luck nodes which are selected as the cluster heads will be
died soon and the life of all the nodes in their cluster will
be finished. Thus, LEACH chooses the cluster head
among the nodes which have enough energy randomly.
This can prevent the discharging of the battery of a
special node. In addition, LEACH uses local data fusion
for compressing the data which should be sent from
cluster heads to the base station.
FTEP [15] is a dynamic and distributed CH election
algorithm based upon two level clustering schemes. If
energy level of current CH falls below a threshold value
or any CH fails to communicate with cluster members
then election process is started which is based on residual
energy of sensor nodes.
In EEMC (An Energy Efficient Multi Level Clustering)
[16], CHs at each level are elected on the basis of
probability function which takes into consideration the
residual energy as well as distance factor very efficiently.
In this scheme whole information is sent and received by
sink node for cluster formation.
Steiner Points Grid Routing was proposed by, Chiu-
Kuo Liang, et al. [17] In order to reduce the total energy
consumption for data transmission between the source
node and the sink node, a different virtual grid structure
instead of virtual grid in GGR is constructed. The idea is
to construct the virtual grid structure based on the square
Steiner trees [18].
The paper in [19] presents a new version of LEACH
protocol called VLEACH which aims to reduce energy
consumption within the wireless network. In this
approach, by selecting a Vice-CH, cluster nodes data will
always reach the BS; no need to elect a new CH each
time the CH dies. This will extend the overall network
life time.
3. Data Compression with Conditional
The goal of data compression is removing the data over-
head of sensors and reducing the correlations of data for
achieving beneficial information for the base station.
Data source of each sensor node is illustrated with a dis-
crete random variable. The entropy of discrete random
variable of X is illustrated with H(X) that is equal to
minimum number of bits which are required for coding X
without losing any information [20]. The common
entropy of two random variables of X and Y is equal to
the minimum number of bits which are required for
coding X and Y together. If X includes some information
from Y, then the common entropy is equal to H (X|Y) and
H (X|Y) H (X) which ensure a considerable reduction in
the volume of coded data. In The proposed approach, the
conditional entropy is used for coding the data of
For modeling the data correlations we have used the
model in [21]. In fact, the data correlation of the nodes is
a function of distance. Thus, with a network of N nodes
(X1, X2, ···, XN) in which the data production of each node
is equal to H (Xi) = H1{i = 1, 2, ···, N}, we have:
Copyright © 2011 SciRes. WSN
1 1,2,
ji ,
 
In this equation c is a constant which presents the
amount of data correlation and d is the distance between
nodes and .
4. Proposed Approach
We assume that the whole network is divided in to
several clusters; each cluster has a cluster-head (CH).
The clustering and the selection of cluster-head (CH) can
be done by using any existing protocol like LEACH, or
the optimized versions of LEACH such as [19] and [22].
The proposed algorithm works in each cluster inde-
pendently and performs in two phases.
4.1. Inform at i on Pack e t Flow
In this phase, the cluster head transmit the information
packet to its neighbors. The information packets include
the information below:
Node‘s location: Each node should now it location in
Current Energy: Remaining energy of a node
Hop count: Number of hops from cluster head
Data Label: Data value which is sensed by a node
When a node receives the information packet, it con-
siders the sender as one of its possible parents and stores
its information. Then, it updates the node location,
current energy and data label fields of the packets with
its own, increments the hop count and transmit the
packet to its neighbors. This process will be done until
all the nodes in the cluster receive the information
4.2. Tree Construction and Data Forwarding
When the entire nodes received the information packet,
each node selects it parent which should sent its data to it.
This selection will be done based on the filters below:
First, among the possible parent, the one which has the
least hop distance from the cluster head (Closest node to
cluster head) will be selected.
If there is more than one node with the least hop
distance, the nodes which have the most current energy
will be selected as the parent.
If there is more than one node with the least hop
distance and the most current energy, the node which has
the least data correlation will be selected as the parent.
All the above conditions lead to the best parent selec-
tion. Filter 1, selects the shortest path from a node to
cluster head. Filter 2, increases the network lifetime by
participating most durable nodes. Filter 3, reduce the
networks overhead by checking the data correlation of
the nodes.
4.3. Energy Model
Our energy model is like the energy model in [23]. In
this model energy consumption for transmitting K bit is
equal to:
TXelec amp
And the energy for receiving K bit is equal to:
RX elec
In these equations, d is a constant value which relates
to the distance between two nodes and the parameters
below are the constant values which are defined previ-
ously and they are equal to:
100 pJbitm 50 nJbit
amp elec
5. Performance Evaluation
The proposed approach is simulated and evaluated with
J-Sim (Java-Based simulator) [14]. J-SIM is simulation
software selected to implement the model. It was chosen
because it is component-based, a feature that enables
users to modify or improve it. J-Sim uses the concept of
components instead of the concept of having an object
for each individual node. J-Sim uses three top level
components: the target node which produces stimuli, the
sensor node that reacts to the stimuli, and the sink node
which is the ultimate destination. For stimuli reporting,
each component is broken into parts and modeled
differently within the simulator; this eases the use of
different protocols in different simulation runs. In our
simulation analysis, sensor nodes are randomly distri-
buted in a 160 m × 160 m area. The radio range of each
node is 40 m and the default parameters for radio com-
munication model of J-sim are used. The cluster-head is
formed by the sink. Source node randomly sends
packages with constant bit rate (CBR) to the sink. Packet
size is 64 bytes and package rate is 5 pkt/s.
We have compared our approach with LEACH as an
innovative Energy-Efficient clustering approach and
VLEACH as a modern Energy-Efficient clustering
approach. As it has mentioned before, our idea is not
related to clustering and the selection of cluster-head
(CH) and they can be done by using any existing
protocol like LEACH, or the optimized versions of
LEACH. Therefore, For Clustering, we have used the
Copyright © 2011 SciRes. WSN
mechanism of VLEACH in our simulation which is more
energy-efficient in comparison to LEACH.
According to Figure 1, the total residual energy of the
nodes will be decreased, gradually. But Comparing to
other approaches, the proposed approach, because of
using the mentioned technique, can remain more energy.
Figure 2 illustrates the throughputs of the mentioned
approaches. Throughput of a node is defined as the
average rate of successful message delivery over a com-
munication channel. Thus, we can observe that has the
highest throughput among LEACH and VLEACH.
In our proposed approach, increasing the data correla-
tion will increase the network lifetime. In Figure 3, the
horizontal pivot shows the Data Correlation Parameter (c)
which is explained in Section 3 and the vertical pivot
demonstrates the number of epochs until the dead of the
first node. c has direct relation to data correlation. Larger
numbers of c lead to more data correlation which
increases lifetime, too.
6. Conclusions
In this paper, we have proposed an energy-efficient data
Figure 1. The reaming energy of the nodes after passing
Figure 2. Di fferent Throughput of the approac hes.
Figure 3. Increasing the network lifetime by increasing the
data C orrel ation in the proposed approach.
collection approach in wireless sensor networks which
uses an efficient strategy to forward data toward the best
route. In our algorithm, there are three factors which
enable the nodes to choose an appropriate parent in term
of energy. These factors are distance, residual energy and
data correlation. With the suggested mechanism, the
remaining energy of the nodes will be increased and the
life time of the whole network will be increased, too.
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