International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
Study of Various Approaches for Minimizing
Energy Consumption in Wireless Sensor
Network
Palkin Sharma1, Sonia Goyal2
1
Department of ECE, Punjabi University, Patiala, Punjab, INDIA
Department of ECE, Punjabi University, Patiala, Punjab, INDIA
2
Abstract- With the advancement in technology, the
application fields of Wireless Sensor Networks (WSN)
are also increasing. Sensors are the devices that are
used to detect or monitor an object or area of interest
and communicate the data collected to other nodes in
the network. Wireless Sensor Networks are deployed
for battlefield surveillance, tracking, area, health and
environmental monitoring. These applications require
a network of number of sensor nodes. The nodes are
battery powered, and this limited energy source
becomes the major limitation for wireless sensor
network because network will be operational as long
as the nodes in the network are having sufficient
energy for the entire process. This paper reviews
different approaches that contributed towards the
extended network lifetime and increased energy
efficiency in WSN.
Keywords- Energy consumption, limitation, network
lifetime, optimum, wireless sensor networks
I. INTRODUCTION
Wireless sensor network consists of large number of
sensor nodes. These sensor nodes have the capability
of sensing the environment, collecting data and after
processing the data, it is sent to the sink or base station
using single hp or multi hop communication. Wireless
Sensor Networks (WSN) consists of various types of
nodes such as infrared, acoustic, radar, seismic,
thermal, biological, etc. so thus they have wide
application range [1]. The sensor nodes used in the
network are powered by batteries so when they are
deployed in hazardous areas, battery replacement
becomes impractical. The node energy thus becomes
an important issue as it directly affects the duration of
network. Generally the network lifetime is defined as
duration until any sensor node dies [2]. In order to
extend the network lifetime with limited battery power,
energy conservation is required. The various sources of
energy consumption can be idle listening (listening to
an idle channel), collision (more than one packet is
received), overhearing (receiving packet destined to
other nodes), and many more [3]. Among various
sources of energy consumption, communication phase
ISSN: 2231-5381
requires the most of energy. So efficient use of energy
has become an important requirement in the WSN and
gained much attention of researchers in recent time. In
order to increase the energy efficiency, hierarchical
network architecture or clustered architecture is
considered as a solution [4]. Nodes are basically
combined into clusters and for every cluster a selected
node called as cluster head collects the data and
transmits to the sink or base station. Various clustering
algorithms have been developed in recent times in
which different parameters have been considered in
order to increase the network lifetime. The network
requires deployment of nodes, clustering and then data
transmission to the base station. Increasing energy
efficiency approaches have been adopted at node
deployment phase, data aggregation and clustering
phase. Low Energy Adaptive Clustering Hierarchy
(LEACH) is the common and popular clustering
protocol to achieve the load balancing. Other protocols
are Energy Efficient Clustering Scheme (EECS) [5],
Hybrid Energy Efficient Distributed Clustering
(HEED), Stable Election Protocol (SEP) [6]. The
clustering schemes form hierarchical network with
cluster heads at the higher level and member nodes at
the lower level. Various approaches have been adopted
during node deployment and data aggregation as these
can also contribute to energy conservation. Each of the
approach has considered one or more parameter to
improve the performance of the wireless sensor
network.
For the purpose of sensing the area of interest, nodes
are deployed in the desired area randomly or
deterministically [7]. Node deployment in an efficient
way can also lead to energy conservation it the
network. Clustering has been considered as an energy
efficient approach so nodes are divided into clusters
and cluster heads are selected based on various
parameters. The parameters will contribute towards
the improvement in the working of the network.
Cluster heads collect the data from nodes and
aggregate the data before sending it to sink or base
http://www.ijettjournal.org
Page 180
International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
station. Aggregation of data also consumes energy of
the nodes, especially the cluster heads, so different
mechanisms have been proposed for data aggregation
also [8]. The transmission of data packets of L bits
over a distance d also consumes energy given as :
is the cross- over distance,
is
energy dissipation rate to run transmit amplifier when
d<
, l is packet size,
is energy
dissipation rate to run transmit amplifier when d
>
,
is the energy being dissipated to run
the transmitter or receiver circuitry [10].
E = L*
+ L* *
if d<<
(1) A new improved barrier coverage algorithm increased
=L*
+ L*
* if d>>
the network lifetime upto 34.63% by introducing four
(2)
procedures BEGIN, ACTIVE, IDEAL and CHECK.
The amount of energy required to receive a packet of Energy consumption in proposed method is 10.21 J
L bits is given by:
which is very less [11]. Various parameters of node
deployment like coverage area, number of nodes also
E =L*
affect Quality of Service (QoS) of the network.
(3)
Considering these parameters, improvement in QoS is
Where
is the energy being dissipated to run the achieved in [12].
transmitter or receiver circuitry,
and
is the
amount of energy dissipated per bit [9]. Recent B. Selection of Cluster Head
researches in wireless sensor networks have focussed Clustering leads to load balancing and minimized
on improving the energy consumption by analyzing need of network maintenance. Selection of optimum
various parameters and increasing the network cluster head is the main area of research these days.
lifetime.
Various algorithms have been developed. Low Energy
Adaptive Clustering Hierarchy (LEACH) is one of the
II. RELATED WORK
very first protocol and all the further protocols are
The limited battery power source is the biggest based on this. The selection of cluster head is
challenge for the wireless sensor networks as it probability based and it leads to load balancing but
directly influences the lifetime of networks. For randomly selected cluster heads are not optimum in
covering larger area and monitoring of areas for a number so imbalanced energy consumption will be
longer time, it is required that the sensor nodes remain there [13]. Residual energy of a sensor node also
alive for a longer period otherwise network cannot affects the lifetime of a network so it can be one of the
operate in an efficient manner. Thus it has become parameter to choose the best cluster head so as to
important to use such methodologies in order to increase time for which network operates . the residual
increase this lifetime of networks such that services energy has been considered as a parameter for cluster
are provided but with reduced energy consumption. head selection in [14]. The residual energy of a sensor
Recent works are aimed at energy efficiency and node is given by:
improvement mechanisms adopted during node
=
-[(
)+(
)]
(5)
deployment, selection of cluster head and data where
,
are the energies required by the node
aggregation.
for transmission and reception of the packet
respectively[14]. In [15], Adaptive Cluster Habit
A. Node Deployment
(ACH) protocol selects the optimum number of cluster
Effective node deployment leads to increased
heads, and this proposed protocol is 31.8%, 35.6%,
coverage area, better connectivity, and energy saving
22.5% and 10.7% more efficient than Low Energy
in the network. Nodes deployed on the basis of
Adaptive Clustering Hierarchy (LEACH), Threshold
heterogeneity in energy dissipation, improved the
sensitive Energy Efficient Network (TEEN), Stable
number of alive nodes by 80% in the network and lead Election Protocol (SEP), Distributed Energy Efficient
to extended network lifetime [10]. The average energy Clustering (DEEC) in terms of network lifetime.
dissipation in combinations of sensor nodes made is
Energy consumption is more in processing the packets
calculated on the basis of equation given by:
of information, one such factor is considered in [16]
which is length of the packet. The energy consumed
(l,d)= l
+
: d<
by the Cluster head is given by:
=l
+
:d
=L*(
+
)+L*
*
(6)
>
(4)
where L is length of the packet,
is the energy
being dissipated to run the transmitter or receiver
ISSN: 2231-5381
http://www.ijettjournal.org
Page 181
International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
circuitry,
is the amount of energy dissipated per
bit,
is the energy required in data aggregation by
the Cluster Head (CH) [16].
An intelligent routing protocol [17], used
reinforcement learning algorithm for the selection of
cluster heads in a network and considered parameters
like battery level of the node and distance between a
node and the base station. The main focus of the work
is on the three output parameters network lifetime,
packet delay and packet delivery. The network
lifetime increases up to 20.5%, packet delivery factor
increases by 47% and packet delay is minimized.
Another approach for energy efficient clustering is
proposed in [18] using fuzzy logic. The parameters
considered for selection are residual energy of nodes
and distance of nodes from base station. This
approach increases the network lifetime up to 5-7% as
compared to LEACH.
C. Data Aggregation
For the purpose of increased energy efficiency and
increased network lifetime, data transmission is also
minimized in some approaches by data aggregation. A
new data collection approach Maximum Amount
Shortest Path is used in [19], it considers shortest path,
residual energy and delay for improvement of the
network lifetime. It is an improvement of ant colony
optimization. In [20], time constraint during the data
aggregation at sensor nodes is considered and
provided optimal time allotment for intermediate
node.
The energy consumption and other output parameters
considered for the performance evaluation of the
proposed methods in various recent works has been
shown in Table I. The improvements with the
proposed methods have been
evaluated considering the performance metrics with
increasing node density or other factors.
III. CONCLUSION
The limited source of battery power has become major
constraint for the operation of the wireless sensor
networks. Various protocols and schemes proposed in
recent times have led to improvements in the lifetime
of the networks. Different protocols have considered
different input parameters and performance metrics
like network lifetime, throughput, node density,
quality of service and others have been used to show
the improvement as compared to previous works. So
we conclude that parameters like energy of the node,
distance from the base station, length of the packet
etc. can affect the network lifetime and performance
ISSN: 2231-5381
of the wireless sensor networks and these parameters
can be further analysed to make more changes in the
performances.
Table I: Output parameters and performance evaluation of various
methods for energy efficiency
PROPOSED
METHOD
OUTPUT
PARAMETERS
PERFORMANCE
EVALUATION
Optimum
node deployment
[10]
Energy
Dissipation
0.0022J
Network
lifetime
Improved
Barrier coverage
algorithm[11]
Cluster
head selection
Based on
residual
energy &
cluster
reformation
[14]
Adaptive
Cluster Head
(ACH)[15]
Energy
consumption
Increased up to
30%
33.62J
less
consumption
Network
lifetime
34.63%
Improvement
Average
Energy
consumed
Decreased
from 13.76J to
11.87J with
increase in nodes
Increased from
280 sec. to
670 sec.
Network
lifetime
Network
Lifetime
Throughput
Improved by
39.4%
Increased by
30.7%
Stability
period
Increased by
40%
Packet
Energy
Consumption
Length
Optimization
Using Delta
Modulator
[16]
Cluster head
Network
lifetime
Selection
using
reinforcement Packet
delivery
learning[17]
30% energy
saved
with optimization
Cluster
head selection
using
Fuzzy logic
[18]
16%
Increased
http://www.ijettjournal.org
No. of
alive nodes
16% increased
44% increased
Page 182
less
International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
REFERENCES
[1] C. Li, M. Ye, Guihai Chen, J. Wu, “An Energy Efficient
Unequal Clustering Mechanism for Wireless Sensor Networks”,
IEEE International Conference on Mobile Adhoc and Sensor
Systems”, pp. 604-612, 2005
[2] A.Alfieri, A.Bianco, P.Brandimarte, “Maximizing System
Lifetime in Wireless Sensor Networks”, pp 390-402, 2007
[3] M. Leghari, S. Abbasi, Dr. L. Das Dhomeja, “A Survey on
Packet size Optimization Techniques in Wireless Sensor
Networks”, Research Gate, First International Conference on
Wireless Sensor Networks, April 2013
[11] Nagarajan. M, Dr. S. Karthikeyan, “A New Approach to
increase the lifetime and efficiency of wireless sensor network”,
IEEE, International Conference on Medical Engineering, 2012
[12] Horvat, Goran, “Influence of Node Deployment parameters on
QoS in Large Scale Wireless Sensor Networks”, IEEE 3rd
Mediterranean Conference on Embedded Computing, pp. 202- 205,
2014
[13] W. Heinzelman, H.Balakrishnan, “Energy Efficient
Communication Protocol for Wireless Microsensor Networks”,
IEEE 33rd Hawaii International Conference on System Sciences,
Vol. 8, 2000
[4] I.F Akyildiz, W.Su, Y.Sankarasubramaniam, E.Cayirci, “A
Survey on Sensor Networks”, IEEE Communication Magazine, vol
40, pp. 102-114, 2002
[14] A. Chunawale, S. Sirsikar. “Minimization of Average Energy
Consumption to Prolong Lifetime of Wireless Sensor Networks”,
IEEE Global Conference on Wireless Computing and Networking,
pp. 244-248, 2014
[5] J. Singh, R. Kumar, A. Kumar Mishra, “Clustering Algorithms
for Wireless Sensor Networks: A Review”, IEEE 2 nd International
Conference on Computing for Sustainable Global Development,
pp.637-642, 2015
[15] A. Ahmad, N. Javaid, “
: Routing Scheme to Maximize
Lifetime and Throughput of Wireless Sensor Networks”, IEEE
Sensors Journal, pp. 3516-3532, 2014
[6] M. Pramanick, P. Basak, S. Neogy, “Analysis of Energy
Efficient Wireless Sensor Networks Routing Schemes”, IEEE
Fourth International Conference of Emerging Applications of
Information Technology, pp. 379-384, 2014
[7] N.Akshay, M.Kumar, B.Harish and S. Dhanirkar, “ An Efficient
Approach for Sensor Deployment in Wireless Sensor Networks”,
IEEE International Conference on Emerging Trends in Robotics and
Communication Technologies, pp.350-355, 2010
[8] Tang X, Xu, “Optimizing Lifetime for Continuous Data
Aggregation with Precision guarantees in Wireless Sensor
Networks”, IEEE Transactions Networking, pp. 904-917, 2008
[9] W.B. Heinzelman, A.P Chandrakasan, H.Balakrishnan, “ An
Application Specific protocol Architecture for Wireless
Microsensor Networks”, IEEE Transactions n Wireless
Communication, Vol. 1, pp. 660-670, 2002
[10] S. Gupta, N. Parveen, “Optimum Node Deployment strategy
for Heterogeneous Wireless Sensor Networks by Estimating
Network Lifetime”, IEEE 2nd International Conference on Emerging
Trends in Engineering and Technology, pp. 1119-1122, 2009
ISSN: 2231-5381
[16] A. Ruperee, S. Nema, S. Pawar, “Achieving Energy Efficiency
and Increasing Network Life in Wireless Sensor Network”, IEEE,
International Conference on Communication Workshop, pp. 236240, 2014
[17] F. Kiani, E. Amiri, M. Zamani, “Efficient Intelligent Energy
Routing Protocol in Wireless Sensor Networks”, Hindawi, 2015
[18] Z.W. Siew, A.Kiring, “Energy Efficient Clustering Algorithm
in Wireless Sensor Networks using Fuzzy Logic Control”, IEEE
Colloquim on Humanities, Science and Engineering, pp. 392-397,
2011
[19] A. Thomas, A. Kumar, “Energy efficiency and network
lifetime maximization in wireless sensor networks using ant colony
optimization”, Elsevier, 2012
[20] K.B. Ashwini and G.T. Raju, “Extending Network Lifetime by
Time Constrained Data Aggregation in Wireless Sensor Networks”,
Advances in Intelligent Systems and Computing 243, Springer
India, 2014
http://www.ijettjournal.org
Page 183