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Hemant kumar Damor, Jitendra S. Dhobi and Kartik N. Shah / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp.1810-1813
1810 | P a g e
Energy Optimization in Wireless Sensor Networks
Hemant kumar Damor1
, Jitendra S. Dhobi2
and Kartik N. Shah3
1, 2
Computer Engineering Department Govt. Engineering College, Modasa
3
School of Computing science and engineering, VIT University, Vellore.
Abstract
Wireless Sensor Nodes are generally
having less memory and low battery life. Due to this
constraint, we need a strong algorithm by which we
can reduce the energy consumption. The main
energy is utilized during sending of the data. Some
part of energy is utilized in processing the data. In
this paper, we will give another approach for
reduced energy consumption. We will consider the
cost of sending as well as processing. So, we will use
short distance path as well as compression of the
data to reduce the power consumption. Finally we
will simulate this scenario by making a small
simulator for 4 nodes.
Keywords—Wireless Sensor Network, Energy
optimization, compression, efficiency
I. INTRODUCTION
From recent years, it is found that the area of
wireless network and mobile computing is evolved
rapidly. When we compare Wireless network with
wired networks, we have benefit with wireless
networks as they are capable of scaling easily, rapidly
deployable and most importantly cost effective.
Sensor nodes are low-power devices that is having
inbuilt functionality of sensing, small amount of
computing and wireless communication. It is
expected that the cost of producing the sensor node is
negligible. A simple transceiver is equipped to
transmit and receive measurements from neighbours.
The microcontroller performs the data processing
task and is responsible for data functionality in sensor
node. These sensor nodes are generally used to sense
light, temperature, sound, position, vibration,
pressure, stress etc. We can define wireless sensor
network as aggregating different sensor nodes into
computation and dedicated communication
infrastructures. Wireless Sensor Networks (WSNs)
are new era of networking system. It deals with scale
and density, which is very hard in the ideal
environments. Sensor networks are made with the
intention of monitoring the physical world. So, they
are often deployed in uncontrolled and natural
environments. Wireless sensor networks must be
designed to operate while unthread (no external
power), unattended (no manual configuration or
management), intermittently connected (radios may
be turned off for substantial periods of time to
conserve power), and uncontrolled environment [1].
Figure 1 : Components of sensor node [1]
The following figure shows the typical
sensor node which is very small like coin and with
limited power and memory.
Figure2: Typical sensor node
WSNs are made up of integrate general-
purpose computing with heterogeneous sensing nodes
and wireless communication and low power sensor.
The power unit is one of the most important
components of a sensor node [2].A WSN can limit
the radio frequency channel, due to, that is to say,
unstable links, limit of physical protection of each
sensor node, actual of each nodes connection,
variation topology in addition dangerousness about
routing security is high by activity spite nodes. The
restrictions in the hardware of the sensor nodes,
makes it difficult to guarantee the maintenance of
security because of its vulnerability.[3][4].A WSN is
consists of spatially distributed autonomous sensors
together monitor physical or environmental
conditions. A sensor network normally makes a
wireless ad-hoc network, that is, each sensor is
deployed with a multi-hop routing algorithm. The
network does not have any already existing
infrastructure, such as routers in wired networks or
access points in managed (infrastructure) wireless
networks.
II. RELATED WORK

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The challenges in deployment of WSNs are
as follows [14-16][17][18]:
1. The WSNs are used for not only collecting
environmental information but it also used for
changing the environment information.
2. The resources are limited and memory is less. It
should manage all the complex functionality
using different topologies.
3. There are many uncertainties in WSNs. They can
affect the functionality of WSN.
4. One of the big challenges of WSN is to make the
node as self-organizing and self-optimizing.
5. There can be a large number of WSN nodes but
network must have limited number of nodes that
guarantee the desired WSN service.
6. WSN operates in the real world. So, it must have
real-time features as per need.
7. WSNs are not safe because it uses free band. So,
there must be some security for managing
original data.
There are lot of techniques and protocols
available for optimizing energy used by sensor nodes.
The categories are mainly categorized into following
types.
1. MAC layer techniques
2. Network layer approaches
3. Transmission control approaches
4. Automatic approaches
A. MAC layer approaches
The main portion of the node's energy is
spent on radio transmission and on listening the
medium for message [5]. MAC protocols manage
communication and regulate the shared medium such
that performance is improved. For example, Zigbee
technology uses MAC protocol for less energy
consumption. The TDMA MAC protocol is based
upon cross layer optimization based upon Physical
layers and MAC [13].
B. Network layer (Routing) approaches
The main objective of WSNs application is
to gather the data from nodes and transfer the data to
sink in energy efficient way using proper routing
protocol. REACA , EARQ, MMSPEED, Energy
Efficient Broadcast Problem (EEBP) and Green
Wave Sleep Scheduling (GWSS) are some of the
algorithm used for less energy consumption at
network layer [6-9].
C. Transmission Control approaches
There are many Transmission Power
Control (TPC) approaches available. Its main goal is
to reduce the energy consumption and improve the
channel capacity. TPC solutions work with single
transmission power for whole network.One of the
algorithm is Power Control Algorithm with Back
Listing (PCBL), in this algorithm, each node transmit
the packet with different transmission power levels to
find optimal transmission power based on Packet
Reception Ratio (PRR) [10]. Local Mean
Algorithm(LMA) and Equal Transmission Power
(ETP) are other approaches used at this layer.
D. Automatic approaches
Autonomic computing was introduced by
IBM in 2001 to describe the systems that are self-
manageable [11].The main properties with are [12]:
Self- configuration: It concern with system's ability to
configure by itself for achieving high level goals.
Self-optimization: It concern with the change in
system pro-actively to optimize the performance or
quality of service.
III. PROPOSED APPROACH
There are many approaches available to
optimize the energy as discussed in previous work
section. Our approach comes under the category of
Network layer (Routing) approaches. The proposed
approach is basically divided into 2 steps. First step
deals with the routing part and second step deals with
the compression-decompression part. Both the steps
when combined, can give better result and benefits of
reliability and also less energy consumption. Our
main objective is to reduce the energy consumed by
the sensor node to have good battery life and also
provide reliability of data. Each step is explained in
details in following sub-sections.
Step1: Routing based upon the cost
The routing plays important role in design of
Wireless networks because the data transfer path is
most important to save energy. If the data follow the
wrong path then it waste the energy of the nodes
which comes on that path as well as waste the time.
So, we can consider the network as a graph where
each node are sensor nodes and the cost of the edge is
cost for transferring data from 1 node to another. It
can be anything like distance or energy consumption.
Here, we will use Floyd-Warshall algorithm to find
the shortest distance between node and destination
via intermediate nodes. So, the route will be shortest
route and the data can be transferred on that path so
that less energy will be utilized. In case, when the
battery life of the node which comes in shortest path
is completed then the distance will become infinite.
So, another shortest route will be calculated based
upon the existing scenario. It will increase the
flexibility and reliability of the data. Floyd-Warshall
algorithm is as follows :
Algorithm 1: Floyd Warshall Algorithm [19]

1812 | P a g e
Algorithm 2: Floyd Warshall Algorithm for Path
Reconstruction [19]
After calculating shortest path, we can go
for transmission of data. Now we can save more
energy by sending data via shortest path. But we also
require that it should take less energy for
transmission of data. To reduce the energy utilized
during transmission can be reduced by using
compression-decompression. It is discussed in the
step2.
Step2: Compression- Decompression
Before sending the data, we can compress it
to save the energy of the transmitting node as well as
intermediate nodes. It can be achieved by using
binary transfer. For example, there are 4 nodes and
they capture the temperature of the region and send it
to sink node via shortest path. In this case, we can
have 2 bits to represent each nodes (node 1 : 00, node
2: 01, node 3: 10 and node 4 : 11 ). If we take it as
integer then it will take 2 bytes of size. Now, if we
want to send the temperature captured by the node,
we can convert the value in binary. As the node is a
physical device, it cannot stay in temperature more
than 64 degree Celsius. If the temperature goes
beyond the 60 degree, we can say it as fire in that
region. To represent the temperature, we need 6 bits
of size (2^{6} = 64). Total we require 8 bits (2+6). It
is of 1 byte data. So, the data is compressed from 4
byte to 1 byte and we can transfer this information to
destination sink. It will save energy of intermediate
nodes also. On other side, destination sink will check
first 2 bits to check which node sent the data and
remaining 6 bits for checking the temperature of that
region. It is part of decompression process.
IV. IMPLEMENTATION
Above 2 steps process can be achieved by
creating a simulator. The simulator's work will be
divided into above mentioned 2 steps. It can be
implemented using c# language in .NET technology.
The design of the simulator can be as shown in figure
2.
Figure 2: Implementation
In above figure, each nodes (Node A, Node
B, Node C and Node D) are capable of capturing
temperature (in this simulator, it will generate
random temperature). Each textbox will take input as
cost between nodes. Distance button will calculate
the shortest path from each node to destination node
via intermediate node. While the transfer button will
do the functionality of step2 mentioned above. It will
transfer the data from individual node to destination
node via shortest path.
V. CONCLUSIONS
From the above discussion, we can conclude
that the two step procedure proposed in this paper
will save 75% of energy of each node (In case of 4
nodes) in step 2 only.It can be shown as follows:
Before Compression:-
Data for transfer : 2 bytes for storing the
address of node + 2 bytes to store the temperature = 4
bytes
After Compression:-
Data for transfer : 2 bits for identity of node
+ 6 bits for temperature = 8 bits = 1 byte.
Energy saved = 4 byte - 1 byte = 3 byte = 75%Also,
we can save some amount of energy in step1. When
we combine both the steps, it will save too much
energy. One more benefit from the step1 is that it can
work for the case when one of the nodes from the
route is dead. In that case, it can reconstruct the path.
VI. FUTURE WORK
In our case, we have studied the algorithms
for 4 nodes, we can enhance the implementation for

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more than 4 nodes as well. We can have more other
approaches which can be more cost effective. Also
we can use some other algorithms for compression
and decompression. But we must keep in mind that
compression and Decompression also requires energy
to process it. So, the algorithm must be less complex
so that more energy is not wasted on processing the
data compared to sending the data. We can have
modification in hardware to have some more efficient
technique. We can take energy available in the nodes
which comes along with the path as 1 parameter for
comparison.
ACKNOWLEDGMENT
Authors would like to thank the Computer
department of Government Engineering College,
Modasa and VIT University for providing the
resources to carry out this work. Author would also
like to thank Gujarat Technological University to
motivate us to work.
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[19]
http://en.wikipedia.org/wiki/Floyd%E2%80
%93Wars- hall_algorithm

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