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International Journal of Advanced Research in Management, Architecture, Technology and
Engineering (IJARMATE)
Vol. 1, Issue 1, August 2015
16
All Rights Reserved © 2015 IJARMATE
Efficient Energy Management Routing in WSN
A.Nasrin Banu
1
, M.Manju
2
, S.Nilofer
3
, S.Mageshwari
4
, A.Peratchi Selvi
5
, Christo Ananth
6
U.G. Scholars, Department of ECE, Francis Xavier Engineering College, Tirunelveli
1,2,3,4,5
Associate Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli
6
Abstract— In this proposal, a neural network
approach is proposed for energy conservation routing in
a wireless sensor network. Our designed neural network
system has been successfully applied to our scheme of
energy conservation. Neural network is applied to predict
Most Significant Node and selecting the Group Head
amongst the association of sensor nodes in the network.
After having a precise prediction about Most Significant
Node, we would like to expand our approach in future to
different WSN power management techniques and
observe the results. In this proposal, we used arbitrary
data for our experiment purpose; it is also expected to
generate a real time data for the experiment in future and
also by using adhoc networks the energy level of the node
can be maximized. The selection of Group Head is
proposed using neural network with feed forward
learning method. And the neural network found able to
select a node amongst competing nodes as Group Head.
Index Terms— Neural network, WSN, adhoc network
I. INTRODUCTION
A Wireless Sensor Network (WSN) contains hundreds
or thousands of these sensor nodes. These sensors have the
ability to communicate either among each other or directly to
an external base-station (BS). A greater number of sensors
allows for sensing over larger geographical regions with
greater accuracy. Figure 1.1 shows the schematic diagram of
sensor node components. Basically, each sensor node
comprises sensing, processing, transmission, mobilizer,
position finding system, and power units (some of these
components are optional like the mobilizer). The same figure
shows the communication architecture of a WSN. Sensor
nodes are usually scattered in a sensor field, which is an area
where the sensor nodes are deployed. Sensor nodes
coordinate among themselves to produce high-quality
information about the physical environment. Each sensor
node bases its decisions on its mission, the information it
currently has, and its knowledge of its computing,
communication, and energy resources. Each of these scattered
sensor nodes has the capability to collect and route data either
to other sensors or back to an external base station(s). A
base-station may be a fixed node or a mobile node capable of
connecting the sensor network to an existing communications
infrastructure or to the Internet where a user can have access
to the reported data. Networking unattended sensor nodes
may have profound effect on the efficiency of many military
and civil applications such as target field imaging, intrusion
detection, weather monitoring, security and tactical
surveillance, distributed computing, detecting ambient
conditions such as temperature, movement, sound, light or the
presence of certain objects, inventory control, and disaster
management. Deployment of a sensor network in these
applications can be in random fashion (e.g., dropped from an
airplane) or can be planted manually (e.g., fire alarm sensors
in a facility). For example, in a disaster management
application, a large number of sensors can be dropped from a
helicopter. Networking these sensors can assist rescue
operations by locating survivors, identifying risky areas, and
making the rescue team more aware of the overall situation in
the disaster area.
In the past few years, an intensive research that
addresses the potential of collaboration among sensors in data
gathering and processing and in the coordination and
management of the sensing activity were conducted.
However, sensor nodes are constrained in energy supply and
bandwidth. Thus, innovative techniques that eliminate energy
inefficiencies that would shorten the lifetime of the network
are highly required. Such constraints combined with a typical
deployment of large number of sensor nodes pose many
challenges to the design and management of WSNs and
necessitate energy-awareness at all layers of the networking
protocol stack. For example, at the network layer, it is highly
desirable to find methods for energy-efficient route discovery
and relaying of data from the sensor nodes to the BS so that
the lifetime of the network is maximized.
The most important application [1] of neural
networks in WSNs can be summarized to sensor data
prediction, Sensor fusion, path discovery, sensor data
classification and nodes clustering which all lead to less
communication cost and energy conservation in
WSNs.Neural Network based methods can be according to
neural network topologies that applied such as Self
Organizing maps, Back propagation neural networks,
recurrent neural networks Radial Basis Functions etc. Self
Organizing Map neural networks . Main advantage of this
paper is Low communication costs and energy conservation
and Reduction of energy consumption in sensor node after
deployment and designing, Prediction of sensor node. But it
srequires continuous monitoring and less applicable in dense
area.
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International Journal of Advanced Research in Management, Architecture, Technology and
Engineering (IJARMATE)
Vol. 1, Issue 1, August 2015
17
All Rights Reserved © 2015 IJARMATE
Wireless ad-hoc sensor networks [2] due to their
abilities are being rapidly developed to collect data across the
area of deployment.the collected data and facilitate
communication protocols, it is necessary to identify the
location of each sensor. localization algorithms use
trilateration or multilateration based on range measurements
obtained from RSSI,TOA,TDOA and AOA. This paper deals
with localization techniques in ad-hoc wireless networks,
where anchors and unknown nodes are randomly positioned
in a uniform distribution in a squared area. We have proposed
a localization method that with use of probabilistic Neural
network estimates the location of unknown nodes. we can
reduce calculations and energy consumption with the help of
independent Component Analysis by removing some
unnecessary anchor nodes. A PNN can estimate the location
of unknown nodes, properly and with the help of ICA we can
reduce calculations and therefore energy consumption by
about 43 percent in dense networks.
Due to use of RSSI the hardware of nodes are
simpler and cheaper.One of the advantage of using this
algorithm is its simple calculations that can easily be done in
every node which has a simple microcontroller. Its noise
sensitivity is much less than many other approaches. With the
help of ICA, energy consumption and calculation will
decrease and it make network simpler.
Wireless sensor Networks [3] are design with energy
constraint.Energy attempt is being made to reduce the energy
consumption of the wireless sensor node. Communication
amongst nodes consumes the largest part of the energy. The
paper focuses on use of classification techniques using neural
network to reduce the data traffic from the node and there by
reduce energy consumption. The sensor data is classified
using ART1 Neural Networks Model. Wireless sensor
network populates distributed nodes.
Directed diffusion routing protocol is implemented
to carry out performance comparison.this paer focuses on
classification techniques using ART1 neural network models.
Lifetime improvement is carried out in both routing
techniques.The sensor network is populated with 50
nodes.communication over the network is carried out by
cooperative routing in one case and with difiusion routing it is
used for randomization and determines the network topology.
Many sensor network routing protocols have been
proposed, but none of them have been designed with security
as a goal. Security goals are proposed [4] for routing in sensor
networks, show how attacks against ad-hoc and peer-to-peer
networks can be adapted into powerful attacks against sensor
networks, introduce two classes of novel attacks against
sensor networks inkholes and hello floods, and analyze the
security of all the major sensor network routing protocols. We
describe crippling attacks against all of them and suggest
countermeasures and design considerations. This is the first
such analysis of secure routing in sensor networks. We
present crippling attacks against all the major routing
protocols for sensor networks. Because these protocols have
not been designed with security as a goal, it is unsurprising
they are all insecure. We use the term sensor network to refer
to a heterogeneous system combining tiny sensors and
actuators with general purpose computing elements. Sensor
networks may consist of hundreds or thousands of low power,
low-cost nodes, possibly mobile but more likely at fixed
locations, deployed en masse to monitor and affect the
environment. For the remainder of this paper we assume that
all nodes locations are fixed for the duration of their lifetime.
Wireless sensor networks [5] consists of small nodes
with sensing , computation and wireless communication
capabilities. many routing , power management and data
dissemination protocols have been specifically designed for
WSns where energy awareness is an essential design issue.
The routing protocols which might differ depending on the
application and network architecture. These protocols can be
classified into multipath-based, query-based,
negotiation-based, QoS-based and coherent-based depending
on the protocol operation.We study the trade-off between
energy and communication overhead savings in every routing
paradigm.
II. EXISTING
SYSTEM
Energy management is important to the reliability of
the network. The nature of the application may make it
infeasible for interaction with the sensor once it has been
deployed. Frequently the sensors are located in remote areas
making it impossible to access them. Smart dust nodes are
designed to be disposable, making it more cost effective to
deploy additional new nodes rather than replace batteries in
existing nodes. Many wireless sensor applications require the
sensors to be operational for many years. It is thus essential
that the sensors are reliable and work on their own for the
duration of the application. If the sensor loses power, it is
gone and so is the reliability of the network. Energy
management techniques include those that reduce
communication and increase computation, power down
certain components of the node or the entire node, nodes that
cover smaller areas, and renewable sources of energy. The
desire to save energy has also affected routing algorithms,
scheduling, data collection and aggregation and MAC
(Medium Access Control) protocol research. The tradeoff
between energy savings and latency are of major concern.
Some time critical applications cannot tolerate delay in packet
delivery. The lifetime of the network can only increase by
preserving the energy in the sensor nodes. Number of
techniques has been evolved to increase the lifetime of the
wireless sensor network. Since most of the energy
consumption of each node is due to sensing and routing
operations, many of the proposed techniques try to optimize
these two tasks. Some approaches update the routing path
when a sensor node in a path is low in energy thus that they
would exclude the node from the routing path and preserve its
energy.
Many techniques such as MCFA, GBR and Rumor routing
use the shortest path method to reduce the communication and
energy consumption. Many of WSN management techniques
use an agent-based method to manage the wireless sensor
network and its energy consumption. It is monitored for the
network resources continuously.
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International Journal of Advanced Research in Management, Architecture, Technology and
Engineering (IJARMATE)
Vol. 1, Issue 1, August 2015
18
All Rights Reserved © 2015 IJARMATE
III.
PROPOSED SYSTEM
There are two methods suggested here for energy
efficient routing in WSNs. First is Most Significant Sensor
Node prediction and another is Group Head selection. Now,
we discuss both of these problems in any WSN and seek
possible solutions using neural networks, which will actually
use to determine the shortest routing path in any WSN for
minimizing the energy consumption. Selecting Group Heads
amongst all the nodes is also energy conserving scheme for a
WSN is proposed herewith. Sensor nodes are initially
powered by batteries with full capacities. Each sensor collects
data which are typically associated with other sensors in its
neighborhood, and then the associated data is sent to the Base
Station through Group Head for evaluating the tasks more
efficiently. Assuming the periodic sensing of same period for
all the sensors and Group Head is selected. Inside each fixed
group of nodes, a node is periodically elected to act as Group
Head through which communication to/from Group nodes
takes place.
There are two methods suggested here for energy efficient
routing in WSNs. First is Most Significant Sensor Node
prediction and another is Group Head selection. Now, we
discuss both of these problems in any WSN and seek possible
solutions using neural networks, which will actually use to
determine the shortest routing path in any WSN for
minimizing the energy consumption.
Fig.1. System Architecture
Usually WSNs life-time ends by having a single sensor
node which uses all its energy and the other sensors
consuming the remaining energy. This sensor (which is the
cause of the networks end of lifetime) is most likely located in
a very significant sensor node which always is in the routing
path of many nodes to the base station. By predicting these
Significant nodes, it is possible to allocate tasks to the nodes
in a more efficient way and thus increase the lifetime of the
network. In order to predict WSN.s most significant nodes,
we propose a method based on Neural Networks. With it we
would be able to know the energy level finally at the last of a
WSN.s life time also we can be able to conclude that which
node is consuming more energy. Such nodes which are
blocking most of the energy in the network are the most
significant nodes of the network.
Selecting Group Heads amongst all the nodes is also energy
conserving scheme for a WSN is proposed herewith. Sensor
nodes are initially powered by batteries with full capacities.
Each sensor collects data which are typically associated with
other sensors in its neighborhood, and then the associated data
is sent to the Base Station through Group Head for evaluating
the tasks more efficiently. Assuming the periodic sensing of
same period for all the sensors and Group Head is selected.
Inside each fixed group of nodes, a node is periodically
elected to act as Group Head through which communication
to/from Group nodes takes place.
In order to predict Most Significant Node (MSN) in a WSN
we are depicting a set of input patterns for a five layered feed
forward neural network. These input patterns belong to one
wireless sensor node and by using them as the inputs of the
neural network we can predict the energy level of the sensor at
the last of WSN.s lifetime. These patterns may be in the form
of features coded from
-Sensor node’s distance from sink,
-Sensor node’s distance from the neighboring border,
-Sensors number of neighbors, the number of neighbors
which initially route their data through this sensor. The neural
network can be trained with different network parameters.
Thus, if the neural network be executed for each one of WSN
at the start of the WSN.s lifetime it would be possible to
predict the Most Significant Sensor nodes of the WSN. The
result of this prediction is dependent of initial energy
management scheme followed by the WSN. For example if in
a WSN management algorithm the energy of those nodes
which are located at the corners of the sensing field is mostly
used, after successful training, the network would be able to
understand this behavior of the algorithm and then it can
predict that the final energy level of the nodes at the corner of
the sensing environment must be the lowest to conserve the
overall energy of the system.
Fig.2. Creation of node and deployment in an area
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International Journal of Advanced Research in Management, Architecture, Technology and
Engineering (IJARMATE)
Vol. 1, Issue 1, August 2015
19
All Rights Reserved © 2015 IJARMATE
Fig.3. Sensing of data to nearer nodes with sensing of maximum energy
Fig.4. Transmission of data from node to cluster head.
IV. CONCLUSION
In this proposal, a neural network approach is proposed for
energy conservation routing in a wireless sensor network. Our
designed neural network system has been successfully applied
to our scheme of energy conservation. Neural network is
applied to predict Most Significant Node and selecting the
Group Head amongst the association of sensor nodes in the
network. After having a precise prediction about Most
Significant Node, we would like to expand our approach in
future to different WSN power management techniques and
observe the results. In this proposal, we used arbitrary data for
our experiment purpose; it is also expected to generate a real
time data for the experiment in future and also by using adhoc
networks the energy level of the node can be maximized. The
selection of Group Head is proposed using neural network
with feed forward learning method. And the neural network
found able to select a node amongst competing nodes as
Group Head.
R
EFERENCES
[1] Chunhung Richard Lin And Mario Gerla, Adaptive Clustering For
Mobile Wireless Networks. IEEE Journal On Selected Areas In
Communications, Vol. 15, No. 7, September 2013.
[2] Dehni L, Kief F, Bennani Y. Power Control and Clustering in
Wireless Sensor Networks. In:Proceedings of Med-Hoc-Net 2013
[3] Demin Wang, Bin Xie, Dharma P. Agrawal, Coverage And
Lifetime Optimization Of Wireless Sensor Networks With
Gaussian Distribution. IEEE Transactions On Mobile Computing,
Vol. 7, No. 12, December 2011.
[4] Gaurav S. Kasbekar, Yigal Bejerano, and Saswati Sarkar, Lifetime
And Coverage Gurantees Through Distributed Coordinate-Free
Sensor Activation. IEEE/ACM Transactions On Networking, Vol.
19, No. 2, April 2011.
[5] Heinzelman W, Chandrakasan A, Balakrishnan
H.Application-specific protocol architecture for wireless
microsensor networks. In: IEEE Transactions on Wireless
Communications,2007.
