An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks
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The document presents a novel energy-efficient min-max optimization (NEMO) scheme aimed at enhancing data delivery and security in wireless sensor networks (WSN). It utilizes a fruit fly optimization (FFO) algorithm to determine optimal paths for data collection, minimizing energy consumption while incorporating RSA security for data integrity during transmission. The proposed system involves creating a virtual grid structure for effective data handling and implementing controlled mobile sink movement for improved network lifetime.
![78 International Journal for Modern Trends in Science and Technology
An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks
B. Energy Consumption of Sensor Node
The sensor nodes operate in the three modes:
sensing, computing and communications. The
sensing unit is entrusted with the responsibility to
detect the physical characteristics of the
environment and an energy consumption that
varies with the hardware nature and applications
[1]. The communication unit consists of a
short-range circuit which performs the
transmission and reception tasks [2].
Communication energy contributes to data
forwarding and it is determined by the
transmission range that increases with the signal
propagation in an exponential way.
The energy consumption model includes the
five states:
Acquisition: The acquisition state includes
sensing, conversion, pre-processing and
eventually storage of these data
Transmission: The transmission state
includes processing, packet forming,
encoding, framing, queuing and base band
adapting to circuits
Reception: This state is responsible for low
noise amplification, down converter oscillator,
filtering, detection, decoding, error detection,
address checking and random reception
Listen: The listen state is similar to a reception
and involves the processes of low noise
amplification, down convertor oscillator,
filtering and terminates at detection
Sleep: The sleep state expends less energy as
compared to the other state
The general approaches to energy conservation in
sensor nodes are as follows:
Duty cycle
Data driven
Mobility
B.1 Duty Cycle
Duty cycle is mainly focused on the networking
subsystem. The most effective energy-conserving
operation is putting the radio transceiver in the
sleep mode whenever communication is not
required. Duty cycle is defined as the fraction of
time nodes are active during their lifetime. As
sensor nodes perform a cooperative task, they need
to coordinate their sleep times. A sleep scheduling
algorithm thus accompanies any duty cycling
scheme. It is typically a distributed algorithm
based on which sensor nodes decide when to do
transition from active to sleep and vice versa.
B.2 Data Driven
Data driven techniques are designed to reduce
the amount of sampled data by keeping the sensing
accuracy within an acceptable level for the
application. Data reduction schemes address the
case of unneeded samples, while energy-efficient
data acquisition schemes are mainly aimed at
reducing the energy spent by the sensing
subsystem. However, some of them can reduce the
energy spent in communication as well. The
techniques aim at reducing the amount of data to
be delivered to the sink node.
B.3 Mobility
Mobility is also useful for reducing energy
consumption. Packets coming from sensor nodes
traverse the network towards the sink by following
a multi-hop path [3]. When the sink is static a few
paths can be more loaded than others, depending
on the network topology and packet generation
rates at the sources [4]. The nodes closer to the
sink also have to relay more packets so that they
are subject to premature energy depletion, even
when techniques for energy conservation are
applied [5]. The traffic flow can be altered if a
designated mobile device makes itself responsible
for data collection.
C. Fruit Fly Optimization
Fruit Fly Optimization is an emerging method
for understanding universal optimization
predicated on the foraging comportment of the fruit
fly [6-8]. The sensory perception of the fruit fly is
better than that of other species, especially the
sense of smell and vision [9]. The olfactory organ of
a fruit fly can collect sundry smells from the air,
and even a victuals source 40 km away.
Afterwards, the fruit fly flies in the food, uses its
acute vision to find the victuals and where its
fellows accumulate, and then it flies in that
direction, as shown in Figure B.2
Figure B.2 Schematic diagram of fruit fly swarm
Food
Iterative evolution
Fruit Fly group
(X, Y)
Fruit Fly 1
(X1, Y1)
Fruit Fly 2
(X2, Y2)
Fruit Fly 3
(X3, Y3)
(0, 0)
Dist1
Dist2
S1=1/ Dist1
S2=1/ Dist2](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-2-320.jpg)
![79 International Journal for Modern Trends in Science and Technology
Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST
D. RSA Security Protocol
RSA security protocol stands for the Rivest,
Shamir and Adleman, who are the creator of the
RSA. RSA is an asymmetric-key security protocol
as it uses two different keys for its encryption and
decryption purpose. It is the most popular and
proven asymmetric key cryptography algorithm. It
generates two keys (private key and public key).
The private key is secret to the user and public key
is known to others who want to communicate with
the user. For this reason it is also known as
public-key cryptography. It is the first algorithm
known to be suitable for signing as well as
encryption, and was one of the first great advances
in public key cryptography. RSA is widely used in
electronic commerce protocols, and is believed to
be secure given sufficiently long keys and the use of
up-to-date implementations.
II. RELATED WORKS
Oh et al [10] proposed a communication protocol
to support sink mobility without global position
information. To reduce the number of cell headers,
we consider multi-hop clusters. Also, to avoid the
location registration of a mobile sink to the whole
cell headers, we use a rendezvous cell header on
which queries of the mobile sink and reporting data
of a source node meet. However, such a manner
also has a data detour problem that the source
node sends data packets to the mobile sink via the
rendezvous cell header. Thus, a scheme is
presented to find a path with less hop counts
between cell headers where the source node and
the mobile sink are located in. Simulation results
show that the proposed protocol is superior to the
existing protocols in terms of the control overhead
and the data delivery hop counts.
Erman et al [11] addressed the data delivery to
mobile sink and source event dynamic conditions
hexagonal grid structure is constructed. Nodes
send data which forwards towards the center cell
from the nearest border line. The data will be
stored and replicated at the nodes which are on the
border line. Sink sends queries towards the center
cell and after reaching particular border line node
that consists data it sends in reverse path. When
sink moves, it informs to both border nodes and
center nodes along the route. The border line cells
and center cells result in more energy dissipation.
Kumar Saurabh et al [12] protected the security
of the sensed data in wireless sensor networks. The
RSA algorithm is used as a digital signature
authentication in the field of security basically
works on deciding encryption variable. In this also
the basic concept is to decide a description variable
and then decide the description variable using
same encryption variable. It is a secure and fast
cryptographic system. The major effort will be
applied to the RSA encryption technique in order to
make node authenticated as well as to secure data
while dealing with aggregation.
Chen et al [13] discussed about the virtual circles
and straight lines are used for virtual structure
construction. The virtual backbone network is
formed by a set of cluster head along with straight
lines and virtual circles. For data collection sink
moves around with straight lines and virtual
circles. For data collection sink moves around the
sensor field and communicates with cluster heads,
which are on the border. The cluster heads
minimize route readjustment by following a set of
rules. The cluster head depletes its energy, fast
because it is placed at the center of the sensor field
and also mainly involve in route readjustment.
Hazim Iscan et al [14] studied a fruit fly
optimization algorithm based on foraging behavior
of the real fruit flies. In order to find the optimum
solution for an optimization problem, fixed
parameters are obtained as a result of manual test
in the fruit fly algorithm. This method is aimed to
find the optimum solution by analyzing the
constant parameter concerning the direction of the
algorithm instead of manual defining on
initialization stage. The study shows an automated
approach for finding the related parameter by
utilizing a grid search algorithm.
Abdo Saif Mohammed et al [15] improved low
energy aware protocol with a novel algorithm to
select cluster heads with highest and balanced
energy in wireless sensor networks with an
authentication protocol to protect our previous
work and network from attackers. This method
uses RSA algorithms to secure the packet during
send to both cluster heads and base station and it
prolongs the lifetime of wireless sensor networks.
Huan Zhao et al [16] presented a novel sensor
deployment scheme based on a fruit fly algorithm
to improve the coverage rate. Each fruit fly
represents a solution for sensor deployment
independently, and they are given the random
direction and distance for finding food using
osphresis. Then find the fruit fly with the highest
smell concentration, judgment value from the fruit
fly group and keep its positions, and then the fruit
fly group will fly towards that position by using
their sensitive vision.](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-3-320.jpg)
![80 International Journal for Modern Trends in Science and Technology
An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks
Khan et al [17] proposed a virtual grid of uniform
size cells is formed by partitioning the sensor field
and the nodes closest to the center of the cell are
appointed as cell headers. Nodes other than cell
headers report data to cell headers. Cell headers
adjust the routes based on the propagation rules
for sink mobility. Mobile sink moves around the
sensor field to collect data periodically. The
disadvantage of this scheme is certain cell headers
take long route to deliver the data to mobile sink
this increases energy consumption.
III. EXISTING SYSTEM
Exploiting the sink’s mobility helps to prolong
the network lifetime, thereby alleviating
energy-hole problems. In the virtual infrastructure
based data dissemination schemes, to minimize
the energy consumption of each individual node
only a set of designated nodes scattered in the
sensor field is responsible to keep track of sink’s
locations such designated nodes gather the
observed data from the nodes in their vicinity
during the absence of the sink and then proactively
or reactively report data to the mobile sink. In order
to minimize the route reconstruction cost only a
limited number of cell-headers previous originating
cell header and downstream of originating cell
header take part in the routes re-adjustment
process. Virtual grid based dynamic route
readjustment scheme gave minimum route
readjustment cost, but it did not consider the
distance priority based routing and which results
in more delay of packet delivery. For that in
proposed work fruit fly optimization is used to
minimize the delay in packet transmission by
considering the shortest path routing in the
network.
IV. PROPOSED SYSTEM
A. Proposed System Architecture
A Novel Energy-efficient Min-max Optimization
(NEMO) scheme is used to provide the energy
efficient data delivery by applying the Fruit Fly
Optimization (FFO) algorithm. FFO have two main
functions called vision and smell function. Vision
function is related to the calculation of minimum
hop count value and the smell function is related to
the maximum value of Fitness Function (FF).
Rivest Shamir and Adleman (RSA) security protocol
is used for the message authentication between the
sources and sink node.
The proposed scheme enables sensor nodes to
maintain nearly optimal routes to the latest
location of a Mobile Sink (MS) with minimal
network overhead. It partitions the sensor field in a
virtual grid of equal sized cells and constructs a
virtual backbone network comprised of all the Cell
Header (CH). Nodes close to the center of the cells
are appointed as CHs, which are responsible for
data collection from member nodes within the cell
and delivering the data to the MS using the virtual
backbone network.
The goal behind such virtual structure
construction is to minimize the routes
re-adjustment cost due to sink mobility so that the
observed data is delivered to the MS in an energy
efficient way. In addition, it also sets up
communication routes such that the end-to-end
delay and energy cost is minimized in the data
delivery phase to the MS. The MS moves along the
periphery of the sensor field and communicates
with the border CHs for data collection. The routes
re-adjustment process is governed by a set of rules
to dynamically cope with the sink mobility.
Using this scheme, only a subset of the CHs
needs to take part in re-adjusting their routes to
the latest location of the MS thereby reducing the
communication cost. Simulation results reveal
decreased energy consumption and faster
convergence when compared to other state-of-the
art.
B. Module Description
The modules of the proposed system are classified
as:
Virtual Structure Construction
Fruit Fly Optimization
RSA Security
B.1 Virtual Structure Construction
In the proposed scheme, the virtual grid
structure is formed by taking a number of sensor
nodes in the field and it is partitioned into several
uniform sized cells and explained as follows
B.1.1 Network Partitioning
The partitioning of sensor field is taken because
of uniform workload on the part of CH nodes which
expand the network lifetime. Cell size is partitioned
from sensor network such that it should satisfy the
range in between sensor node minimum range and
sensor node maximum range as mentioned in the
Table B.1](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-4-320.jpg)
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Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST
Table B.1 Network partition
B.1.2 CH Election
The proposed scheme elects CH in every cell, i.e.
the node which is closest to the midpoint of the cell.
The total number of nodes computes the midpoints
of all the cells by the sensor field’s dimension
knowledge. In election process to reduce
communication cost, the nodes whose distance to
the midpoint of the cell having less threshold will
only take part in the election. The threshold
distance may increase during the election process
if no node is found within the threshold distance.
This election strategy helps in energy conservation
and also elects CH at the appropriate position
within the cell.
B.1.3 Establishing Adjacencies
After every CH election, every CH shares its
status within the cell and slightly outside the cell
boundary. Nodes associate themselves to the
closest one when it receives notification from more
than one CH. Nodes when receive multiple
notifications it shares information to primary CH
about the secondary CH. In this way, neighboring
CHs form adjacencies using gateway nodes is
shown in the Figure B.2.
Figure B.2 Virtual structure after establishing adjacencies
B.2. Fruit Fly Optimization
FFO is one of the swarm optimization algorithm
based on the foraging behavior of fruit fly using the
vision and smell function. FFO is mainly used to
select the optimal path between the sink and
source node. Initially it calculates the distance
between the neighbors CH’s and then takes the
reciprocal of that value as the decision value. After
that for each and every possible path between the
source and sink for a particular position of mobile
sink FF is calculated.
FF is the sum of the decision value along the
particular path and then it applies the two main
functions of fruit flies to the path. From the entire
possible path based on the vision function, it only
selects the minimum hop count path and then by
applying the smell function it calculates the
optimal path based on the FF. Figure 4.3 shows the
calculation of decisive value among the neighbor
CH’s.
Procedure for FFO
Step1: Each and every CH calculates the decision
value among the neighbor CH’s using equations
4.1 and 4.2. The decision value is the reciprocal of
the distance calculated between the current CH
and neighbor CH’s
(4.1)
(4.2)
Step2: For each and every location of mobile sink
the FF is calculated. Initially the location of MS is
at (0, 0)
Step3: Based on the particular location of mobile
sink, each and every CH calculates the FF for its
entire possible path to the current location of
mobile sink
FF=Sum (decision value) [along all the paths]
Step4: Vision function is used to calculate the
minimum hop count value from all possible paths
from source to sink
[Best Vision Possible Path] = min (hop count)
Step5: After the vision function, smell function is
calculated based on the maximum fitness function
for the paths and then decide that path as the best
optimal path
[Best Smell Optimal Path] = max (FF)
Consider the below example in Figure B.4
shows the optimal path selection from the possible
paths using FFO with help of vision and smell
function at each and every location of MS. Table
B.2 shows the selection of optimal path from
No. of
nodes
(N)
Minimum and
Maximum Range
for CH Selection
No. of
CH
(K)
100 1 < N×0.05 ≤ 6 4
200 6 < N×0.05 ≤ 12 9
300 12 < N×0.05 ≤ 20 16](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-5-320.jpg)
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Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST
After sensing data the source node signs the
message using the signature algorithm and then it
forwards the message along with the signature
value to the Corresponding Cell Header (CCH) and
then it verifies the received message with the
verification algorithm, if the message is valid it then
forwards the packet to next CH or else it informs
the source node that the particular CH as
misbehaving node and then the source node
selects the next optimal path using FFO without
having that misbehaving node in the path.
Likewise it repeats the process until it reaches
the sink node. In this way a secure communication
between the sink and the source node is preserved.
Procedure for RSA
Key Generation: Source node generates key
[public (e), private (d)] and broadcast its public key
(e, n) to all other nodes
Signature: Source signs the message M using
signature algorithm (S=Md mod n) with the help of
source’s private key d and generates the sign S
then it forwards (S, M) to the source’s CCH
Verification: CCH verifies the original message (M)
using verification algorithm (M′=Se mod n) using
source’s public key e which generates the verified
message (M′)
If (M′ == M)
It forwards (M,S) to intermediate CH along
the path
It repeats the same process till it reaches
the sink node
else
It drops the packet and informs that the
particular node as misbehaving node
Source node chooses the next optimal
path without having that misbehaving
node
PARAMETER EVALUATION
Novel Energy efficient Min max Optimization
(NEMO) scheme is compared with Virtual Grid
based Dynamic Route Adjustment (VGDRA) were a
common feature between them is the use of a
virtual infrastructure for network operation. Here
three different criteria to evaluate the performance
of VGDRA against VGDRA scheme. The following
are the parameters which are taken into account
for comparison:
Energy
Packet Delivery Ratio
Delay
Energy:
Figure A.1 represents the comparison between
Nodes and Energy. Nodes using NEMO scheme
incur less energy compared to the VGDRA scheme
because of taking the shortest path between the
source and sink. The NEMO scheme, using the
average node energy consumption in
reconstructing the data delivery routes to the latest
location of mobile sink.
The values of power consumption are listed in
Table A.1. Power consumption of the proposed
scheme consumes minimum compared to the
VGDRA scheme. For the different number of nodes
the proposed scheme minimizes energy by 9-12%
when compared to the existing approach.
Figure A.1 Comparing the energy efficiency for different
network sizes
Table 5.1 Nodes Vs Energy
Packet delivery ratio
Figure 5.2 indicates the packet delivery
performance. The packet delivery ratio can be
calculated as the ratio between transmitted and
received for overall packets. NEMO algorithm and
VGDRA are compared with node variation. When
sensor nodes increased from 100 to 300 nodes in
the environment, the packet delivery ratio is
decreased in both the algorithms. But NEMO
scheme has better packet delivery ratio compared
to VGDRA scheme.
Number
of
Nodes
Power Consumption
(Joule)
VGDRA NEMO
100 49.68 44.76
200 95.19 86.41
300 151.24 132.18](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-7-320.jpg)
![84 International Journal for Modern Trends in Science and Technology
An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks
Figure A.2 Comparing the packet delivery for different
network sizes
Table A.2 Nodes Vs Packet delivery ratio
The values of the packet delivery ratio are listed
in Table 5.2. The packet delivery ratio is maximized
compared to the VGDRA scheme. For the different
number of nodes the proposed scheme maximizes
the packet delivery ratio by 4-8% when compared
to the existing approach.
Delay
The delay time is an indirect reflection of the data
delivery efficiency as the more promptly the nodes
come to know about the latest location of a mobile
sink, the most efficient routes they can select in
disseminating the sensed data. Figure A.3
represents the delay time of the NEMO is minimum
compared to VGDRA when the sink is moving at a
speed of 10 m/s.
Figure 5.3 Comparing the delay time for different network
sizes
Table 5.3 Nodes Vs End to end delay
The values for the delay time are listed in Table
A.3. The delay time is minimum compared to the
VGDRA scheme. For the different number of nodes
the proposed scheme minimizes the delay by 3-7%
when compared to the existing approach.
V. CONCLUSION
A novel Fruit Fly Optimization (FFO) with secure
data transmission in wireless sensor network
scheme used for energy efficient data delivery and
also to provide message authentication between
the source and the sink communication. For an
energy efficient data delivery, fruit fly optimization
algorithm uses the vision and smell function to
calculate the optimal path based on shortest path
routing. The FFO used to achieve minimum delay
with more packet delivery ration compared to the
previous work and which leads to less energy
consumption in the network. In order to provide
message authentication between the source and
the sink, The RSA security protocol is used. Data
communication takes place between the source
and sink and it is achieved only through the
intermediate cell header. There is a possibility that
those nodes can also be act as a misbehaving node
in the network. In such cases, RSA identifies those
misbehaving node and chooses the next optimal
path without having that misbehaving node using
the fruit fly optimization algorithm. As a result, it
achieves secured energy efficient data delivery in
wireless sensor networks. In future, analyze the
performance of the proposed scheme at different
sink’s speeds and different data generation rates of
the sensor nodes. The proposed scheme offers a
lightweight solution and does not impose many
constraints on part of the resource constrained
sensor motes, yet its practical implementation on
real hardware needs to be confirmed.
REFERENCES
[1] Qian Liao and Hao Zhu (2013), ‘An Energy Balanced
Clustering Algorithm Based on LEACH Protocol’, In
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Number of
Nodes
Packet Delivery Ratio
(%)
VGDRA NEMO
100 0.812 0.852
200 0.786 0.816
300 0.642
0.694
Number
of Nodes
End to End Delay
(s)
VGDRA NEMO
100 4.49 4.14
200 5.26 4.98
300 6.96 6.77](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-8-320.jpg)
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Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST
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Author Profile:
Dr.S.ANANDAMURUGAN obtained
his Bachelor’s degree in Electrical
and Electronics Engineering from
“Maharaja Engineering College -
Avinashi” under Bharathiyar
University and Masters Degree in
Computer Science and Engineering
from “Arulmigu Kalasalingam
College of Engineering – Krishnan
Koil” under Madurai Kamaraj University. He completed
his Ph.D in Wireless Sensor Networks from Anna
University, Chennai. He has 15 years of teaching
experience. Currently he is working as an Assistant
Professor (Selection Grade) in the department of
Information Technology in Kongu Engineering College,
Perundurai. He is a life member of ISTE, CSI & ACEEE.
He has received “Best Staff” award for the year 2007-08.
He has authored more than 70 books. He has Published
20 papers in International and National Journals and 10
Papers in International and National Conferences. His
area of interest includes Sensor Networks and Green
Computing. He is an Editorial Board Member of the
International Journal of Computing Academic Research
(IJCAR). He has organized 1CSIR sponsored seminar for
the benefit of faculty members and students. He has
attended about 40 Seminars, FDP’s, and Workshops
organized by various Engineering colleges.](https://image.slidesharecdn.com/27-160428075833/85/An-Energy-Efficient-Min-Max-Optimization-with-RSA-Security-in-Wireless-Sensor-Networks-9-320.jpg)
An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks
- 1. 77 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks Dr. S. Anandamurugan Department of Information Technology, Kongu Engineering College, Erode, India Paper Setup must be in A4 size with Margin: Top 1.1 inch, Bottom 1 inch, Left 0.5 inch, Right 0.5 inch, A Novel Energy-efficient Min-max Optimization (NEMO) is proposed to improve the data delivery performance and provide security in WSN. The NEMO scheme is applied in the virtual grid environment to periodically collect the data from source node to the mobile sink through the cell headers. Here the movement of sink is in controlled fashion and collects the data from the border line cell headers. For efficient data delivery Fruit Fly Optimization (FFO) algorithm is applied here to find the best path by using the fitness value calculated between the nodes based on the distance. The optimal path is chosen by first calculating the minimum hop count paths and then finds the maximum of total fitness value along those paths. In that way best path is selected by considering the shortest path which improves the data delivery performance and also it minimizes the energy consumption. The proposed scheme enables the sensor nodes to maintain the optimal path towards the latest location of mobile sink by using the FFO algorithm which leads to maximize the network lifetime in wireless sensor networks. RSA digital signature is used to provide the security between the intermediate nodes during the data delivery. The source node generates the keys and broadcast it to all other nodes in the network. Source node signs the data using its private key and the intermediate nodes verifies the data using the source’s public key which is already broadcasted by the source node. If the data is valid then it forwards to the next intermediate nodes and till the sink node gets the data, forwarding takes place. Else the data packets are dropped and inform that node as misbehaving node and the source chooses the next best path without having that misbehaving node in the path.. KEYWORDS: Energy, optimization, data delivery, FFO, fitness value, RSA, Intermediate nodes, Misbehaving node Copyright © 2015 International Journal for Modern Trends in Science and Technology All rights reserved. I. INTRODUCTION A. Wireless Sensor Network Wireless sensor network is a group of specialized transducers with a communication infrastructure intended to monitor and record conditions at diverse locations. Commonly monitored parameters are temperature, humidity, pressure, wind direction and speed, illumination intensity, vibration intensity, sound intensity, power-line voltage, chemical concentrations, pollutant levels and vital body functions. The application of wireless sensor networks to reduce the effort of human in various environments such as disaster management, intelligent transport system, battle field, healthcare environment and so on. Especially for sink mobility sensor nodes deployed at various points on interest (junctions, car parks, area susceptible to falling rocks) can provide early warning to drivers (mobile sink) in intelligent transport system. The architecture diagram for sensor network is shown in Figure A.1. Figure A.1 Sensor network architecture ABSTRACT
- 2. 78 International Journal for Modern Trends in Science and Technology An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks B. Energy Consumption of Sensor Node The sensor nodes operate in the three modes: sensing, computing and communications. The sensing unit is entrusted with the responsibility to detect the physical characteristics of the environment and an energy consumption that varies with the hardware nature and applications [1]. The communication unit consists of a short-range circuit which performs the transmission and reception tasks [2]. Communication energy contributes to data forwarding and it is determined by the transmission range that increases with the signal propagation in an exponential way. The energy consumption model includes the five states: Acquisition: The acquisition state includes sensing, conversion, pre-processing and eventually storage of these data Transmission: The transmission state includes processing, packet forming, encoding, framing, queuing and base band adapting to circuits Reception: This state is responsible for low noise amplification, down converter oscillator, filtering, detection, decoding, error detection, address checking and random reception Listen: The listen state is similar to a reception and involves the processes of low noise amplification, down convertor oscillator, filtering and terminates at detection Sleep: The sleep state expends less energy as compared to the other state The general approaches to energy conservation in sensor nodes are as follows: Duty cycle Data driven Mobility B.1 Duty Cycle Duty cycle is mainly focused on the networking subsystem. The most effective energy-conserving operation is putting the radio transceiver in the sleep mode whenever communication is not required. Duty cycle is defined as the fraction of time nodes are active during their lifetime. As sensor nodes perform a cooperative task, they need to coordinate their sleep times. A sleep scheduling algorithm thus accompanies any duty cycling scheme. It is typically a distributed algorithm based on which sensor nodes decide when to do transition from active to sleep and vice versa. B.2 Data Driven Data driven techniques are designed to reduce the amount of sampled data by keeping the sensing accuracy within an acceptable level for the application. Data reduction schemes address the case of unneeded samples, while energy-efficient data acquisition schemes are mainly aimed at reducing the energy spent by the sensing subsystem. However, some of them can reduce the energy spent in communication as well. The techniques aim at reducing the amount of data to be delivered to the sink node. B.3 Mobility Mobility is also useful for reducing energy consumption. Packets coming from sensor nodes traverse the network towards the sink by following a multi-hop path [3]. When the sink is static a few paths can be more loaded than others, depending on the network topology and packet generation rates at the sources [4]. The nodes closer to the sink also have to relay more packets so that they are subject to premature energy depletion, even when techniques for energy conservation are applied [5]. The traffic flow can be altered if a designated mobile device makes itself responsible for data collection. C. Fruit Fly Optimization Fruit Fly Optimization is an emerging method for understanding universal optimization predicated on the foraging comportment of the fruit fly [6-8]. The sensory perception of the fruit fly is better than that of other species, especially the sense of smell and vision [9]. The olfactory organ of a fruit fly can collect sundry smells from the air, and even a victuals source 40 km away. Afterwards, the fruit fly flies in the food, uses its acute vision to find the victuals and where its fellows accumulate, and then it flies in that direction, as shown in Figure B.2 Figure B.2 Schematic diagram of fruit fly swarm Food Iterative evolution Fruit Fly group (X, Y) Fruit Fly 1 (X1, Y1) Fruit Fly 2 (X2, Y2) Fruit Fly 3 (X3, Y3) (0, 0) Dist1 Dist2 S1=1/ Dist1 S2=1/ Dist2
- 3. 79 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST D. RSA Security Protocol RSA security protocol stands for the Rivest, Shamir and Adleman, who are the creator of the RSA. RSA is an asymmetric-key security protocol as it uses two different keys for its encryption and decryption purpose. It is the most popular and proven asymmetric key cryptography algorithm. It generates two keys (private key and public key). The private key is secret to the user and public key is known to others who want to communicate with the user. For this reason it is also known as public-key cryptography. It is the first algorithm known to be suitable for signing as well as encryption, and was one of the first great advances in public key cryptography. RSA is widely used in electronic commerce protocols, and is believed to be secure given sufficiently long keys and the use of up-to-date implementations. II. RELATED WORKS Oh et al [10] proposed a communication protocol to support sink mobility without global position information. To reduce the number of cell headers, we consider multi-hop clusters. Also, to avoid the location registration of a mobile sink to the whole cell headers, we use a rendezvous cell header on which queries of the mobile sink and reporting data of a source node meet. However, such a manner also has a data detour problem that the source node sends data packets to the mobile sink via the rendezvous cell header. Thus, a scheme is presented to find a path with less hop counts between cell headers where the source node and the mobile sink are located in. Simulation results show that the proposed protocol is superior to the existing protocols in terms of the control overhead and the data delivery hop counts. Erman et al [11] addressed the data delivery to mobile sink and source event dynamic conditions hexagonal grid structure is constructed. Nodes send data which forwards towards the center cell from the nearest border line. The data will be stored and replicated at the nodes which are on the border line. Sink sends queries towards the center cell and after reaching particular border line node that consists data it sends in reverse path. When sink moves, it informs to both border nodes and center nodes along the route. The border line cells and center cells result in more energy dissipation. Kumar Saurabh et al [12] protected the security of the sensed data in wireless sensor networks. The RSA algorithm is used as a digital signature authentication in the field of security basically works on deciding encryption variable. In this also the basic concept is to decide a description variable and then decide the description variable using same encryption variable. It is a secure and fast cryptographic system. The major effort will be applied to the RSA encryption technique in order to make node authenticated as well as to secure data while dealing with aggregation. Chen et al [13] discussed about the virtual circles and straight lines are used for virtual structure construction. The virtual backbone network is formed by a set of cluster head along with straight lines and virtual circles. For data collection sink moves around with straight lines and virtual circles. For data collection sink moves around the sensor field and communicates with cluster heads, which are on the border. The cluster heads minimize route readjustment by following a set of rules. The cluster head depletes its energy, fast because it is placed at the center of the sensor field and also mainly involve in route readjustment. Hazim Iscan et al [14] studied a fruit fly optimization algorithm based on foraging behavior of the real fruit flies. In order to find the optimum solution for an optimization problem, fixed parameters are obtained as a result of manual test in the fruit fly algorithm. This method is aimed to find the optimum solution by analyzing the constant parameter concerning the direction of the algorithm instead of manual defining on initialization stage. The study shows an automated approach for finding the related parameter by utilizing a grid search algorithm. Abdo Saif Mohammed et al [15] improved low energy aware protocol with a novel algorithm to select cluster heads with highest and balanced energy in wireless sensor networks with an authentication protocol to protect our previous work and network from attackers. This method uses RSA algorithms to secure the packet during send to both cluster heads and base station and it prolongs the lifetime of wireless sensor networks. Huan Zhao et al [16] presented a novel sensor deployment scheme based on a fruit fly algorithm to improve the coverage rate. Each fruit fly represents a solution for sensor deployment independently, and they are given the random direction and distance for finding food using osphresis. Then find the fruit fly with the highest smell concentration, judgment value from the fruit fly group and keep its positions, and then the fruit fly group will fly towards that position by using their sensitive vision.
- 4. 80 International Journal for Modern Trends in Science and Technology An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks Khan et al [17] proposed a virtual grid of uniform size cells is formed by partitioning the sensor field and the nodes closest to the center of the cell are appointed as cell headers. Nodes other than cell headers report data to cell headers. Cell headers adjust the routes based on the propagation rules for sink mobility. Mobile sink moves around the sensor field to collect data periodically. The disadvantage of this scheme is certain cell headers take long route to deliver the data to mobile sink this increases energy consumption. III. EXISTING SYSTEM Exploiting the sink’s mobility helps to prolong the network lifetime, thereby alleviating energy-hole problems. In the virtual infrastructure based data dissemination schemes, to minimize the energy consumption of each individual node only a set of designated nodes scattered in the sensor field is responsible to keep track of sink’s locations such designated nodes gather the observed data from the nodes in their vicinity during the absence of the sink and then proactively or reactively report data to the mobile sink. In order to minimize the route reconstruction cost only a limited number of cell-headers previous originating cell header and downstream of originating cell header take part in the routes re-adjustment process. Virtual grid based dynamic route readjustment scheme gave minimum route readjustment cost, but it did not consider the distance priority based routing and which results in more delay of packet delivery. For that in proposed work fruit fly optimization is used to minimize the delay in packet transmission by considering the shortest path routing in the network. IV. PROPOSED SYSTEM A. Proposed System Architecture A Novel Energy-efficient Min-max Optimization (NEMO) scheme is used to provide the energy efficient data delivery by applying the Fruit Fly Optimization (FFO) algorithm. FFO have two main functions called vision and smell function. Vision function is related to the calculation of minimum hop count value and the smell function is related to the maximum value of Fitness Function (FF). Rivest Shamir and Adleman (RSA) security protocol is used for the message authentication between the sources and sink node. The proposed scheme enables sensor nodes to maintain nearly optimal routes to the latest location of a Mobile Sink (MS) with minimal network overhead. It partitions the sensor field in a virtual grid of equal sized cells and constructs a virtual backbone network comprised of all the Cell Header (CH). Nodes close to the center of the cells are appointed as CHs, which are responsible for data collection from member nodes within the cell and delivering the data to the MS using the virtual backbone network. The goal behind such virtual structure construction is to minimize the routes re-adjustment cost due to sink mobility so that the observed data is delivered to the MS in an energy efficient way. In addition, it also sets up communication routes such that the end-to-end delay and energy cost is minimized in the data delivery phase to the MS. The MS moves along the periphery of the sensor field and communicates with the border CHs for data collection. The routes re-adjustment process is governed by a set of rules to dynamically cope with the sink mobility. Using this scheme, only a subset of the CHs needs to take part in re-adjusting their routes to the latest location of the MS thereby reducing the communication cost. Simulation results reveal decreased energy consumption and faster convergence when compared to other state-of-the art. B. Module Description The modules of the proposed system are classified as: Virtual Structure Construction Fruit Fly Optimization RSA Security B.1 Virtual Structure Construction In the proposed scheme, the virtual grid structure is formed by taking a number of sensor nodes in the field and it is partitioned into several uniform sized cells and explained as follows B.1.1 Network Partitioning The partitioning of sensor field is taken because of uniform workload on the part of CH nodes which expand the network lifetime. Cell size is partitioned from sensor network such that it should satisfy the range in between sensor node minimum range and sensor node maximum range as mentioned in the Table B.1
- 5. 81 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST Table B.1 Network partition B.1.2 CH Election The proposed scheme elects CH in every cell, i.e. the node which is closest to the midpoint of the cell. The total number of nodes computes the midpoints of all the cells by the sensor field’s dimension knowledge. In election process to reduce communication cost, the nodes whose distance to the midpoint of the cell having less threshold will only take part in the election. The threshold distance may increase during the election process if no node is found within the threshold distance. This election strategy helps in energy conservation and also elects CH at the appropriate position within the cell. B.1.3 Establishing Adjacencies After every CH election, every CH shares its status within the cell and slightly outside the cell boundary. Nodes associate themselves to the closest one when it receives notification from more than one CH. Nodes when receive multiple notifications it shares information to primary CH about the secondary CH. In this way, neighboring CHs form adjacencies using gateway nodes is shown in the Figure B.2. Figure B.2 Virtual structure after establishing adjacencies B.2. Fruit Fly Optimization FFO is one of the swarm optimization algorithm based on the foraging behavior of fruit fly using the vision and smell function. FFO is mainly used to select the optimal path between the sink and source node. Initially it calculates the distance between the neighbors CH’s and then takes the reciprocal of that value as the decision value. After that for each and every possible path between the source and sink for a particular position of mobile sink FF is calculated. FF is the sum of the decision value along the particular path and then it applies the two main functions of fruit flies to the path. From the entire possible path based on the vision function, it only selects the minimum hop count path and then by applying the smell function it calculates the optimal path based on the FF. Figure 4.3 shows the calculation of decisive value among the neighbor CH’s. Procedure for FFO Step1: Each and every CH calculates the decision value among the neighbor CH’s using equations 4.1 and 4.2. The decision value is the reciprocal of the distance calculated between the current CH and neighbor CH’s (4.1) (4.2) Step2: For each and every location of mobile sink the FF is calculated. Initially the location of MS is at (0, 0) Step3: Based on the particular location of mobile sink, each and every CH calculates the FF for its entire possible path to the current location of mobile sink FF=Sum (decision value) [along all the paths] Step4: Vision function is used to calculate the minimum hop count value from all possible paths from source to sink [Best Vision Possible Path] = min (hop count) Step5: After the vision function, smell function is calculated based on the maximum fitness function for the paths and then decide that path as the best optimal path [Best Smell Optimal Path] = max (FF) Consider the below example in Figure B.4 shows the optimal path selection from the possible paths using FFO with help of vision and smell function at each and every location of MS. Table B.2 shows the selection of optimal path from No. of nodes (N) Minimum and Maximum Range for CH Selection No. of CH (K) 100 1 < N×0.05 ≤ 6 4 200 6 < N×0.05 ≤ 12 9 300 12 < N×0.05 ≤ 20 16
- 6. 82 International Journal for Modern Trends in Science and Technology An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks possible paths using FFO based on the FF value for all possible paths. Figure B.3 Calculation of decision value among the neighbor nodes Figure B.4.1 Optimal path selections at 1st and 12th position of MS Figure B.4.2 Optimal path selections at 2nd position of MS Figure B.4.3 Optimal path selections at 3rd and 4th position of MS Figure B.4.4 Optimal path selections at 5th position of MS Figure B.4.5 Optimal path selections at 6thand 7th position of MS Figure B.4.6 Optimal path selections at 8th position of MS Figure B.4.7 Optimal path selections at 9th and 10th position of MS Figure B.4.8 Optimal path selections at 11th position of MS RSA Security In the proposed system, RSA security protocol uses the signature and verification algorithm to provide the secure communication between the sink and source node in the networks. Initially the source node generates two different large distinct prime numbers p and q. Then using these two values it generates a public key suite (e, n) and a private key suite (d, n) and then broadcasts its public key in the network of its range. The entire cluster head stores the public key of the source node in its memory.
- 7. 83 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST After sensing data the source node signs the message using the signature algorithm and then it forwards the message along with the signature value to the Corresponding Cell Header (CCH) and then it verifies the received message with the verification algorithm, if the message is valid it then forwards the packet to next CH or else it informs the source node that the particular CH as misbehaving node and then the source node selects the next optimal path using FFO without having that misbehaving node in the path. Likewise it repeats the process until it reaches the sink node. In this way a secure communication between the sink and the source node is preserved. Procedure for RSA Key Generation: Source node generates key [public (e), private (d)] and broadcast its public key (e, n) to all other nodes Signature: Source signs the message M using signature algorithm (S=Md mod n) with the help of source’s private key d and generates the sign S then it forwards (S, M) to the source’s CCH Verification: CCH verifies the original message (M) using verification algorithm (M′=Se mod n) using source’s public key e which generates the verified message (M′) If (M′ == M) It forwards (M,S) to intermediate CH along the path It repeats the same process till it reaches the sink node else It drops the packet and informs that the particular node as misbehaving node Source node chooses the next optimal path without having that misbehaving node PARAMETER EVALUATION Novel Energy efficient Min max Optimization (NEMO) scheme is compared with Virtual Grid based Dynamic Route Adjustment (VGDRA) were a common feature between them is the use of a virtual infrastructure for network operation. Here three different criteria to evaluate the performance of VGDRA against VGDRA scheme. The following are the parameters which are taken into account for comparison: Energy Packet Delivery Ratio Delay Energy: Figure A.1 represents the comparison between Nodes and Energy. Nodes using NEMO scheme incur less energy compared to the VGDRA scheme because of taking the shortest path between the source and sink. The NEMO scheme, using the average node energy consumption in reconstructing the data delivery routes to the latest location of mobile sink. The values of power consumption are listed in Table A.1. Power consumption of the proposed scheme consumes minimum compared to the VGDRA scheme. For the different number of nodes the proposed scheme minimizes energy by 9-12% when compared to the existing approach. Figure A.1 Comparing the energy efficiency for different network sizes Table 5.1 Nodes Vs Energy Packet delivery ratio Figure 5.2 indicates the packet delivery performance. The packet delivery ratio can be calculated as the ratio between transmitted and received for overall packets. NEMO algorithm and VGDRA are compared with node variation. When sensor nodes increased from 100 to 300 nodes in the environment, the packet delivery ratio is decreased in both the algorithms. But NEMO scheme has better packet delivery ratio compared to VGDRA scheme. Number of Nodes Power Consumption (Joule) VGDRA NEMO 100 49.68 44.76 200 95.19 86.41 300 151.24 132.18
- 8. 84 International Journal for Modern Trends in Science and Technology An Energy-Efficient Min-Max Optimization with RSA Security in Wireless Sensor Networks Figure A.2 Comparing the packet delivery for different network sizes Table A.2 Nodes Vs Packet delivery ratio The values of the packet delivery ratio are listed in Table 5.2. The packet delivery ratio is maximized compared to the VGDRA scheme. For the different number of nodes the proposed scheme maximizes the packet delivery ratio by 4-8% when compared to the existing approach. Delay The delay time is an indirect reflection of the data delivery efficiency as the more promptly the nodes come to know about the latest location of a mobile sink, the most efficient routes they can select in disseminating the sensed data. Figure A.3 represents the delay time of the NEMO is minimum compared to VGDRA when the sink is moving at a speed of 10 m/s. Figure 5.3 Comparing the delay time for different network sizes Table 5.3 Nodes Vs End to end delay The values for the delay time are listed in Table A.3. The delay time is minimum compared to the VGDRA scheme. For the different number of nodes the proposed scheme minimizes the delay by 3-7% when compared to the existing approach. V. CONCLUSION A novel Fruit Fly Optimization (FFO) with secure data transmission in wireless sensor network scheme used for energy efficient data delivery and also to provide message authentication between the source and the sink communication. For an energy efficient data delivery, fruit fly optimization algorithm uses the vision and smell function to calculate the optimal path based on shortest path routing. The FFO used to achieve minimum delay with more packet delivery ration compared to the previous work and which leads to less energy consumption in the network. In order to provide message authentication between the source and the sink, The RSA security protocol is used. Data communication takes place between the source and sink and it is achieved only through the intermediate cell header. There is a possibility that those nodes can also be act as a misbehaving node in the network. In such cases, RSA identifies those misbehaving node and chooses the next optimal path without having that misbehaving node using the fruit fly optimization algorithm. As a result, it achieves secured energy efficient data delivery in wireless sensor networks. In future, analyze the performance of the proposed scheme at different sink’s speeds and different data generation rates of the sensor nodes. The proposed scheme offers a lightweight solution and does not impose many constraints on part of the resource constrained sensor motes, yet its practical implementation on real hardware needs to be confirmed. REFERENCES [1] Qian Liao and Hao Zhu (2013), ‘An Energy Balanced Clustering Algorithm Based on LEACH Protocol’, In Proceedings of the 2nd International Conference On Number of Nodes Packet Delivery Ratio (%) VGDRA NEMO 100 0.812 0.852 200 0.786 0.816 300 0.642 0.694 Number of Nodes End to End Delay (s) VGDRA NEMO 100 4.49 4.14 200 5.26 4.98 300 6.96 6.77
- 9. 85 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST Systems Engineering and Modeling (ICSEM-13), pp.1272-1280. [2] Sohail Jabbar and Abid Ali Minhas (2015), ‘Energy Efficient Strategy for Throughput Improvement in Wireless Sensor Networks’, Journal on sensor Computing for Mobile Security and Big Data Analytics, Vol.15, No.2, pp.2473-2495. [3] M. Di Francesco, S. K. Das, and G. Anastasi (2011), ‘Data collection in wireless sensor networks with mobile elements’, Journal on Sensor Networks, Vol. 8, No. 1, pp. 1–31. [4] Hamida E B and Chelius G (2008), ‘A line based data dissemination protocol for wireless sensor networks with mobile sink’, International Conference on Computer Communication, pp.2201-2205. [5] Kinalis, S. Nikoletseas, D. Patroumpa, and J. Rolim (2014), ‘Biased sink mobility with adaptive stop times for low latency data collection in sensor networks’, Journal on Sensor, Vol. 15, No.8, pp. 56–63. [6] Pratyay Kuila and Prasanta Jana (2014), ‘Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach’, Journal on Communication System, Vol.18, No.6, pp.1016-1025. [7] Baskaran M and Sadagopan C (2015), ‘Synchronous Firefly Algorithm for Cluster Head Selection in WSN’, Journal on Sensors Networks, Vol.79, No.6, pp.780-789 [8] Rajeev Kumar and Dilip Kumar (2015), ‘Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks’, Journal on Sensors, Vol.10, No.5, pp.1155-1174. [9] Pan, W.T. (2011), ‘A New Evolutionary Computation Approach: Fruit Fly Optimization Algorithm’, Conference on Digital Technology and Innovation Management. [10]Oh S and Lee E. (2010), ‘Communication scheme to support sink mobility in multi-hop clustered wireless sensor networks’, In Proceedings of IEEE International Conference on Advanced Information Networking and Applications, pp.866-872. [11]Erman A and Dilo A (2012), ‘A virtual infrastructure based on honeycomb tessellation for data dissemination in multi-sink mobile wireless sensor networks’, Journal on EURASIP, Vol.12, No.17, pp.1-54. [12]Kumar Saurabh and Sukhpreet Singh (2012), ‘Providing Security in Data Aggregation using RSA Algorithm’, Journal on Computers and Technology, Vol.3, No.1, pp.2277. [13]Chen T S and Tsai H W (2013), ‘Geographic converge cast using mobile sink in wireless sensor networks’, Journal on Computer Technologies, Vol.36, No.4, pp.445–458. [14]Hazim Iscan and Mesut Gunduz (2014), ‘Parameter Analysis on Fruit Fly Optimization Algorithm’, Journal on Computer and Communications, Vol.5, No.2, pp.137-141. [15]Abdo Saif Mohammed and Shanmukhaswamy M N (2014), ‘A Novel Sensor Deployment Approach Using Fruit Fly Optimization Algorithm in Wireless Sensor Networks’, Journal on Computer Science and Information Technologies, Vol.5, No.4, pp.4880-4885. [16]Huan Zhao and Yan Wang (2015), ‘A Novel Sensor Deployment Approach Using Fruit Fly Optimization Algorithm in Wireless Sensor Networks’, Journal on Computer Science, Vol.10, No.6, pp.1292-1297. [17] Khan A W and Abdullah A H (2015), ‘VGDRA: A Virtual Grid-Based Dynamic Routes adjustment Scheme for Mobile Sink-Based Wireless Sensor Networks’, Journal on Computer and Communication, Vol.15, No.1, pp.526-534. Author Profile: Dr.S.ANANDAMURUGAN obtained his Bachelor’s degree in Electrical and Electronics Engineering from “Maharaja Engineering College - Avinashi” under Bharathiyar University and Masters Degree in Computer Science and Engineering from “Arulmigu Kalasalingam College of Engineering – Krishnan Koil” under Madurai Kamaraj University. He completed his Ph.D in Wireless Sensor Networks from Anna University, Chennai. He has 15 years of teaching experience. Currently he is working as an Assistant Professor (Selection Grade) in the department of Information Technology in Kongu Engineering College, Perundurai. He is a life member of ISTE, CSI & ACEEE. He has received “Best Staff” award for the year 2007-08. He has authored more than 70 books. He has Published 20 papers in International and National Journals and 10 Papers in International and National Conferences. His area of interest includes Sensor Networks and Green Computing. He is an Editorial Board Member of the International Journal of Computing Academic Research (IJCAR). He has organized 1CSIR sponsored seminar for the benefit of faculty members and students. He has attended about 40 Seminars, FDP’s, and Workshops organized by various Engineering colleges.