This paper proposes a bio-inspired clustering algorithm based on the bacteria foraging optimization (BFO) approach to enhance the energy efficiency and network lifetime of wireless sensor networks (WSNs). It discusses various routing protocols, issues related to deployment, energy consumption, and optimal clustering strategies to improve signal strength and reduce data loss. The results from simulations indicate that the BFO algorithm performs better than traditional clustering methods like LEACH and K-means in terms of energy dissipation and overall network performance.

1. 9 ICRTEDC -2014
IJEEE, Vol. 1, Spl. Issue 2 (May, 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
EFFECTIVE AND SECURE DATA GV/ICRTEDC/02
COMMUNICATION IN WSNs
CONSIDERING TRANSFER MODULE
OF NODES
1
Dr. Dinesh Arora, 2
Dr.Hardeep Singh
1
Gurukul Vidyapeeth Institute of Engg. & Technology, Banur, Punjab, India
2
Indo Global College of Engg. & Technology, Mohali, Punjab, India
1
drdinesh169@gmail.com, 2
hardeep_saini17@yahoo.co.in
ABSTRACT: Wireless network which allows users to
access information and service regardless of their
geographic position. These networks are highly distributed
networks of small wireless nodes, monitors the
environment or system by measuring physical parameters
such as temperature, pressure. A network consists of
numbers of nodes with one as a source and one as a
destination. Data loss, high energy consumption, reduction
in signal strength and interferences in data were various
factors which incorrupt the transmission in Wireless
sensor networks. A Bio-inspired clustering algorithm
based on BFO has been proposed and investigation on
energy efficient clustering algorithms related to WSNs has
been done in this paper. The contribution of this paper
related to use of Bacteria foraging algorithm firstly for
WSNs for enhancing network lifetime of sensor nodes.
Keywords: Wireless Sensor Network, Sensor Nodes,
Routing in WSNs, BFO;
1. INTRODUCTION
Wireless network which allows users to access
information and service regardless of their geographic
position provides advantage in terms of cost factor,
flexibility, size and power consumption as it can operate in
wide range of environment without the limitation of
physical media when compared with wired technology. In
wireless technology, message can be forwarded over
multiple hops. Wireless networks operate in two basic
modes. These are:
 Infrastructure mode (BSS)
 Ad-Hoc mode (IBSS)
1.1 Infrastructure mode
In BSS mode, network is connected with central device
called as access point (AP). All other communicating
devices called as clients are connected to each other
through central access point. Clients and access point must
use the same label of an Ethernet jack on the wall called
SSID. Clients and AP uses same SSID to identify the
network. A mobile host interacts with a bridge in the
network (called base station) within its communication
radius. Channel is setup in the access point (AP) and
discovered by clients.
Typical example of infrastructure based wireless networks
includes cellular networks, WI-FI, WI-MAX, satellite
communication, radar etc.
1.2 Ad-Hoc Mode (IBSS)
In Ad-Hoc mode, which also called as Independent Base
Service Set (IBSS), no central access point (AP) is
required. Here all nodes use the same SSID and channel.
Here all nodes can exchange information without the use
of pre-existing fixed network infrastructure. Devices are
free to move in the network without bound to any
agreement to stay connected. It is self-organizing and
adaptive network where each mobile host itself acts as a
router. Ad-Hoc network allows spontaneous formation and
deformation of mobile networks.
2. LITERATURE SURVEY
This section enlightens the earlier work done related to
wireless sensor networks. Literature survey illustrating the
various routing protocols for lifetime enhancement,
throughput paths, best roués and in field of wireless sensor
network security.
B.P.S Sahoo, Satyajit Rath, Deepak Puthal [1]
presented the adaptive approach to find an optimal routing
path from source to sink when the sensor node are
deployed randomly deployed in a restricted service area
with single link. Their analysis shows that the approach
they followed reduces the message communication to find
an optimal routing path. Hence the network consumes less
energy and increase the lifetime of network.
Asar Ali, Zeeshan Akbar [2] evaluate the performance of
two different routing protocols like ad hoc on demand
distance vector (AODV) and dynamic source routing
(DSR) for monitoring of critical conditions with the help
of important matrices like throughput and end to end delay
in different scenarios. Based on result derived from
simulation a conclusion is drawn on the comparison
between these two different routing protocols with
parameter like end-to-end delay and throughput.
Theodore Zahariadis et al [3] present a trust aware,
location based routing protocol which protects the WSN
against routing attacks, and also support large scale
WSN’s deployments. Their proposed solution has been
shown to efficiently detect and avoid malicious nodes and
has been implemented in state-of-the-art sensor nodes for
a real-life test-bed. Their work focused on the assessment
of the implementation cost and on the lessons learned
through the design, implementation and validation
process. The domain of WSNs applications is increasing

2. ICRTEDC-2014 10
widely over the last few years. As severely constrained
node resources, limited network resources and the
requirement to operate in an ad hoc manner characterize
this new type of networking, implementing security
functionality to protect against adversary nodes becomes a
challenging task.
Guoxing Zhan et al [4] proposes a fact that multi hope
routing in wireless sensor network offers little protection
against deception through replaying routing information. It
cannot be solved solely by encryption or authentication
techniques. To secure multi-hop routing in WSNs against
intruders exploiting the replay of routing information, we
propose TARF, a trust-aware routing framework for
WSNs.
3. ROUTING IN WSNs
The Wireless networks are highly distributed networks of
small wireless nodes, monitors the environment or system
by measuring physical parameters such as temperature,
pressure. A network consists of numbers of nodes with
one as a source and one as a destination. In a wide area the
number of nodes is not specified.
Here the concentration point is neither routing nor
transmission but deployment which means to distribute
systematically or strategically. In Wireless sensor
networks this arrangement method is called as sensor take
battle station.
These wireless sensor network’s faces numbers of
following problems at the time of communication.
 Issue regarding deployment
 Issue regarding distance
 Issue regarding energy consumption
 Issue regarding coverage area
3.1 Bacteria Foraging Optimization in WSNs
Bacterial Foraging Optimization (BFO) is a population-
based numerical optimization algorithm. In recent years,
bacterial foraging behaviour has provided rich source of
solution in many engineering applications and
computational model. It has been applied for solving
practical engineering problems like optimal control,
harmonic estimation, channel equalization etc. In this
paper, BFO has been used for cluster head selection to
provide improved energy efficiency in routing. The
process of cluster head selection involves application of a
clustering algorithm. This has been classically done with
LEACH, K-Means and direct method [6].
4. RESEARCH OBJECTIVE
The aim and objective of our research incorporated in our
paper covers the following steps.
1) Optimum route distance between nodes and
sensors
2) Optimum or least power consumption between
location points and sensors.
3) Maximum Bandwidth utilization.
4) Increase in Sensor Coverage.
5) Optimization of Mean location points of wireless
sensors.
5. PROPOSED STEPS TO ACHIEVE THE
OBJECTIVE
 With minimum number of sensor nodes having
maximum coverage in the network and the nodes
are within the communication range.
 By making optimized wireless clusters using the
Euclidean distance from all the location nodes to
the Sensor Nodes.
 By making the Clusters of the sensor nodes with
a corresponding central transceiver point which
will be further chosen from a group of sensors.
 By Optimizing the Sensors position within each
individual cluster, using BFO.

6. PROBLEM FORMULATION
In wireless sensor network data is transmitted through
node to node where distance is the most effecting factor to
the efficiency of the networks. Sensors are to be linked to
many near falling nodes by determining their distances. If
the distance is large than it will result to more energy
consumption and even results to week the signal strength.
If any sensor is linked to large number of nodes to reduce
the coverage area cost, then it will produce delay and leads
to more energy requirement. On other side if the to lower
the deployment cost sensors should be located far from
each other so that they may cover maximum area as well
as maximum numbers of nodes. But this may increase
wireless sensor node power consumption due to the energy
needed to reach large distances. Data loss, high energy
consumption, reduction in signal strength and
interferences in data were various factors which incorrupt
the transmission in Wireless sensor networks.
7. PROMBLEM SOLUTION
For reduction of the challenged faced by the wireless
sensor networks, we need to establish the sensors at a
place where it results to better communication. For this
various factors as distance, coverage area, signal strength,
energy level all has to be determined. There are to two
methods can be used.
 Hit & trial method
 Iterative optimization
8. SYSTEM DESIGN EVALUATION AND
SIMULATION RESULTS
This section includes results of the proposed algorithm in
terms of various parameters by analyzing the result
visually in network animator and graphically evaluating
the performance of the proposed system in terms of
standard parameters, which discussed later in this chapter.
This section also incorporate the validity of proposed work
by comparing the performance with existing system.
8.1 SIMULATION
 Firstly calculate the total number of the users in
the area under the wireless network and also
determine the total numbers of sensors are to be
used in the network for the communication
between the different nodes.
 Identify the initial position of all the nodes in the
network with determining the rough positions of

3. 11 ICRTEDC -2014
sensors anywhere in the coverage area, which
means randomly.
 Calculate the distance between nodes with
respect to each other and their distance from the
sensor nodes also. Based on this data, position of
sensor node and how many numbers of nodes to
be associated to it will be decided.
 According to the clustered information, find out
which node is associated to which sensor in the
network. This clustered data provides the
information about which node is connected to
which sensor node and even tell what numbers of
nodes are connected to the sensor nodes.
 The point of interest is where to place the sensor
node in the coverage area so that it may cover a
wide area with no loss in data and providing low
deployment cost and effective and strong signal
strength. Keeping all these factors in mind “soft
computing technique” is used to optimize the
position of the sensors in the coverage area.
 Soft computing is a technique in which
emphasize gains in understanding system
behavior and used for routing in the network.
Sensors are placed at different positions and a
fitness value or a threshold value is determined
according to position with cohort to distance.
 This process is repeated again and again for all
numbers of sensor nodes in the wireless sensor
networks to get the best position of the sensors in
the network coverage area. Best position here
specifies the position at which sensor when
placed gives strongest signal strength with no loss
in data during transmission and it is connected to
maximum numbers of nodes without producing
delays in the communications.
8.2 RESULTS
1) Getting initial total number of sensors in network:
Here we defined the number of sensors we need,
to place in the network. Initially let we want three
sensors in the network. We can place any number
of sensors in the network according to our
requirement. Before entering the number of
sensors in the network.
2) Getting initial total number of working nodes in
network: After selecting the number of sensors
we want to place in a network, we enter the
number of nodes through which we decide the
best location for sensor. These nodes decide the
reliability of our network. Greater the number of
nodes greater the chances of getting the best
location of sensors.
Get initial location of nodes of network by having user
define and randomly generated x-y coordinates by
selecting option 1 and option 2 from user.
Figure 9.1: Plotting initial location of sensor and nodes in
given network in different presentation
Next step is to optimize sensor location as per their
allotment and using Soft computing algorithm with fitness
function.
Figure 9.2: Optimization algorithm to find best location as
per parameters.
Figure 9.3: Best location finding by optimization
algorithm in each round

4. ICRTEDC-2014 12
Figure 9.4: Final optimized locations of sensors because of
fitness function and BFO algorithm and finally plot to
demonstrate the problem solution
10. CONCLUSION
A bio-inspired clustering algorithm based on BFO has
been proposed and investigation on energy efficient
clustering algorithms related to WSNs has been done.
This increases Network life of WSNs. The contribution of
this paper related to use of Bacteria foraging algorithm
firstly for WSNs for enhancing network lifetime of sensor
nodes. To validate the algorithm, simulations had been
carried out using MATLAB. Simulation results showed
better performance of BFO as compared to other
clustering protocols like LEACH, K-Means and direct
method in terms of performance metrics like number of
nodes and total energy dissipation in the system. Other
bio-inspired algorithms like Ant colony optimization,
artificial Immune system, Genetic algorithm (significant
time and power consuming) can also be compared to BFO;
but the challenge of reducing computational complexity
still remains.
REFERENCES
1. B. P. S. Sahoo and Deepak Puthal, “DRUG: An Energy-
Efficient Data-Centric Routing Protocol for Wireless Sensor
Network”, Manuscript Accepted for Publication in
International Conference on Intelligent Computing,
Communication & Devices (ICCD-2014) Springer Book
Series on "Advances in Intelligent Systems and Computing.
2. Asar Ali Zeeshan Akbar,” Evaluation of AODV and DSR
Routing Protocols of Wireless Sensor Networks for
Monitoring Applications”, Master’s Degree Thesis,
Karlskrona October 2009.
3. Theodore Zahariadis,Helen Leligou,Panagiotis
Karkazis,Panagiotis Trakadas, “DESIGN AND
IMPLEMENTATION OF A TRUST-AWARE ROUTING
PROTOCOL FOR LARGE WSNs”, International Journal of
Network Security & Its Applications (IJNSA), Vol.2, No.3,
July 2010.
4. Guoxing Zhan, Weisong Shi and Julia Deng, “TARF: A Trust-
Aware Routing Framework for Wireless Sensor Networks”,
EWSN 2010, LNCS 5970, pp. 65–80, Springer-Verlag Berlin
Heidelberg 2010.
5. Shuang Li, Alvin Lim and Cong Liu, “Improving QoS-based
Routing by Limiting Interference in Lossy Wireless Sensor
Networks”, International Journal of Wireless & Mobile
Networks (IJWMN), Vol.2, No.4, November 2010.
6. Sanatan Mohanty, “Energy Efficient Routing Algorithms for
Wireless Sensor Networks and Performance Evaluation of
Quality of Service for IEEE 802.15.4 Networks”, M.Tech
Thesis, National Institute of Technology, Rourkela-769008,
January 2010.