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.
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 networkâs faces numbers of
following problems at the time of communication.
ï· Issue regarding deployment
ï· Issue regarding distance
ï· Issue regarding energy consumption
ï· 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
ï· With minimum number of sensor nodes having
maximum coverage in the network and the nodes
are within the communication range.
ï· By making optimized wireless clusters using the
Euclidean distance from all the location nodes to
the Sensor Nodes.
ï· By making the Clusters of the sensor nodes with
a corresponding central transceiver point which
will be further chosen from a group of sensors.
ï· By Optimizing the Sensors position within each
individual cluster, using BFO.
ï·
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.
ï· Hit & trial method
ï· 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
ï· 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.
ï· 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.
ï· 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.
ï· 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.
ï· 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 âsoft
computing techniqueâ is used to optimize the
position of the sensors in the coverage area.
ï· 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.
ï· 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.
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