Node Deployment Technique using Wireless Sensor Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 359
Node Deployment Technique Using Wireless Sensor Networks
Bhawna Garg1, Assistant Professor Er, Amrita Chaudhary2
1Department of Computer Science and Engineering Galaxy Global Group of Institution Dinarpur, Amabala, India
2Department of Computer Science and Engineering Galaxy Global Group of Institution Dinarpur, Amabala, India
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Abstract— A wireless sensornetworkinvolvesofsensor nodes
which are capable to perform sensing, computation and
transmission. These sensor nodes have limited battery power.
Therefore to increase the lifetimeofwirelesssensornetwork, it
is required to develop such techniques to consume less energy.
Less consumption of overall energy of the network results is
increase in the system capacity. In this kind of networks, some
of the key parameters that need to be satisfied are
connectivity, number of sensor nodes and energyconsumed by
nodes. We proposed a multi-objective optimizationalgorithm
based on energy and connectivitytoprolong WSNlifetime. The
efficiency of algorithm can be shown as finding the optimal
solutions among the least energy consumption while
maintaining the connectivity of the network
Index Terms: WSN, Energy Efficient Routing, Relay
I. INTRODUCTION
Wireless sensor network is a network consisting of several
number of heterogeneous nodes called as sensors nodes
which are spatially distributedall overthelocationandthese
networks are used to monitor physical or environmental
conditions such as temp, pressure, sound, vibration at these
locations. Wireless communication enables[1] the co-
operation of nodes to fulfill bigger tasks that single nodes
cannot. Nodes in WSN are thickly deployed and are greater
in numbers as compared to mobile ad hoc networks. These
nodes communicate with each other and pass data along
from one to each other from source to sink. Essentially
Sensor nodes bridge the gap betweenphysical worldand the
virtual world. Each node consists of processing capability,
may contain many processing units like multiple types of
memory, have a RF transceiver, have a power source like
battery, and accommodate various sensors and actuators.
A sensor network generallyconsistsofseveral[3]tinysensor
nodes and a few powerful switch nodes also called
base stations or called as sink. Sensor nodes are usually
densely set up in a large area and communicate with each
other in short distances through wireless communication.
Although particular sensor nodes have limited number of
resources, they are able to achieve worthytask ofbigvolume
when they work as a team member. Informationgathered by
and transmitted on a sensor network of wireless networks
describes conditions of physical environments of the area
where the sensor network is set up.
II.Routing in wireless sensor network
The nodes in the wireless sensor network are deployed at
inaccessible location, so differentroutingprotocol havebeen
proposed over a period of time for the routing the packetsin
wireless sensor network. The routingprotocol forthesensor
nodes depends upon the type of application. The routing
protocol in wireless sensor network is broadly classified as
cluster based routing, single path routing,Multipathrouting.
The routing protocols in the wirelesssensornetwork haveto
meet a strict power saving constraint. The main goal for the
routing protocol is to deliver the packet from source to
destination based on the topology or position addressing. If
the addressing is host based then it is called as topological
addressing and if a unique identification is chosen for the
addressing of node then it is called as position based.
The sensor node is capable of delivering[4] the requested
user query back to the base station. The routing protocol for
wireless sensor network can further be classified as single
path algorithm in which only one instance of packet are sent
at any time. There are many other protocols for wireless
sensor network such as partial flooding and multipath
routing. The single path routing protocol algorithm uses
resources less than the resources used in multipath or
partial flooding routing. The delivery of data is the main aim
for single path, multipath and partial flooding routing
protocol. The channel access problem is the main problem
that is faced by the routing protocol. The routing protocol
guarantees to access the channel efficiently by the user to
increase the throughput of network. The routing algorithm
used the concept of memorization of past traffic to increase
the reliability of network.
Energy Efficient Routing: As the energy consumption for
performing computation has been significantly increased in
present days but battery lifetime has not improved
considerably.Thesignal strengthadjustmentconsumes large
amount of energy. The energy efficient routing protocol is
necessary to reduce the energy consumption[5] problem
both in channel activation and data transfer.

Planner Graph Routing: If the neighbors are not closer to
the target and the packets are dropped by the used path.
Data recovery scheme is used to recover the data. It resends
the data and provides end to end deliveryofpackets.The use
of planner graph routing increase the efficiency of network.
2.1 Routing Challenges and Design Issues
Depending upon the application thereareseveral challenges
in the wireless sensor networks thataffectstheperformance
of the routing protocol. Some of the challenges and design
issues are as follows:
Node Deployment:In WSN motesaredeployedaccordingto
the demand of the application. Thus affects the productivity
of the routing protocol. The nodes are deployed in uniform
way or randomized way. In uniform way, the nodes are
placed manually at fixed spot and routing paths are
predetermined. In randomized way, the nodes are scattered
randomly. This causes several issues such as optimal
clustering, coverage etc. The position of the sink node or
cluster head is an important factor in terms of energy
efficiency.
Energy Consumption: Sensor nodes are small in size so
they have limited power supply to perform sensing[9],
processing and transmitting the information via a wireless
communication. The lifetime of the sensor node is totally
dependent on the battery. Once the battery is depleted the
sensor node will be dead it causes change in topology
rerouting of data. The multi-hop communication consumes
less energy than direct communication. But it increases the
overhead on the topology management and MAC. Direct
communication is better when nodes are close to the sink
node.
Fault Tolerance: A fault can occur in a node due to
depletion of power supply, physical damage etc. If a sensor
nodes stops working it shouldnoteffectstheoverall working
of the WSNs. Routing protocol must be capable of handling
the failure of sensor nodes by accommodating new link
formation , routes to the base stations and by adjusting the
transmit and receiving power on the link to reduce the
energy consumption.
Network Dynamics: For most of the application the sensor
node are stationary but according to the need of the
application the base station and sensor node are mobile.
Routing becomes a challenge due to the mobility of nodes.
According to the need of application the sensing
phenomenon can be static or dynamic. Dynamic sensing is
done target detection application while forest alert
application requires static sensing.
Data Delivery Models: The Data Delivery models depend
upon the application of the network. There are four types of
data delivery models: continuous,eventdriven,querydriven
and hybrid. In continuous the data is send periodicallytothe
sink. In event driven the data is transmit when the event
happens. In query driven the data is transmit when sink
generates a query to the node. Hybrid use combination of all
three models depending upon the need of application.
Scalability: A large number of sensor nodes in range of
thousands are deployed in sensing area. Scalability means
that the routing algorithms should work efficiently with
large number of sensor nodes. Routing protocol must be
efficient to react to the event occurring in the environment.
Most of the sensor node remains in the sleep state until
event does not occurs.
Data Aggregation: Sensors nodesgenerateshugeamountof
similar packets, data aggregation is used to reduce the
transmission of similar packets. Data aggregation is
combination of information from different sensor nodes by
applying functions like suppression,average, maximum. The
routing protocol incorporates this data aggregation
technique to reduce data redundancy and achieve energy
efficiency.
Node capabilities: According to the demand of application
different functionalities can be given to the sensor nodes.
Depending on the application a sensor node can be perform
functions like sensing, relaying, aggregation
III.Need and significance of proposed research work
The main motive of research is to increase the lifetime of
WSN using multi-objective node deployment. The two main
multi-objectivesare:(i)Energyconsumption(ii)Connectivity.
There are various optimization protocols: Pareto optimal
and non-dominant sorting genetic algorithm (NSGA-II) that
are used to improve the lifetime of WSN network.
3.1 Objectives of proposed work
To enhance the lifetime of wireless sensor networks using
optimal multi-objective node deployment. The network
lifetime is very critical parameter related to sensor network
and has been tackled at various levels such as design,
operation and deployment. A multi-objective routing
protocol is designed to maximize the lifetime while
considering other conflicting objectiveslike,minimization of
energy consumption, maximization of connectivity.
To make cluster heads using DE (Differential Evolution)
algorithm. The DE algorithm is a simple, fast, efficient and
population-based direct search algorithm for solving global
optimization problems. The DE algorithm has been widely
used in many areas. The DE algorithm uses the basic
framework of the genetic algorithm for designing a unique
differential mutation operator. The main operations of the
DE algorithm have the mutation operation, crossover
operation and selection operation. It takes on some
advantages of the simple structure, ease of use, robustness,
and fast convergence.
A wireless sensor network consists of sensor nodes which
are capable to perform sensing, computation and
transmission. These sensor nodes have limited battery
power. Therefore to increase the lifetime of wireless sensor

network, it is required to develop such techniques to
consume less energy. Less consumption of overall energy of
the network results is increase in the system capacity.
Many researches are made on node deployment in wireless
sensor networks. In this kind of network, some of the key
objectives that need to be satisfied are connectivity and
energy consumed by nodes. NSGA-II based multi-objective
algorithm for optimizing all of these objectives
simultaneously. The efficiency of this algorithm can be
finding the optimal balance point among the least energy
consumption, and the minimum number of active nodes to
maintaining the connectivity of the network.
3.2 Planning of work
i. To maximize the lifetime of wireless sensor
network we proposed mainly two approaches
named “Non-Dominated Sorting Genetic
Algorithm-II and Differential Evolution
Algorithm”.
ii. NSGA-II is one of the most efficient multi-objective
optimization algorithm. It finds the optimal
locations of node deployment into the network.The
optimal differential evolution algorithm to find the
direction of node deployment to the optimized
selection of cluster heads.
NSGA-II algorithm is one of the most popular algorithms.
By introducing the fast non dominated sorting approach,
binary crowding tournament selection, and the Pareto-
optimal front can be searched effectively. NSGA-II has a
diversity-preserving mechanismwhichassuresconvergence
toward the Pareto optimal front without losing solution
diversity.
Initially, a random population 𝐷𝑡 (𝑡 = 0) is created and is
sorted into different non dominancelevels.Foreachsolution
a fitness value is equal to its non dominance level. For
creating an offspring population 𝐸𝑡ofsize 𝑁,someoperators
such as binary tournament, mutation, selection and
recombination are used. Then the following steps will be
repeated until the number of generation reaches the
maximum number of generations.
Algorithm 1: Fast non-dominated sorting algorithm
Input: population 𝑃
Output: The non-dominated fronts (Fr1, Fr2, . . .)
For each 𝑝 ∈ 𝑃
For each 𝑞 ∈ 𝑃
If (𝑝 < 𝑞) then
𝑆𝑝= 𝑆𝑝 ∪ {𝑞}
Else if (𝑞 < 𝑝) then
𝑛𝑝 = 𝑛𝑝 + 1
if 𝑛𝑝 = 0 then
𝐹1 = 𝐹1 ∪ {𝑝}
𝑖 = 1
while 𝐹𝑖 < > Ø
𝐻 = Ø
For each 𝑝 ∈ Fr𝑖
For each 𝑞 ∈ 𝑆𝑝
𝑛𝑞 = 𝑛𝑞 − 1
if 𝑛𝑞 = 0 the𝐻 = 𝐻∪ {𝑞}
𝑖 = 𝑖 + 1
Fr𝑖 = 𝐻
Step 1: Combine parent and offspring populations to create
𝑃𝑡= 𝐷𝑡 ∪ 𝐸𝑡. Perform a fast non dominated sorting in 𝑃𝑡
according to the algorithm shown in Algorithm 1 and
identify different fronts Fr𝑖, 𝑖 = 1, 2,..
Step 2: Set a new population 𝐷𝑡+1 = Ø. Set counter 𝑖 = 1.
While |𝐷𝑡+1|+|Fr𝑖| < 𝑁, do 𝐷𝑡+1 = 𝐷𝑡+1 ∪ Fr𝑖 and 𝑖 = 𝑖+1.
Step 3: Select (𝑁−|𝐷𝑡+1|)mostwidelyspreadsolutionsfrom
Fr𝑖 using the binarycrowdingtournamentselectionoperator
and insert them into 𝐷𝑡+1.
Step 4: Create an offspring population 𝐸𝑡+1 from 𝐷𝑡+1 by
using the binary crowding tournament selection, crossover,
and mutation operators. Set 𝑡 = 𝑡 + 1.
The process of non dominated sorting and filling the
population 𝐷𝑡+1 steps can be performed together. In Step 3,
the crowding-sorting of the solutions in front Fr𝑖, which is
the last front that could not be completely accommodated, is
performed by using a crowded-distance metric. The
crowding comparison operator compares two solutionsand
returns the winner of thetournament. The winnerisselected
based on two attributes: the non
dominance ranking 𝑟𝑖 and the local crowding distance 𝑑𝑖 in
the population. Thiscrowdingdistanceattributeofa solution
𝑖 is a measure of the search space around 𝑖, which is not
occupied by any other solution inthepopulation.Basedon 𝑟𝑖
and 𝑑𝑖, the binary crowding tournament selection operator
works as follows. A solution 𝑖 wins a tournament over
another solution 𝑗 if any of the following conditions is true.

(1) If ri < rj (this assures that the selected solution lies on a
better non dominated front).
(2) If ri < rj and di < dj (this is applied when both solutions
lie on the same front and the above condition cannot be
applied; in this case, the solution residing in a less crowded
area with a larger 𝑑𝑖 wins).
IV.Simulation Parameters
The used simulation parameters are shown in Table 4.1
Table 4.1: Simulation Parameters.
Name of the parameter Parameter values
Network area (variable) 100 m · 100 m
Number of sensor nodes
(variable)
50
Initial energy(Einit) 0.5 J
Eelec 50 nJ/bit
Efs 10 pJ/bits/m2
Eamp 0.0013 pJ/bit/m4
Distance do sqrt(efs/emp)
Eda 50 nJ/bit/signal
Packet size (variable) 4000 bits
4.2 Simulation Scenario
Initially there is a network in which nodes are deployed
randomly. This is shown in figure 4.1.
Figure 4.1: Random network deployment using 50 nodes.
Initially there is a network in which nodes are deployed
optimally. This is shown in figure 4.2.
Figure 4.2: Optimal network deployment using 50 nodes.
4.3 Performance Evaluation
The figure 4.3 shows the graph of half node dead in optimal
deployment DE algorithm happens after 1000 rounds in
spite of random deployment DEalgorithmwhichishaving its
half dead after 800 rounds. Hence optimal deployment
algorithm is energy efficient than random deployment
algorithm. The figure 4.3 as shown below:

Figure 4.3: Comparison of optimal and random
deployment in terms of half node dead.
The figure 4.4 shows the graph of last node dead in optimal
deployment DE algorithm happens after 1600 rounds in
spite of random deployment DEalgorithmwhichishaving its
last dead nodeafter1200rounds.Henceoptimal deployment
algorithm is energy efficient than random deployment
algorithm. The figure 4.4 as shown below:
Figure 4.4: Comparison of optimal and random
deployment in terms of last node dead.
Figure 4.5 gives the graph which compares the performance
of random and optimal deploy DE in terms of number of
dead nodes with total number of rounds. Green line
represents the optimal deployment DE and blue line
represents the random deployment DE. Graph shows that
optimal deployment shows improved performance over
random deployment after 800 rounds.
Figure 4.5: Comaprison of the performance of random and
optimal deploy-DE in terms of number of dead nodes &
number of rounds.
Figure 4.6 gives the graph which compares the performance
of random and optimal deploy DE in terms of number of
remaining energy with total number of rounds. Green line
represents the optimal deployment DE and blue line
represents the random deployment DE. Graph shows that
random deployment have almost sameresidual energyupto
initial 100 rounds as optimal deployment is having. The
optimal deployment shows improved performance of the
remaining energy over random deployment after 100
rounds.
Figure 4.6: Comaprison of the performance of optimal and
random deploy-DE in terms of number of dead remaining
energy with total number of rounds.
V. Conclusion and Future Scope
In this paper , We executed different simulation results to
show the performance of the proposed algorithm. The
simulation results to show the random and optimal
deployment of nodes in the network. The results obtained
from the simulations showed that with the same number of

active sensor nodes, optimal deployment DE can obtain
better results than random deployment DE algorithm
In the future, NSGA-II algorithm is implemented for multi
objective parametersinwirelesssensornetworks.Algorithm
can be further implemented for other parameters such as
coverage, reliability and quality of service (QoS).
Hybrid DE-PSO algorithm can be used further to get the
optimal results in the wireless sensor network to improve
the lifetime of the network.
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Node Deployment Technique using Wireless Sensor Networks

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