An Approach of Mobile Wireless Sensor Network for Target Coverage and Network Connectivity with Minimum Movement

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1275
An Approach of Mobile Wireless Sensor Network for Target Coverage and
Network Connectivity with Minimum Movement
Daler Kaur1, Mrs. Maninder Kaur2
1M.Tech Student, Dept. of Electronics and Comm., DIET, Kharar, Punjab, India.
2HOD (M.Tech), Dept. of Electronics and Comm., DIET, Kharar, Punjab, India.
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Abstract - A Mobile wireless sensor network is a set of
physically distributed sensor nodes. Sensor node is a small
wireless device with limited battery life, radio transmission
range and storage size. A sensor node performs the task of
collecting important data, processingthe data, monitoring the
environment, etc. This property of sensors i.e. mobility can be
very efficiently used to improve the target coverage quality
and network connectivity in randomly deployed mobilesensor
networks. Target coverage (TCOV) and Network connectivity
(TCON) are two main challenging issues of mobile sensor
networks. This paper focuses on the challenges of the Mobile
Sensor Deployment (MSD) problem and investigates how to
deploy mobile sensors with minimum movement and energy
consumption to form a WSN that provides both target
coverage and network connectivity.
Key Words: Mobile Wireless Sensor Network (MWSNs),
Target Coverage (TCOV), Network Connectivity (NCON),
Mobile Sensor Deployment (MSD).
1. INTRODUCTION
A Mobile Wireless Sensor Network (MWSN) can simply be
defined as a wireless sensor network (WSN) in which
the sensor nodes aremobile.MWSNsarea smaller,emerging
field of research in contrast to their well-established
predecessor. MWSNs are much more versatile than static
sensor networks as they can bedeployedinanyscenario and
cope with rapid topology changes [14]. Basically, Mobile
Wireless Sensor Networks (MWSN) are the collection of the
small and the light weight wireless nodes [4].
F
igure -1: Mobile Wireless Sensor Network
1.1 Coverage in MWSNs
Coverage is the primary evaluation metric for a wireless
network. It is always advantageous to have the ability to
deploy a network over a larger physical area. This can
significantly increase a system’s value to the end user. It is
important to keep in mind that the coverageofthenetworkis
not equal to the range of the wireless communication links
being used [3]. Multi-hop communication techniques can
extend the coverage of the network well beyond the range of
the radio technology alone.
Coverage is a measure of the quality of service providedbya
sensor network. Due to the attenuation of energy
propagation, each sensor node has a sensing gradient, in
which the accuracy and probability of sensing and detection
attenuate as the distance to the node increases. The total
coverage of the whole network can therefore be defined as
the union (including possible cooperativesignal processing)
of all nodes’ sensing gradients. It represents how well each
point in the sensing field is covered [6]. Coverage is a
fundamental issue in a WSN, which determines how well a
phenomenon of interest (area or target) is monitored or
tracked by sensors. Each sensor node is able to sense the
phenomenon in a finite sensing area.
1.2 Connectivity in MWSNs
Connectivity is an important issue in WSNs which concerns
with delivering the sensed data from the source sensor to
the destination (sink node) via radio transmissions. As
sensors are low-cost devices with constrained resources,
each sensor node has only limited communication range
compared with the size of the monitored area. Multi-hop
communications are necessary when a sensor cannot reach
the sink node directly [5]. Two sensors are called neighbors
if they are within each other's communication range. The
sensor nodes and the communication links between each
pair of neighbors build the network topology, which is
required to be connected by the connectivity requirement.
Connectivity represents how well the sensor nodes in the
network are “connected” to each other. It is a fundamental
property of a wireless sensor network,formanyupper-layer
protocols and applications, such as distributed signal
processing, data gathering and remote control, require the
network to be connected. Since the sensor nodes

communicate via wireless medium, a node can only directly
talk to those that are in close proximity to itself (within its
communication range). If a sensor network is modeled as a
graph with sensor nodes as vertices and direct
communication links between any two nodes as edges, by a
connected network we mean the graph is connected [3].
1.2 Network Lifetime in MWSNs
Network lifetime is one of the most important and
challenging issues in WSNs which defines how long the
deployed WSN can function well. Sensors are unattended
nodes with limited battery energy. In the absence of proper
planning, the network may quickly cease to work due to the
network departure or the absence of observation sensors
deployed close to the interestedphenomenon.Sincea sensor
network is usually expected to last several months without
recharging, prolonging network lifetime is one of the most
important issues in wireless sensor networks [9].
A sensor node is generally composed of four components:
sensing unit, data processing unit, data communication unit
and power unit. The power unit supplies power to the other
three units. Any activity of the other three units - sensing,
data processing, data transmitting and data receiving-will
consume battery energy. Experiments show that wireless
communication (data transmitting and receiving)
contributes a major part to energy consumption rather than
sensing and data processing. Therefore, reducingthe energy
consumption of wireless radios is the key to energy
conservation and prolonging network lifetime [11].
2. LITERATURE SURVEY
Zhuofan Liao, JianxinWang,ShigengZhang,Jiannong Cao
and Geyong Min, “Minimizing Movement For Target
Coverage and Network Connectivity in Mobile Sensor
Networks” (2015).In this paper, the Mobile Sensor
Deployment problem is divided into two sub-problems,
Target COVerage (TCOV) problem and Network
CONnectivity (NCON) problem. For the TCOV problem, it is
NP-hard. For a special case of TCOV, an extended Hungarian
method is provided; for general cases, two heuristic
algorithms are proposed based on clique partition and
Voronoi diagram, respectively. For the NCON problem, first
propose an edge constrained Steiner tree algorithm to find
the destinations of mobile sensors, then use the extended
Hungarian to dispatch rest sensors to connect the network
[1] .
Sonali Karegaonkar and Archana Raut, “Improving
Target Coverage and Network Connectivity of Mobile
Sensor Networks” (2015). In this paper, in addition to
Basic algorithm and TV-Greedyalgorithm,LWZ compression
algorithm is applied while sending data from sensor node to
sink node, hence the computation speed of transmission is
maximized. Simulation result obtained validates the
performance of the proposed algorithm. Hence the issues of
TCOV and NCON in MSNs are successfully overcomes and
increase the network lifetime [2].
D.Prasad, “Enhancing Target Coverage and Network
Connectivity of Mobile Sensor Networks” (2016). In this
paper the issue of Target Coverage (TCOV) and Network
Connectivity (NCON) in Mobile Sensor Networks(MSNs)are
taken into consideration. To solve TCOV problem, two
algorithms are proposed: Basic algorithm and TV Greedy
algorithm. TV Greedy algorithm achieves less movement
than basic algorithm because it selects the sensor which is
very close to target to achieve that target. Hence, the
proposed scheme overcomes the issue of TCOV & NCON in
MSNs & increase the network lifetime [3].
Mr. Mayur C. Akewar and Dr Nileshsingh V. Thakur, “A
study of Wireless Mobile Sensor Network deployment”
(2012). In this paper, fundamental problem of deployment
in mobile sensor network is discussed. The issues of mobile
sensor network deployment are investigated in detail. It
further discusses the types of algorithm and different ways
of deployment like deterministic, random and incremental
deployment along with self deployment. Different
approaches for mobile sensor network deployment are
discussed in detail with their comparisons. Modeling of
deployment problem with other real world problem is also
discussed [4].
E. Mathews and C. Mathew, “Deployment of mobile
routers ensuring coverage and connectivity,” (2012). In
this paper, two new localized and distributed algorithms for
creating an ad-hoc mobile router network has been
discussed that facilitatescommunicationbetweentheagents
without restricting their movements. The first algorithm,
agent assisted router deployment,isusedinscenarios where
a proactive pre-deployment is not feasible due to thelimited
speed of the routers compared to thespeedoftheagents and
the second one self-spreading is used in scenarioswherethe
proactive pre-deployment is feasible. The algorithms have a
greedy deployment strategy for releasing new routers
effectively into the area and a triangular deployment
strategy for connecting different connected components
created from different base stations [5].
3. PROPOSED METHOD
In the present work, main aim is to focuses on the challenges
of the Mobile Sensor Deployment (MSD) problem and
investigates how to deploy mobile sensors with minimumor
no movement at all to form a Wireless Sensor Network that
provides both target coverage and network connectivity.
As sensor movement consumes much more energy than
sensing and communication do, the movement of sensors
should be minimized to increase the networks’ lifetime.

3.1 Methodology
In present work, firstly sensor nodes should be deployed in
such a way that they cover the complete area. We prefer to
have static nodes. Then divide the complete area into the
zones. Each zone will have a zone header. Zone headers
should be at a convenient distance from each sub nodes.
Then we will select a source and a destination. Information
will transfer from sub nodes to headers and then from
headers to headers, finally to the destination. A lot of energy
will be saved, thus increasing network’s lifetime.
Figure 2 show the flow chart of the methodology.
Figure -2: Flow chart of methodology
4. RESULT ANALYSIS
The proposed technique is implemented using MATLAB
which is developed by MathWorks,
allows matrix manipulations, plotting of functions anddata,
implementation of algorithms, creation of user interfaces,
and interfacing with programs written in other languages,
including C, C++, Java, and Fortran. The MATLAB software
provides communication for control and manipulation of
virtual reality objects using MATLAB objects.
Following is the complete program flow and results
associated with it:
First step is to create a deployment area with entering the
value of length and breadth. Length and breadth are entered
according to the desired requirements. Length and breadth
of the deployment area is 10. Length and breadth are on x-
axis and y-axis respectively.
Figure -3: Deployment of area
After deploying desired area, second step is to divide this
deployed area into equal zones. Number of zones should be
selected as per requirement to divide the deployment area
into zones. As shown in figure 4, the deployment area is
divided into 5 zones. These 5 zones are equally spaced.
Third step is to deploy the sensor nodes in each of the zone.
Each zone will have a zone header. The rest of the nodes are
sub-nodes. The number of sensor nodes to be deployed in
one zone is selected by desired required. Sensors are
deployed in uniform manner. Each sensor nodehasa sensor
id number. The position or sensor id number of each sensor
node is saved in Excel file.
Figure -4: Divided deployment area
As shown in Figure 5, each zone has a zone header which is
shown by red color. The rest of the nodes are sub-nodes.
Deploying sensor node randomly in
described area
Prefer to have static nodes
Divide the complete area in zones
Each zone will have a zone header
Select a source and a destination
Information will transfer from sub
node to header & then from headers
to headers, finally to the destination
A lot of energy will be saved, thus
increasing network’s lifetime

Figure -5: Deployment of nodes in each zone
After deployment of nodes, fourth step is to select a sender
and receiver from different zones. Sender will first
communicate with its respective zone header.
Figure -6: Comm. between sender and receiver
The sender zone’s header will thensearchforreceiver’szone
header. Once found, receiver’s zone header will transmit to
the receiver node. Sender and receiver can be from any
zones available. For selecting the sender, refer to the Excel
file which contains sensor id numbers.
Once communication is complete,nextstepistocalculatethe
energy. This is the energy depletion across the nodes that
were involved in the communication process. By this step,
we get the information about the energy consumed in the
transfer process.
The graph between nodes involved and energy values is
shown in figure 7. This is the energy depletion across the
nodes that were involved in the communication process. By
this step, we get the information abouttheenergyconsumed
in the transfer process.
Figure-7: Graph of nodes involved info. transfer versus
energy at those nodes
Table no. 1 shows the detail of percentage of energy at each
sensor node which are involved in the communication
between sender and the receiver.
Table-1: Energy distribution at sensor nodes involved in
communication.
Serial No. ID no. of sensor node Energy at sensor
node (in percentage)
1. 104 100
2. 103 96.66
3. 101 93.33
4. 102 90.00
5. 201 86.66
6. 202 83.33
7. 301 80.00
8. 302 76.66
9. 401 73.33
10. 402 70.00
11. 501 66.66
12. 503 63.33
Overall energy distribution shows the energy across all the
sensor nodes which are deployed.Thisisthebroaderviewof
node wise energy across all the nodes deployed. Now same
energy distribution values can be seen on all the nodes put
together.
Here, conclusion is that the energy isconsumedonlyofthose
sensor nodes only which are involved in the communication
or transfer path between source node and destination node.
The rest of the sensor nodes which are not involved in
communication or transfer path have their full energy. No

energy wastage of other sensor nodes is there. Hence, this
will lead to increasing the lifetime of whole network as no
energy wastage is there.
Figure-8: Graph of overall energy distribution
Next to the energy graphs, graph of impact of number of
sensor and impact of number of targets is shown. We then
investigatethe impact of thenumberofmobilesensorsonthe
movement distance of the algorithms usedandourproposed
algorithm. The comparison shows that proposed system
shows better results than previous methods.
Figure-9: Impact on number of sensors
In Figure 9, comparison of impact on number of sensor by
Ex-Hungarian algorithm, Basic algorithm, TV-Greedy
algorithm and proposed algorithm is shown. It is clear from
the graph that the movement by proposed algorithm which
is shown by blue line has minimum impact on number of
sensors as compared to all the other algorithms. So, by
proposed algorithm there is minimum impact on number of
sensors.
In figure 10, comparison of impact on number of targets by
Ex-Hungarian algorithm, Basic algorithm, TV-Greedy
algorithm and proposed algorithm is shown. It is clear from
the graph that the movement byproposedalgorithmwhichis
shown by blue line has less impact on number of target as
compared to all the other algorithms.
Figure-10: Impact on number of targets
5. CONCLUSIONS
In this work, we have studied the MobileSensorDeployment
(MSD) problem in Mobile Sensor Networks (MSNs), aiming
at deploying mobile sensors to provide target coverage and
network connectivity with requirements of moving sensors.
As sensors are usually powered by energy limited batteries
and thus severely power-constrained, energy consumption
should be the top consideration in mobile sensor networks.
Specially, movement of sensors should be minimized to
prolong the network lifetime because sensor movement
consumes much more energy than sensing and
communication do. However, most of the existing studies
aimed at improving the quality of target coverage, e.g.,
detecting targets with high detection probability, lowering
false alarm rate and detectiondelay.Littleattentionhasbeen
paid to minimizing sensor movement. To fill in this gap, this
work focuses on moving sensors to cover discrete targets
and form a connected network with minimum movement
and energy consumption.
REFERENCES
[1] Zhuofan Liao, Jianxin Wang, Shigeng Zhang, Jiannong Cao
and Geyong Min, “Minimizing Movement For Target
Coverage And Network Connectivity In Mobile Sensor
Networks”, IEEE Transactions on Parallel and Distributed
Systems, Vol. 26, No. 7, July 2015.
[2] Sonali Karegaonkar and Archana Raut, “Improving Target
Coverage and Network Connectivity of Mobile Sensor
Networks”, International Journal of Science and Research
(IJSR), Vol.4, Issue No. 4, April 2015.
[3] D. Prasad, “Enhancing Target Coverage and Network
Connectivity of Mobile Sensor Networks”, International
Journal of Innovative Research in Computer and
Communication Engineering (IJIRCCE), Vol.4, Issue 1,
January 2016.
[4] Mr. Mayur C. Akewar and Dr. Nileshsingh V. Thakur, “A
study of Wireless Mobile Sensor Network deployment”,
International Journal of Computer Networks&Wireless
Communication (IJCNWC), Vol.2, No. 4, August 2012.

[5] E. Mathews and C. Mathew, “Deployment of mobile
routers ensuring coverage and connectivity,” Int. J.
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BIOGRAPHY
Daler Kaur has received B.Tech
degree in Electronics and
Communication Engineering from
Indo Global College of
Engineering, Mohali, under
I.K.Gujral Punjab Technical
University, Jalandhar, Punjab. She
is currently pursuing M.Tech
degree in Electronics and
Communication Engineering from
Doaba Institute of Engineering
and Technology, Kharar, under
I.K.Gujral Punjab Technical
University, Jalandhar, Punjab.

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