AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NETWORKS BASED ON FLOWER POLLINATION ALGORITHM

Natarajan Meghanathan et al. (Eds) : NETCOM, NCS, WiMoNe, GRAPH-HOC, SPM, CSEIT - 2016
pp. 117– 129, 2016. © CS & IT-CSCP 2016 DOI : 10.5121/csit.2016.61511
AN EFFICIENT DEPLOYMENT APPROACH
FOR IMPROVED COVERAGE IN WIRELESS
SENSOR NETWORKS BASED ON FLOWER
POLLINATION ALGORITHM
Faten Hajjej, Ridha Ejbali and Mourad Zaied
Research Group on Intelligent Machines (REGIM-Lab) Sfax, Tunisia
hajjej.faten.tn@ieee.org
ridha_ejbali@ieee.org
mourad.zaied@ieee.org
ABSTRACT
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous
advancement in various scientific and industrial fields. WSNs offer favorable low cost and
readily deployable solutions to perform the monitoring, target tracking, and recognition of
physical events. The foremost step required for these types of ad-hoc networks is to deploy all
the sensor nodes in their positions carefully to form an efficient network. Such network should
satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement
problems. In fact, we present a new optimization approach based on the Flower Pollination
Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We
have compared the performance of the resulting algorithm, called FPACO, with the original
practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances,
FPACO performs better than all other algorithms.
KEYWORDS
WSN, Sensors Deployment problem, Coverage, FPA.
1. INTRODUCTION
Technological innovations in miniaturization, power management and wireless communication in
the recent years have enabled the progress of wireless networks, which have attracted a growing
interest for many applications and fields, such as military sensing, physical air traffic control,
video surveillance, traffic surveillance, industrial and manufacturing automation, security,
environment monitoring, and building and structural monitoring.
A wireless Sensor Network (WSN), which is a targeted wireless network, consists of a significant
number of miniaturized electronic devices, called sensors, distributed over a specified area in
order to sense the environment and communicate the accumulated information from the

118 Computer Science & Information Technology (CS & IT)
monitored field to other networks (e.g., the internet). In WSN, sensors have limited resources,
typically the energy resources, and the calculation capabilities, as well as the storage capacity.
Therefore, most studies and researches on WSNs have focused on the optimization of resources
in order to enhance the performances and meet the quality of service (QoS) requirements.
Determining the sensor field topologies is a key challenge in sensor resource management.
Consequently, WSN performance is powerfully influenced by the deployment topology of sensor
nodes, which affect QoS metrics, such as energy consumption, sensor lifetime, and sensing
coverage equally [1].
In the literature, the deployment topology can be classified according to; the placement
methodology that can be either random placement or grid-based placement (deterministic
placement), the optimization of performance metrics such as connectivity, sensing coverage,
energy consumption and lifetime, and the roles the deployed node, which can be regular, relay,
cluster-head, or base-station, plays [2]. However, the placement techniques can be further
categorized into static and dynamic whether the optimization is performed at the time of
deployment or whiles the network is working, respectively. The choice of the deployment scheme
depends on many properties [2]. Therefore, many studies considered that for some cases random
placement becomes the only option due to the environment characteristics [3] [4] and deployment
cost, and time. Figure 1 shows the different categories of node placement strategies.
Our major focus in this paper is on how to choose the optimal nodes deployment that can achieve
maximal coverage of the monitored area [5]. Thus, optimal nodes placement issue is a problem
that has been proven NP-hard for most formulations of sensor deployment [6].
The coverage metric is a decisive metric that can be considered as a measure of permanence and
QoS for WSN. Coverage in a WSN is to ensure that the Region of Interest (RoI) is monitored
with high reliability in order to have the necessary information on the supervised phenomenon
[7]. Coverage issues can be commonly classified into two types: target coverage problem and
area coverage problem. The former ensures the monitoring of only certain specific points which
have fixed positions in the area of interest, while the latter is concerned with the supervision of
the whole deployment area. Target coverage can be categorized as Q-coverage or simple
coverage. For simple coverage, each target should be monitored by at least one sensor node. For
Q-coverage, each target has to be monitored by at least Q different working sensor nodes.
Figure 1. Sensor node placement methodologies
The connectivity metric is as important as coverage in wireless sensor networks. A WSN is
defined as connected if, and only if, there exists at least one route between each pair of nodes.
Thus, connectivity depends on the existence of paths and can therefore be directly affected by

Computer Science & Information Technology (CS & IT) 119
changes of topology. For this reason an optimal deployment strategy have to maximize coverage
with respect to the connectivity constraint.
Nature constantly inspires research in the field of optimization. While genetics, ants and particle
swarm algorithms are famous examples, other nature inspired optimization algorithms emerge
regularly. Flower Pollination Algorithm (FPA) is novel global optimization algorithm inspired
from pollination process of flowers. FPA is simple and very powerful; in fact, it can outperform
both genetic algorithm (GA) and particle swarm optimization (PSO) according to [8].
To find the best nodes deployment that would achieve maximal coverage of the targeted area
without affecting network connectivity, a new approach based on FPA is introduced to enhance
coverage in a wireless sensor network. We considered a centralized topology and an area
coverage problem with random sensor deployment. Here, different scenario was tested. The
proposed approach was able to maximize the total coverage area for the considered scenarios.
The remainder of this paper is organized as follows. Section 2 gives a literature survey about
different deployment algorithms. The problem formulation is presented in section 3. Section 4
specifies the proposed FPA based deployment approach. In section 5, the simulation results and
discussion are given. Finally, section 6 concludes the paper.
2. LITERATURE SURVEY
Over the last years, researchers attempted to tackle the nodes deployment problem in WSN
through various optimization processes both by mathematical programming and through nature
inspired techniques. This problem was sometimes modelled as a one single objective problem, in
special cases deal with several objectives through well selected weights.
Yu et al. proposed a node placement algorithm for mobile sensor networks based on the strength
of van der Waals in order to improve the total coverage area. In fact, the proximity relationship
of nodes is defined by the Delaunay triangulation method, the frictional force is inserted into the
equation of force, the force calculated generate an acceleration in the movement of nodes. To
evaluate whether the nodes are uniformly distributed over the deployment field an evaluation
metric named pair correlation function was introduced in [9]. The Genetic Algorithm (GA) was
introduced as a solution for coverage holes problem in WSN [10]. This approach found the
optimal positions and the number of mobile nodes that have to be added to the initial deployment
schema. Simulation results prove that this algorithm has optimized network coverage in terms of
overall coverage ratio and additional number of mobile nodes. Sengupta et al. addressed the
problem of achieving an optimal trade-off between coverage, energy consumption, and lifetime in
WSN by using the multi-objective evolutionary algorithm (MOEA). They developed an enhanced
version of Multi-objective evolutionary algorithm based on differential evolution (MOEA/D-DE)
known as MOEA/DFD which includes the fuzzy dominance [11]. Sakamoto et al. proposed a
simulation approach founded on Particle Swarm Optimization (PSO). They focused on the size of
giant component and number of covered mesh clients (NCMC), which are important objective
functions to optimize Wireless Mesh Networks (WMNs) [12]. In their work, the authors of [13]
proposed a modified version of the original artificial bee colony (ABC); in fact, they change the
updating equation of onlooker bee and scout bee [14]. Indeed, some new parameters, such as
forgetting and neighbors factor for accelerating the convergence speed and probability of mutant
for maximizing the coverage rate were introduced [15]. Comparing their approach with the
deployment topology based on the traditional ABC and PSO algorithm, they found that the

former achieved better performance in terms of coverage and speed of convergence with less
moving distance sensor.
3. PROBLEM FORMULATION
The deployment of sensor nodes in WSN is to find the placement nodes topology or find the
coordinates of the sensor nodes in the two-dimensional plane. The most important concerns for
WSN are how improving the performances and optimizing the resources. Thus, an optimal
placement strategy ought to be considered to achieve the required goal. Here our objective is to
find an optimal placement schema that maximizes the coverage area without losing network
connectivity. For this, the following different mathematical models are described.
3.1. Preliminary
Sensor nodes in WSN are characterized by their positions in the 2D plane (x, y), sensing radius
Rs, and communication radius Rc. Given a multi-hop WSN, where all nodes collaborate in order
to ensure cooperative communication. Such network, can be defined as a linked graph, G = {V,
E}, where V is the set of vertices representing sensors and E is the set of edges representing links
between the sensors. Let u ϵ V and v ϵ V, (u, v) belongs to E if, and only if, u can send a message
directly to v (we say that v is neighbor of u). We assume that Rc is identical for all nodes. Let d(u,
v) be the distance between the nodes u and v, the set E can be defined as follows:
( ){ }2
, ; d(u,v) RCE u v V= ∈ ≤
The network coverage is defined by the sensing radius of the sensor nodes, whereas the network
connectivity is specified by the communication radius of the nodes.
3.2. Connectivity
Definition 1 (Node Degree). Given an undirected graph G. The degree Deg(u), of a vertex u ϵ V
is specified as the number of a vertex u ϵ V is specified as the number of neighbors of u [16].
Definition 2 (k-Node Connectivity). A graph is considered to be connected if for every pair of
nodes, there exists a single hop or a multi-hop path connecting them; otherwise the graph is called
disconnected. A graph is considered to be Q-connected if for any pair of nodes there are at least Q
reciprocally separate paths connecting them [16].
3.3. Binary Sensing Model
The coverage in WSN defined as the total area covered by a set of sensor nodes deployed in the
region of interest (ROI). This region is considered as m × n grids, each grid point size was equal
to 1 and denoted as G(x, y) (Figure. 2).

Figure 2. Sensor coverage in sensing field
Generally, the zone covered by a sensor node is a disk with radius equals to sensing radius of the
sensor. The binary sensing model considered that each grid point within the sensing radius of a
node can be considered as covered with probability equal to "1" and the point out of the sensing
range was set as "0" since it cannot be covered (Eq1). Thus, the coverage of the whole area is
proportional to the grid points that can be covered by at least one sensor Si(xi, yi) [17].
( ) ( )
2 2
1,
0,
i i sif x x y y R
P
otherwise
 − − − ≤
= 

4. THE PROPOSED APPROACH
This work interested by the node sensors deployment problem in WSN. In fact, we deal with area
coverage problem for random placement topology with predefined number of sensors. Here, the
main purpose was to improve the quality of coverage without affecting network connectivity
constraint. Evidently, to supply connected coverage to a zone, the set of disks used much cover
all points in that region and the connectivity graph of all the Rc-disks must form a single
connected component in a graph theoretic sense. The proposed approach, named Flower
Pollination Coverage Optimization approach (FPCOA), was a centralized approach based on
FPA, aimed to deploy all the sensor nodes in their positions carefully to form a WSN with
maximal coverage area.
4.1. Fitness function
The binary model was considered as sensing model (Section 2.3). The proposed approach is a
mono-objective deployment approach designed to optimize one objective function, namely the
ratio of total coverage target area. It is given by:
( )
1
( , , ) 1 1 ( , ,
N
i
i
P x y S P x y S
=
= − −∏

With N is the number of sensor nodes and P(x, y, Si) is the probability that a grid point G(x, y) is
covered by a sensor Si. So, the total coverage area is defined as:
1 1
( , , )
m n
x y
TotCovArea P x y S
= =
= ∑∑
And the ratio of total coverage area is given by:
TotCovArea
Total Coverage ratio
TotalGridArea
=
4.2. Constraints
The network connectivity is taken as a constraint in this optimization problem. Therefore one
path, at least, must exist from the sensor node to the sink node, to guarantee connectivity
4.3. Flower Pollination Coverage Optimization Algorithm (FPCOA)
The proposed approach composed of two main steps. The first step was the creation of the initial
population (Algorithm 1). The second step was the performing of the optimization process based
on FPA (Algorithm 2).
Initial Population. To create the initial population we considered that each individual was
represented by a vector of all sensor nodes position (x, y) in RoI. The WSN parameters are
described in Table 1.
Table 1. Parameters of WSN.

To create initial population, we began by generating the position of the sink node at the centre of
RoI (i.e., at xm/2 and ym/2) for each individual. Then, we deployed the remaining sensors by
taking into consideration the connectivity constraint. Actually, network connectivity is assumed
to be full if the distance between two sensors is less than the communication radius (Rc) of the
sensor. The distance is defined as the Euclidean distance between two sensors. In addition, to
insure a sufficient distribution in RoI, we controlled the number of neighbors of each deployed
node that should be less than a predefined number Ne (see Algorithm1).
Table 2. Pseudo code of Initial Population Creation.
Flower Pollination Algorithm (FPA). metaheuristics are generic algorithms, often inspired
from nature, designed to solve challenging optimization problems [17] [18]. Here, we considered
one of the most recent metaheuristic algorithms named Flower Pollination Algorithm (FPA),
developed by Xin-She Yang in the year 2012 [8] for the global optimization problems. FPA
inspired from the flower pollination process of flowering plants. In nature, flowers pollination
process resulting from the transfer of pollen, typically, by pollinators such as insects, birds, bats
and other animals. In fact, pollination process can be commonly classified into two types: self-
pollination and cross-pollination. The former can occur by the pollen of the same flower. The

latter can take place by pollen of a flower of a different plant [20] [21]. FPA has the following
four rules:
1. Cross-pollination is considered as global pollination process with pollen carrying; pollinators
performing Lévy flights.
2. Self-pollination is considered as local pollination.
3. Flower constancy can be defined as the reproduction probability is proportional to the
similarity of the two flowers involved.
4. Global and local pollination is controlled by a switch probability p ϵ [0, 1].
Table 3. Pseudo Code of Flower Pollination Coverage Optimization.

Here each flower was represented by a vector of all sensor nodes position (x, y) in RoI, fCurr1,
fCurr2, …,fCurrN was the flower population at iteration t, fNext1, fNext2, …,fNextN was the flower
population at iteration t +1, Nbflower was the total number of flower and the Current-Global -
Flower is the best solution found among all solutions at the current generation or iteration t. To
imitate the movement of pollinator [22], FPA uses Lévy flight. That is, we draw L > 0 from a
Lévy distribution:
01
( )sin( )
12~ (s s 0)L
s λ
πλ
λ λ
π +
Γ
? ?
The pseudo-code of FPA is presented in Table3.
5. SIMULATION AND RESULTS
To validate the proposed approach, some simulations were undertaken. We used a binary sensing
model the nodes are initially randomly distributed. The network is homogeneous, i.e., all sensors
have the same deployment parameters such as the sensing and communication radius.
Simulations were carried out using MATLAB R2016a. The algorithm was run a maximum
number of iterations of 3000. The average of 10 runs was recorded. For the simulations, we
considered a square area divided into a number of squares of 1 m2 each. The center of each of
these squares is taken as the demand point to detect by at least one sensor node. In this section,
the performance of the proposed FPCOA is evaluated with regard to the total coverage ratio.
Moreover, the obtained results were compared with those obtained with two metaheuristics
algorithms, namely, PSO and GA and, finally, the effect of the number of randomly deployed
sensor nodes was discussed.
5.1. Efficiency of the proposed approach
In order to test the performances of FPCOA, we considered a square area with each side 100m in
length. We considered also that the number of sensors and the communication radius Rc as well
as the sensing radius Rs as constant values. Here the number of sensors was set as 15, Rc as 15m
and Rs as 15m.
Table 4. Deployment results of FPCOA.
As seen in Table 4, the effective coverage area was improved significantly over the 3000
iterations. The decrease in the standard deviation values can be explained by the stability of the
algorithm with larger numbers of iterations. In fact, FCPOA improved the coverage ratio by
48.2% compared with the random initial distribution. To highlight this improvement, the best

deployments obtained by the FCPOA for initial and final configurations are shown in Figure 2
and Figure 3, respectively, where the colored areas represent detected coverage areas.
Figure 3. Initial configuration of Sensors Figure 4. Final configuration of Sensors
5.2. Comparison with other approaches
To evaluate the efficiency of our proposed approach we choose to compare our results with those
obtained with GA and PSO, respectively. Figure 5 gives the comparison of the coverage rate
tested on the same initial population for the three approaches.
Figure 3. Comparison of total coverage ratio with GA and PSO
From this figure, we can find that after the nodes reached stable distribution and obtained the
optimal placement topology, the proposed algorithm has better coverage rate than the other two
approaches. The results of the proposed approach clearly outperform both than GA and PSO
respectively. This figure shows that FPCOA gives a much more stable performance in total
coverage than both the two algorithms.

5.3. Effect of Number of Sensor Nodes
In order to show the effect of number of sensor on the total coverage ratio for the propose
approach, we considered that the sensor nodes were randomly deployed in a 50m×50m sensor
field, the communication radius Rc was set as 5m and the sensing radius Rs was set as 5m.
Figure 4. The Coverage Ratio vs. Number of Sensor Nodes
Figure 6 shows the coverage ratio when adding sensor nodes to the network for both of FPCOA
and PSO. As shown, the coverage ratio increases as the number of deployed nodes increases. This
figure indicates that the proposed approach offers higher coverage with less sensor nodes.
FPCOA requires around 32 sensor nodes to get 100% coverage compared to PSO which requires
34 sensor nodes. Thus, it can be said that FPCOA is able to offer higher coverage with the lowest
cost.
6. CONCLUSION
In this paper, the sensor placement problem for WSN is addressed. A deployment approach based
on FPA was proposed. This approach can find the optimal placement topology in terms one QoS
metric. The simulations results of the different scenarios prove that our proposed approach
achieved the optimal placement regarding coverage maximization and connectivity constraint. In
a future work, we will incorporate other QoS metrics like energy consumption and deal with
multi-objective node placement problem for the WSNs.
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AUTHORS
Faten Hajjej received the graduate degree in Computer Engineering from the
National School of Engineers of Sfax, University of Sfax, in 2009. She has been
pursuing the Ph.D degree with the Research Group on Intelligent Machines (REGIM-
Lab), University of Sfax, under the supervision of Dr. Ridha EJBALI and Prof.
Mourad Zaied. His research interests include the internet of things, wireless sensor
network, multi objective optimization, nature inspired optimization algorithms.
Ridha Ejbali received the Ph.D degree in Computer Engineering, Master degree and
computer engineer degree from the National Engineering School of Sfax Tunisia
(ENIS) respectively in 2012, 2006 and 2004. He was assistant technologist at the
Higher Institute of Technological Studies, Kebili Tunisia since 2005. He joined the
faculty of sciences of Gabes Tunisia (FSG) where he is an assistant in the Department
computer sciences since 2012. His research area is now in pattern recognition and
machine learning using Wavelets and Wavelet networks theories.
Mourad Zaied received the HDR, the Ph.D degrees in Computer Engineering and the
Master of science from the National Engineering School of Sfax respectively in 2013,
2008 and in 2003. He obtained the degree of Computer Engineer from the National
Engineering School of Monastir in 1995. Since 1997 he served in several institutes and
faculties in university of Gabes as teaching assistant. He joined in 2007 the National
Engineering School of Gabes (ENIG) as where he is currently an associate professor in
the Department of Electrical Engineering. He is a member of the REsearch Group on
Intelligent Machines laboratory (REGIM) http://www.regim.org in the National
Engineering School of Sfax (ENIS) since 2001. His research interests include
Computer Vision and Image and video analysis. These research activities are centered on Wavelets and
Wavelet networks and their applications to data classification and approximation, pattern recognition and
image, audio and video coding and indexing.

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