Arab J Sci Eng (2014) 39:6317–6325DOI 10.1007/s13369-014-1292-3

RESEARCH ARTICLE - ELECTRICAL ENGINEERING

Sensor Node Placement in Wireless Sensor Network UsingMulti-objective Territorial Predator Scent Marking Algorithm

H. Zainol Abidin · N. M. Din · I. M. Yassin ·H. A. Omar · N. A. M. Radzi · S. K. Sadon

Received: 29 May 2013 / Accepted: 30 September 2013 / Published online: 6 July 2014© King Fahd University of Petroleum and Minerals 2014

Abstract Optimum sensor node placement for wireless sen-sor network (WSN) in a monitored area is needed for cost-effective deployment. The location of sensor nodes must beable to offer maximum coverage and connectivity with mini-mum energy consumption. This paper proposes a sensor nodeplacement approach that utilizes a new biologically inspiredmulti-objective optimization algorithm that imitates the be-haviour of a territorial predator in marking their territorieswith their odours known as multi-objective territorial preda-tor scent marking algorithm (MOTPSMA). The algorithmuses the maximum coverage and minimum energy consump-tion objective functions with subject to full connectivity. Asimulation study has been carried out to compare the per-formance of the proposed algorithm with the multi-objectiveevolutionary algorithm with fuzzy dominance-based decom-position and an integer linear programming algorithm. Simu-lation results show that WSN deployed using the MOTPSMAsensor node placement algorithm outperforms the perfor-

H. Zainol Abidin (B) · I. M. Yassin · H. A. OmarFaculty of Electrical Engineering, Universiti Teknologi MARA,40450 Shah Alam, Selangor, Malaysiae-mail: husnaza@salam.uitm.edu.my

I. M. Yassine-mail: ihsan_yassin@salam.uitm.edu.my

H. A. Omare-mail: hasmila@salam.uitm.edu.my

N. M. Din · N. A. M. Radzi · S. K. SadonCentre for Communications Service Convergence Technologies,College of Engineering, Universiti Tenaga Nasional,Jalan Ikram-Uniten, 43000 Kajang, Malaysiae-mail: Norashidah@uniten.edu.my

N. A. M. Radzie-mail: Asyikin@uniten.edu.my

S. K. Sadone-mail: Sajaa@uniten.edu.my

mance of the other two algorithms in terms of coverage,connectivity and energy usage.

Keywords Sensor node placement · Wireless sensornetwork · Coverage · Connectivity · Energy · Territorialpredator scent marking algorithm · Biological inspired

1 Introduction

The special characteristics of wireless sensor network (WSN)allow them to be used in a wide range of applications, in-cluding environment monitoring, battlefield surveillance andhealth care. Another application and one of the most impor-tant areas of research is object tracking, in which wirelesssensors monitor and report the locations of moving objectswithin the network [1]. WSN for target monitoring appli-cations such as target tracking, landslide monitoring, forestfire detection and precision agriculture can be implemented

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using a fixed number of sensor nodes that are deployed tomonitor one or more locations within a monitored area. Oneway to provide WSN with maximum coverage, connectivityand minimum energy consumption is through an effectiveplanning mechanism in arranging the limited number of sen-sor nodes. For cost-effective deployment, it is critically im-portant to determine optimal locations for the sensor nodes.The locations of the sensor nodes strongly affect the energyconsumption, operational lifetime and sensing coverage [2].Thus, careful sensor node placement is needed. Romoozi [3]stated that there is a trade-off between energy consumptionof sensor nodes and network coverage. Closer sensor nodeswill reduce the energy consumption, but the network cover-age will become smaller.

Most researchers nowadays prefer the artificial intelli-gence (AI)-based approaches particularly those based on bio-logical inspired algorithms in solving optimization problemsin WSN. This is because AI is proven to be able to giveoptimum solution for complex problems. Particle swarm op-timization (PSO)-based algorithms for sensor node place-ment are mostly applied for sensor node placement. Some ofthese algorithms are virtual force directed co-evolutionaryPSO (VFCPSO) [4], improved PSO (IPSO) [5] and intelli-gent single particle optimizer (ISPO) [6]. Aziz et al. in [7]and [8] optimized the sensor node coverage using PSO foroptimal placement and Voronoi diagram to evaluate the fit-ness of the solution. Other sensor node placement algorithmsthat used biological inspired algorithms are genetic algorithm(GA) [9,10], artificial bee colony (ABC) [11], optimized arti-ficial fish swarm algorithm (OAFSA) [12] and the glowwormswarm optimization algorithm (GSO) [13].

This paper introduces a sensor node placement algorithmfor target monitoring by utilizing a new biologically inspiredoptimization algorithm known as territorial predator scentmarking algorithm (TPSMA). This algorithm imitates thebehaviour of a territorial predator in marking its territorieswith their odours. The sensor node placement problem couldnot be solved with a single optimal solution because the dis-tance between two sensor nodes will affect the coverage andthe energy consumption. Thus, our proposed algorithm con-siders a multi-objective optimization (MOO) concept thatwill be further discussed in Sect. 2. Our methodology is thenpresented in Sect. 3 followed by a simulation study in Sect. 4.Finally, based on the results obtained, a number of conclu-sions and recommendation for future work are pointed out inSect. 5.

2 Related Work

In many practical or real-life problems, there are several con-tradicting objectives that need to be optimized simultane-ously [14]. Thus, it is no longer reliable to depend on a single

optimal solution but rather a whole set of possible solutions ofequivalent quality. The sensor node placement problem alsofalls in this category. As mentioned in the previous section,there is a trade-off between the coverage and the energy con-sumption affected by the distance between two sensor nodes.None of the AI-based works presented in previous sectionconsider multi-objective optimization (MOO). Swarm intel-ligence (SI) that is based on information sharing of PSOand maintenance mechanism of an artificial immune system(AIS) to address coverage problems is given in reference[15]. This work can be considered as a MOO approach be-cause their main objectives are to obtain maximum coverageprobability with given number of sensor nodes and minimumnumber of sensor nodes with required coverage probability.Nevertheless, the objectives of this algorithm are not exe-cuted concurrently where the output for each objective willbe different. Thus, there will be two sets of sensor node coor-dinates to be produced. Lizhong et al. [16] proposed a nodedistribution optimization algorithm based on multi-objectivedifferential evolution algorithm (MDEA) that makes use ofsensor radius adjustment and mobility of the sensor nodesto improve the WSN coverage. MDEA is used to optimizethe distribution of the mobile sensor nodes in mobile WSN(MWSN) where the inputs are initial sensor node positionand sensor node sensing range while the objectives are max-imizing network coverage rate and network energy consump-tion of sensing. MDEA is also applied in paper [17] wherethe objectives are energy-related parameters. These two pa-pers used the weighting method where they aggregate themultiple objectives into one objective function consideringweights that can be fixed or dynamically changing during theoptimization process. However, it is not always possible tofind the appropriate weighted function [18].

The trend has shown that evolutionary algorithm (EA)has been enhanced for dealing with MOO problems [18].MOEAs aim at finding a set of representative Pareto optimalsolution in a single run. Most MOEAs are Pareto dominancebased, where single objective EA frameworks are adoptedand the fitness of each solution at each generation is mainlydetermined by its Pareto dominance relations with other so-lutions in the population. Most popular Pareto dominance-based algorithms are non-dominated sorting geneticalgorithm-II (NSGA-II), strength Pareto evolutionaryalgorithm-II (SPEA-II) and Pareto-archived evolution strat-egy (PAES) [18].

Yuan et al. [19] and Sengupta et al. [20] proposed a sen-sor node placement algorithm that utilizes MOEA approachwith decomposition and Pareto optimal concepts. Yuan et al.believed that MOEA is able to improve convergence speedand algorithm efficiency. MOEA approach in [19] consid-ers network size, power consumption and processing time astheir main objectives while Sengupta et al. in [20] suggestedMOEA which take into consideration of coverage, lifetime,

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energy consumption as well as the connectivity. The aim ofthis work was to reduce the total cost of the sensor nodes with-out compromising the coverage and connectivity. MOEA isalso applied to deploy MWSN [21] where the main objectivesof this work are to minimize energy consumption and max-imize the network coverage area. The energy consumptionis reduced by minimizing the mobile sensor node distancetravelled.

Pradhan and Panda [22] proposed a sensor node placementalgorithm that is based on multi-objective PSO with fuzzylogic-based strategy to select the best compromised solutionon the Pareto front. The two objectives that are considered inthis work are maximizing both coverage and lifetime. PSOwith fuzzy logic is also proposed by Rani and Devarajan in[23] where three main objectives are passed on to a fuzzylogic engine the form the fuzzy rules.

Following sections describe our TPSMA based sensornode placement algorithm using MOO.

3 Methodology

3.1 Territorial Predator Scent Marking Algorithm (TPSMA)

Territorial predator such as tigers, bears and dogs can bedefined as predators that consistently defend a specific areaagainst animals from other species. The territory is chosenbased on certain factor such as food resources. Most territo-rial predators use scent marking to indicate the boundariesof their territories which are also playing a role in territorialmaintenance and as information sites for other members ofthe population [24]. Chemical or olfactory communicationenables these animals to leave messages that are relativelylong lasting and can be read later by conspecifics. Further-more, it can also be used at night, underground or in densevegetation [24]. Animal odours can facilitate communica-tion between conspecifics according to four different func-tions, scent matching, reproductive signalling, temporal orspatial signalling and resource protection [25]. Scent match-ing allows a resident animal to distinguish other residentsfrom intruders by recognizing their scent, thereby reducingthe need for territorial encounters [25]. The marks may bedeposited by urination, defecation, rubbing parts of bodiessuch as chin and foot, scratching, using glands and vegeta-tion flattening [24]. For example, to identify its territory, themale tiger marks trees by spraying of urine and anal glandsecretions, as well as marking trails with scat. Dogs and othercanines scent mark by urinating and defecation, while catsscent mark by rubbing their faces and flanks against objects.Bear rubs their bodies that have scent glands against the sub-strate. There are two phases in TPSMA known as markingphase and matching phase.

1 Marking Phase:

Predator checks on all food resources on L locations andmarks the location with the highest food resource level,Lmarked:

∀x = {1, 2, . . . , L} : (Rx |Rx ∈ Z [0,∞)) (1)

Rx = F(x) (2)

Lmarked = max (Rx ) (3)

where Rx = food resources level at location x, F(x) = objec-tive function value and Lmarked = marked location

2 Matching Phase:

Predator locates the marked location Lmarked. Assume thatthe predator locates Lmarked based on linear search behaviouras shown in the algorithm below [26]:

procedure linear search (Lmarked: integer,R1, R2, . . . , RL : distinct integers)

i :=1while (i ≤ L ∧ Lmarked �= Ri )

i := i + 1if i ≤ L

then location := ielse location := 0return location

These phases are further illustrated in Fig. 1a, b, respectively.

3.2 WSN Sensor Node Placement Based on Multi-objectiveTPSMA (MOTPSMA)

Territorial predator scent marking behaviour can be adoptedin designing the sensor node placement algorithm where theterritory of a sensor node can be scent marked based on adesign objective such as maximum coverage, minimum uni-formity, minimum energy consumption and maximum con-nectivity. This is done based on the scent marking behaviourwhere normally predator will scent mark the area due to cer-tain factors such as food resources. Sensor node will identifyits monitored location based on their marked territories thatimitates the scent matching behaviour. As highlighted in theprevious section, the sensor node placement problem can beconsidered as a MOO problem as there is a conflict betweenthe coverage and the energy consumption of WSN. Closersensor nodes will reduce the energy consumption, but thenetwork coverage will become smaller. Based on this fact,it seems that the best trade-offs among the objectives can bedefined in terms of Pareto optimality [14].

MOTPSMA is done based on decomposition by apply-ing non-dominated Pareto optimal. Decomposition approach

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x = Rx

Rx

max(Rx) = Lmarked

(a)

i= Lmarked

(b)

Fig. 1 a Scent marking process. b Scent matching process

was developed based on a MOO algorithm which has been de-scribed in [18]. The algorithm decomposes a MOO probleminto a number of single objective optimization subproblemsand optimizes them simultaneously.

3.3 Dominance and Pareto Optimal

The concepts of dominance and Pareto optimal explained inthis section are based on a paper written by Sengupta, S. etal. [20]. Consider a MOO problem with n objective functions( f1, . . . fn) with D decision variables (x1, . . .xD):

Minimize:

�Y = f ( �X) = ( f1(x1, . . . , xD), . . . , fn(x1, . . . , xD)) (4)

subject to specific constraints:

gi (�x) ≤ 0, i = 1, . . . , ph j (�x) = 0, j = p + 1, . . . , q

(5)

where

Decision (parameter) vector, �X = [x1, . . . , xD]T ∈ P (6)

Objective vector, �Y = [y1, . . . , yn]T ∈ O (7)

P and O are the parameter space and objective space, re-spectively.

A decision vector �A ∈ P is considered to dominate an-other decision vector �B ∈ P if and only if:

∀i ∈ {1, . . . , n} : fi ( �A) ≤ fi ( �B) ∧ ∃ j ∈ {1, . . . , n} :f j ( �A) < f j ( �B) (8)

Hence, a non-dominated Pareto optimal solution can be de-fined as follows:

Let �A ∈ P be an arbitrary decision vector

• The decision vector �A is said to be non-dominated re-garding the set P ′ ⊆ P if and only if there is no vectorP ′ which can dominate �A.

• The decision (parameter) vector �A is called Pareto opti-mal if and only if �As non-dominated regarding the wholeparameter space P .

3.4 Problem Formulation

A monitoring area is broken up into a number of small squaretiles called monitoring locations. These monitoring locationsare also the potential locations of sensor nodes. Thus, thenumber of monitoring locations is equal to the number ofpotential locations for sensor node. Each monitored locationcan only be equipped with one sensor node. The centre ofeach square tile is considered as the monitoring point and

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may be monitored by more than one sensor node. The areais obstacle free.

The covering area of sensor nodes is assumed to be a circu-lar area with a radius of RS that represents the sensing range.Monitoring points that are located within that circle is saidto be covered by the sensor node. At least one sensor nodewould be able to cover a monitoring point. Sensor nodes arehomogeneous static nodes where all sensor nodes will havesimilar sensing range, RS and communication range, RC. Thenumber of sensor nodes must not exceed the number of mon-itoring locations, and at least one path must be there betweeneach sensor node and the sink node to ensure connectivity.Two sensor nodes are said to be connected if the distancebetween them is less than RC.

Each sensor node has a specific initial energy and energyconsumed by each sensor node affects the network lifetimebecause it is related to the working time of a sensor node.Energy consumed by the sensor node consists of three parts[20]:

• Maintenance energy: required for maintaining the sensornodes in active state

• Reception energy: depends on the number of sensor nodesfrom which it receives data for transmitting to sink node

• Transmission energy: depends on the path in which theenergy flows from the sensor node to the sink node

3.5 Mathematical/Placement Optimization Modelling

The parameters for the mathematical modelling are listed inTable 1.

A two-dimensional area, Z2 = {(x, y), 0 ≤ x ≤ l, 0 ≤y ≤ l} where l is the length of the monitoring area, isdeployed with N sensor nodes, with the sensor node setS = (s1, s2, . . . , sN ). The objective of this optimizationprocess is to determine the coordinates of the sensor nodesin a two-dimensional area that satisfy the required goals. Thedecision variable is a set of positions of sensor nodes in thearea as follows:

C = (c1, c2, . . . , cN ) (9)

where ci = (xi , yi ) is the coordinate of si

3.6 MOO Formulation

The first objective is to minimize the energy consumption.Energy consumed by each sensor node, Ei , can be determinedas follows [20]:

Ei = EMi + ETi × PiS + ERi × αi (10)

Table 1 Modelling parameters

Parameters Description

N Number of sensor node index by iS Set of sensor nodes

s Sensor node

P Number of sensor node potential locations

M Set of monitoring points index by p

m Monitoring point

x( j) Variable that indicates whether the monitoringlocation j is equipped with a sensor node or not

ss Sink node

EM Sensor node maintenance energy

ET Sensor node transmission energy

ER Sensor node reception energy

RS Sensing range

RC Communication range

PiS Cost of minimum path from a sensor node i to thesink node

αi Number of sensor nodes from which the sensor nodei receives data and transfer it to the sink node inmulti-hop communication

L Link between sensor nodes i and j

NCov Number of uncovered points

Objective function 1, f1 is the net energy consumed, E :

f1 = E =∑

i∈S

Ei (11)

The second objective is to maximize the area of coverage.Since we need to minimize energy, it is simpler to modelthe coverage as a minimization problem. Thus, coverage isexpressed as a number of uncovered points (NCov) functionthat is determined as follows:

NCovp ={

1 d(si , m p) > Rs

0 otherwise(12)

where d(si , p) is the Euclidean distance between monitor-ing point m p(xmp, ymp) and sensor node i, si (xsi , ysi ). TheEuclidean distance can be calculated as follows:

d(si , m p) =√

(xsi − xm p )2 + (ysi − ym p )

2 (13)

The objective function 2, f2 is the sum of NCov for all mon-itoring points:

f2 = NCov =∑

p∈M

NCovp (14)

The net multi-objective is:

min Y = F(�x) = ( f1, f2) (15)

There are two constraints of this optimization problem. First,the distance between any two sensor nodes must not exceed

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their RC and there must be at least a path from the sensor nodeto the sink node to ensure connectivity. Only one sensor nodecan be placed in each monitoring location. The monitoringlocations are marked with x(p) as follows to indicate whetherthe location is equipped with a sensor node or not.

x(p) =⎧⎨

⎩

1 if location p has a sensor node

0 otherwise(16)

const =

⎧⎪⎪⎨

⎪⎪⎩

d(si , si−1) ≤ RC, i ∈ {1, 2, . . . , N }αi ≥ 1, i ∈ {1, 2, . . . , N }x(p) = 0, p ∈ {1, 2, . . . , M}

(17)

The problem is based on the assumptions listed in Lemma 1,2 and 3 while the objectives are listed in Proposition 4 and5. Problem 6 formalizes the optimization problem.

Lemma 1 The area is obstacle free; the sensing and commu-nication ranges of all sensor nodes are identical and assumedto have a circular coverage area.

Lemma 2 Number of monitoring locations are equal to thenumber of sensor nodes potential locations.

M = P (18)

Lemma 3 Number of sensor nodes must not exceed the num-ber of monitoring locations.

N ≤ M (19)

Proposition 4 All sensor nodes should communicate witheach other and there must be at least a path from the sensornode to the sink node to ensure connectivity as shown in Eq.(17).

Proposition 5 Monitoring points must be covered by at leastone sensor node.

∀p ∈ {1, . . . , M} ∧ ∃i ∈ {1, . . . , N } : d (p, si ) ≤ RS (20)

Problem 6 Place the sensor nodes that will give maximumcoverage with minimum energy consumption with guaran-teed connectivity. Each monitoring location can only beequipped with not more than one sensor node. The problem isrepresented by Eqs. (11), (14) and (15) subject to constraintsin the Eq. (16) and (17).

3.7 Connectivity, Graph and Dijkstra’s Algorithm

Dijkstra’s algorithm [26] is used to check whether sensornodes are connected to the sink node or not. Dijkstra’s al-gorithm gives the minimum cost, PiS, and the number ofhops, αi , between the sensor nodes and the sink node. Agraph G is created containing all sensor nodes while an adja-cency matrix is created which contains the sensor nodes that

are connected. An edge (i, j) belongs to G if the distancebetween sensor nodes i and j is within the RC. Dijkstra’sshortest path algorithm is applied from each of the sensornodes to the sink node. The output returned by the algorithmis the cost of the path, which is used to determine PiS andthe sequence of nodes in the path that will give αi . Senguptaet al. [20] stated that graph theory is able to reduce the hugecomputational effort in formulating the objective functions.

4 Simulation Study

4.1 Simulation Network Model

A numerical simulation has been carried out using MATLABand network simulator (NS2) on Linux platform to demon-strate the performance and the effectiveness of the proposedalgorithm based on recent studies. Figure 2 depicts the mon-itoring area with 60 m × 60 m dimension and consists of 144equal width monitored locations. The properties of the sensornodes are listed in Table 2.

Figure 3 shows the convergence rate for MOTPSMA. Itcan be seen that the algorithm totally converged when the

Fig. 2 Simulation network model

Table 2 Sensor nodes properties

Parameter Value

Sensing range, RS 15 mCommunication range, RC 15 m

Initial energy 1 Ah

Sensor node maintenance energy, EM 13 mA

Sensor node transmission energy, ET 20 mA/m

Sensor node reception energy, ER 2 mA

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Fig. 3 Convergence rate of MOTPSMA

Fig. 4 Coverage ratio

number of iterations reached 400. Thus, 400 iterations areconsidered in the simulation work.

4.2 Simulation Results

WSN performance deployed with proposed sensor nodeplacement algorithm is studied in terms of coverage ratio,connectivity probability and the average energy consump-tion. Simulation results are compared with results producedby MOEA/DFD [20] and integer linear programming (ILP)-based algorithm which is focusing on minimizing the costproposed by Deyab et al. [27]. The simulation results of pro-posed algorithm are compared with these two algorithmsbecause they are recently studied and presenting the samenetwork scenario.

Figure 4 shows the coverage ratio of WSN for differentnumber of sensor nodes. As expected, the coverage ratioof WSN will increase as the number of sensor nodes in-creases. Coverage ratio offered by MOTPSMA is higher than

Fig. 5 Connectivity probability

Fig. 6 Average energy consumed by each sensor node for minimumof 95 % coverage

MOEA/DFD and Deyab’s algorithms which indicates thatMOTPSMA would be able to offer higher coverage withfewer sensor nodes. It can be seen that MOTPSMA needsaround 10 sensor nodes to get at least 95 % coverage com-pared to MOEA/DFD and Deyab’s that need 12 sensor nodesand 20 sensor nodes, respectively. In order to get 100 %coverage, MOTPSMA needs 24 sensor nodes compared toMOEA/DFD that needs 28 sensor nodes and Deyab’s al-gorithm that needs 30 sensor nodes. Thus, it can be con-cluded that MOTPSMA would be able to provide highercoverage with the lowest cost. Cost can be reduced around16 % compared with MOEA/DFD and 14 % compared withDeyab’s algorithm for the minimum of 95 % coverage. Inorder to achieve 100 % coverage, MOTPSMA outperformsMOEA/DFD by 14 % and Deyab’s algorithm by 20 % interms of the deployment cost.

As depicted in Fig. 5, MOTPSMA is able to provide 100 %connectivity with the lowest number of sensor nodes whileMOEA/DFD can be considered fully connected with a small

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-20 -10 0 10 20 30 40 50 60 70 80-20

-10

0

10

20

30

40

50

60

70

80Number of Nodes = 10

X coordinate (m)

Y c

oord

inat

e (m

)

(a)

-20 -10 0 10 20 30 40 50 60 70 80-20

-10

0

10

20

30

40

50

60

70

80Number of Nodes = 20

X coordinate (m)

Y c

oord

inat

e (m

)

(b)

Fig. 7 a Sensor node position for 10 sensor nodes deployed with MOTPSMA and b sensor node position for 20 sensor nodes deployed withMOTPSMA

number of sensor nodes compared with Deyab’s algorithm.This is because MOTPSMA and MOEA/DFD place the sen-sor nodes subject to full connectivity requirement. On theother hand, Deyab’s algorithm provides almost full connec-tivity when the number of sensor nodes gives at least 95 %coverage. As mentioned before, Deyab’s algorithm is focus-ing on minimizing the deployment cost and it is found thatthe lowest sensor nodes that they need in order to achievefull connectivity are around 24 sensor nodes.

From the figure, it can be seen that the connectivity prob-ability of Deyab’s algorithm is unstable at the beginning.It reaches a steady state when the number of sensor nodesgives approximately 95 % coverage. This is because, Deyab’salgorithm places the sensor nodes randomly without consid-ering any location constraint as required by MOTPSMA andMOEA/DFD algorithms.

Figure 6 depicts the average energy consumed by each sen-sor node when the minimum of 95 % coverage is achieved.The bar chart clearly shows that MOTPSMA has the lowestenergy usage compared with MOEA/DFD and Deyab’s al-gorithm. MOTPSMA consumed the lowest energy becausethe objective of the algorithm is to place the sensor nodesat the location that gives the lowest energy usage withoutconsidering other energy-based criterion. On the other hand,Deyab’s algorithm only considers the deployment cost anddid not consider the energy consumption.

Figure 7 illustrates samples of sensor nodes’ positions.The stars show the monitored points while the dots indicatethe sensor nodes. The circles show the coverage of the sen-sor nodes. Figure 7a shows WSN deployed with 10 sensornodes while Fig. 7b shows 20 sensor nodes. As can be seen,more locations are covered when the number of sensor nodesdeployed is increased.

5 Conclusion and Future Works

A multi-objective sensor node placement algorithm knownas MOTPSMA has been developed in order to ensure thatthe WSN coverage and connectivity will be maximized withminimum energy consumption for effective monitoring. Thesimulation network model presented in this paper is an areathat consists of several equal widths monitored locations.Based on the simulation results obtained, it can be concludedthat WSN deployed with proposed MOTPSMA sensor nodesplacement algorithm offers considerably higher coverage ra-tio, higher connectivity and lower energy consumption valuecompared with MOEA/DFD and Deyab’s algorithm. Thisresult implies that WSN deployed with proposed algorithmis expected to provide higher coverage and fully connectedwith longer lifetime. This work will be further extended withother objective function to address other concern issues indeploying WSN.

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