Firefly Optimization Approach to Sensor Deployment for Coverage Area Optimization in WSNs

Volume 2, Spl. Issue 2 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Firefly Optimization Approach to Sensor Deployment for Coverage Area Optimization in WSNs Rajan Sharma Deptt. Of Electronics & Communcation Engg

University Institute of Engineering, Chandigarh University, Gharuan (Punjab)INDIA Email: rajansharma@cumail.in

Abstract— This paper proposes a soft computing based

approach to sensor deployment in a Wireless Sensor Networks (WSN’s). We use firefly optimization approach for coverage area optimization. The use of soft computing approach increases the performance of Wireless Sensor Networks and makes them more intelligent. The approach was implemented using MATLAB. The results indicate that the Firefly Algorithm (FA) is a very promising optimizing algorithm for coverage area optimization in WSN. Index Terms— Firefly, Optimization, Wireless Sensor Network (WSN).

I. INTRODUCTION Wireless sensor network (WSNs) is a demanding field that has been target of research in the last decade and increasing its popularity due to its wide range of applications. WSNs consist of network of wireless nodes that have the capability to sense a parameter of interest.

The sensed parameter is relayed to base station through network formed amongst these nodes. The node integrates Programming, Computation, and Communication Sensing onto a single system. The sensing node is powered by a limited battery, which is at times is not possible to replace or exchange. So major constraint is to reduce power consumption. In the beginning, WSNs were used simply for military purposes in the battle fields but at present their use is extended in many other civilian areas for controlling and monitoring the different processes. The practical implementation of WSN applications for smarter world will be smart cities, smart environment, smart metering, security & emergencies, retail, logistic, industrial control, smart animal farming, domestic and home automation & e-health.

The sensor networks are different from other networks as it does not focus on human interaction but instead focus on interaction with the environment. The basic features of sensor network are self organizing capabilities, short range broadcast communication and multi hop routing, dense deployment and cooperative effort of sensor nodes, frequently changing topology due to selective fading and node failures and having limitations in energy, transmit power, memory and computing power.

Sandeep Chakravorty Deptt. of Electrical Engg.,, School of Engineering and Emerging Technology, Baddi University of Emerging Sciences and Technology, Baddi (HP) INDIA Email: sandeep@baddiuniv.ac.in Owing to their applications, the WSN have a considerable importance in the recent past for area coverage, point coverage and barrier coverage. One major problem in the area of sensor networks is the coverage problem. This problem deals with the ability of the network to cover a certain area or certain events. Coverage problem is classified into three different types [1]: • Area coverage: Area coverage is how to cover an area with the sensors. The objective here is to maximize the coverage percentage • Point coverage: The objective of point coverage is the coverage for a set of points of interest i.e fixed or moving points.

• Barrier coverage: Barrier coverage is about covering the barrier of an area. Barrier coverage can be considered as the coverage with the goal of minimizing the probability of undetected penetration through the barrier (sensor network). Therefore, the sensors need to be deployed along the area’s border.

The coverage reflects how well the network can detect in the monitored area. The sensor network can be deployed by scattering sensor units across the area based on the parameters such as network size, communication distance, sensor period, number of sensors in different hops, initial energy of each sensor, energy cost for transmitting and receiving packets etc [2][3]. Coverage is one of the key factors for QoS in evaluation of WSN. The Coverage and connectivity problems are due to limited sensing range of the sensor nodes and communication range. To overcome the problem of connectivity, the sensors need to be placed close to each other so that they do not cross the limits of the communication range. Whereas to ensure the coverage problem concerns that each points in the region of interest (ROI) is covered by the sensors. In order to minimize the coverage problem, the sensors should not be placed too close each other so that the sensing capability of the network is not fully utilized and also not too far from each which result in forming coverage holes (area outside sensing range of sensors) [4]. . The most challenging concern in sensor deployment is how to save node energy while maintaining the desirable network behavior and to maximize network lifetime.

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Volume 2, Spl. Issue 2 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

The paper is organized as follows, section I contains brief introduction, section II explain the related work, section III presents the methodology, section IV presents the simulation and results. Conclusions are drawn in section V II. RELATED WORK Soft computing approaches to area coverage problem have been explained in [1], they proposed a novel method for the problem of dynamic point coverage in wireless sensor networks using learning automata. Experimental results showed that the proposed algorithm, regardless of the sensor nodes’ density, number of the sensor nodes, and sensing radius of the sensor nodes, outperforms the similar existing methods in terms of the network lifetime Each node is equipped with a learning automaton which will learn (schedule) the proper on and off times of that node based on the movement nature of a single moving target. In [4 ], they proposed new algorithm for sensor network to optimize coverage using deployment strategies and particle swarm optimization (PSO) to minimize the coverage holes and maximize the coverage area.

As the system complexity grows, application of classical approaches become increasingly difficult. Soft computing approaches can be applied to the problems where best solutions can be replaced with optimally available solutions with high probability. In this paper we apply Fire fly algorithm (FA) to maximize the coverage area.

Figure 1: Pseudo code for Firefly Algorithm The light intensity of the firefly is thus attractiveness and is inversely proportional to the distance r from the light source. Thus, the light and hence, the attractiveness decreases as the distance increase.

(1) I

III. FIREFLY ALGORITHM Nature is the best available teacher on earth. The firefly algorithm (FA) is a natured inspired, optimization metaheuristic algorithm which is based on the social (flashing) behavior of fireflies. Primarily firefly’s flash acts as a signal system to attract other fireflies. Some of these flashing characteristics of fireflies can be modeled into firefly-inspired algorithms. The flashing characteristics of fireflies can be idealized in to following three rules [5]-[7]: 1. All fireflies are assumed to be unisex, so that one firefly is attracted to other firefly regardless of their gender.

2. Attractiveness is proportional to their brightness, thus for any two flashing fireflies, the less bright one will move towards the brighter one. The attractiveness is proportional to the brightness and they both decrease as their distance increases. If no one is brighter than a particular firefly, it moves randomly.

3. Brightness of the firefly is affected or determined by the landscape of the objective function to be optimized.

The two important variables, in firefly algorithm are light intensity and the attractiveness. A Firefly is attracted toward another firefly whose flash is brighter than its own flash. The attractiveness is dependent on the light intensity.

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r

= light intensity, = light intensity at distance zero i.e. original light intensity, = the light absorption coefficient = distance between firefly i and j

Attractiveness is proportionally to the light intensity seen by the another fireflies, thus attractiveness is β

(2) = Attractiveness at r is 0 The distance between two fireflies can define using Cartesian distance

(3) Firefly i is attracted toward the more attractive firefly j, the movement is defined as (4) In equation (4), the first term is for attraction, is the limitation when the value is tend to zero or too large. If approaching zero ( , the attractiveness and brightness become constant, . In another word, a firefly can be seen in any position, easy to complete global search. If the is nearing infinity or too large ( , the attractiveness and brightness become decrease. The firefly movements become random. The implementation of firefly algorithm BUEST, Baddi

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Volume 2, Spl. Issue 2 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

can be done in these two asymptotic behaviors. While the second the term is for randomization, as is the randomize parameter. The can be replace by ran -1/2 which is ran is random number generated from 0 to 1.

IV. SIMULATION RESULTS The FA was implemented in MATLAB and simulations were performed. We carried out 50 simulation trials. The minimum, the maximum and the mean performance is placed in the table below: Minimum Coverage 77.9086

Mean Coverage 78.7987

Max Coverage 79.3342

The iteration Vs performance graph of one of the trial run is placed as in fig 3: A. SIMULATION RESULTS

Fig 2 : Deployment of Sensors

Fig 3 : Iteration Vs. Coverage Area.

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V. CONCLUSIONS Soft computing approaches are very powerful approaches to NP hard or NP complete problems. Firefly algorithm based upon the flashing behavior of fire flies was used to optimize the coverage area by 7 sensors each with a radio range of 2 metres.i.e We implemented the proposed approach in MATLAB. 50 trial runs were conducted. We observe that FA was successfully able to deploy 7 sensors in a 100 m2 area with minimum, average and maximum coverage area of 77.9086 . 78.7987 and 79.3342 m2 respectively.

REFERENCES 1. M. Esnaashari1 and M. R. Meybodi, “Dynamic Point Coverage in Wireless Sensor Networks: A. Learning Automata Approach”. Springer, 2009 2. David J. Stein, Esq., “ Wireless Sensor Network Simulator”. Version:1.1, 2006.

3. M. Ilyas, I. Mahgoub, "Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems", CRC Press, London, Washington, D.C., 2005.

4. Shagufi Singla, Paramjeet Singh , “Performances Analysis of Coverage Problem in Wireless Sensor Network by using Particle Swarm Optimization (PSO) and Deployment Strategy “, An International Journal of Engineering Sciences, Issue December 2014, Vol. 1 ISSN: 2229-6913 (Print), ISSN: 2320-0332 5. Yang, X. S. 2009. Firefly algorithms formultimodal optimization in: Stochastic Algorithms: Foundations and Applications (Eds O. Watanabe and T. eugmann), SAGA 2009, Lecture Notes in Computer Science, 5792, Springer-Verlag, Berlin, pp. 169-178.

6. Yang, X. S. 2010. Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Computation. 2(2) : 78-84.

7. Yang, X. S. 2010. Firefly algorithm, levy flights and global optimization. Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, Petridis), Springer London, pp. 209-218.

8. J. Yicka, B. Mukherjeea, and D. Ghosal, “Wirelesssensor network survey,” Elsevier, vol. 58, no. 12, pp. 2292– 2330, Aug. 2008.

9. , “Performances Analysis of Coverage Problem in Wireless Sensor 10.

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