Efficient Coverage and Routing in Wireless Sensor Networks using Firefly Algorithm

IJSTE - International Journal of Science Technology & Engineering | Volume 4 | Issue 5 | November 2017 ISSN (online): 2349-784X

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm Aqib Rasool Bhat Arshid Iqbal Khan Department of Electronics and Communication Engineering Department of Electronics and Instrumentation Technology ICL IET KUK University of Kashmir Raqib Rasool Bhat Department of Electronics and Communication Engineering GGGI KUK

Abstract Sensor nodes are mobile with no specific infrastructure due to which the routing process has become more difficult as connections between nodes can change dynamically. The routing and coverage is a main concern of researchers. The major challenge is to propose an algorithm that can optimize the various parameters, in order to achieve an efficient routing and coverage in WSN. Routing is a well know technique to improve the lifetime of sensor node as the energy consumption is decreased by improving the technique of transferring data in the network. This paper presents a new nature encouraged algorithm known as Firefly algorithm that can tackle combinational and optimization multimodal issue more efficiently. The proposed algorithm is developed with novel fitness function based on residual energy, distance and node degree. The performance of the algorithm is tested on various scenarios and compared with existing algorithm such as DHCR, EADC and Hybrid Routing that shows that the present algorithm performs better than the already existing ones.

Keywords: Firefly Algorithm, WSN, Cluster Head Selection, Routing ________________________________________________________________________________________________________ I.

INTRODUCTION

Wireless sensor networks are the broad assembly of compact sized nodes used for data gathering applications. The collected data is forwarded to the centre point (base station) either directly or through other nodes for processing [3]. Exhausted sensor nodes in the communication have intimidated the performance of the network. These exhausted sensor nodes cannot transmit or receive data. They do not have the ability to reserve adequate amount of energy is one of the major problems of WSN. To solve this problem, a number of routing algorithms have been proposed. In hierarchical routing, the network is categorized into sub networks called as clusters. In each cluster, one of the node is selected to receives data from its respective nodes and transmit to the base station known as cluster head (CH).In hierarchical routing, CH selection and data transmission are two main functions. In CH selection, the complexity of computation explosively increases with the size. Though, it cannot be captured by brute force approach [2]. In data transmission, Cluster Head (CH) forwards data to the Base Station (BH) either directly or through other CH’s that directly depends on the transmission distance between the CH and the BS. The well-known problem is to select the routing path.

For optimization, its computational complexity increases with the network size. Therefore, by brute force approach it cannot be solved [6]. The higher complexity of above discussed problems has prompted investigators to use various nature inspired techniques such as genetic algorithm [4], ant colony optimization [9], particle swarm optimization [11, 5] and many more.

Meguerdichian, et al.[24], focused on the coverage in WSN. Which is one of the basic issues that outlay the quality of service provided by a particular sensor node? They used polynomial time algorithms to solve the questions optimally. However, their algorithms depend mainly on some geometrical structures such as the Delaunay triangulation and the Voronoi diagram which is impossible to be constructed locally or even efficiently in a random distributed manner. In addition, the correctness of using these two geometry structures is not presented in their paper. Firefly algorithm [14] is a latest technique for the optimization and needs to apply to a number of problems. It works on the basis on firefly flashes and attraction. The sequence made by fireflies is unique and it rely on the specific species. There are two important functions i.e., attracting the matting partners and prey. For attracting to mate, females respond to males with a special sequence belonging to the same species. There is an inverse proportionality between the light emitted by fireflies and the distance, which means that the intensity of light decreases as the distance between the two fireflies increases. This conduct of fireflies is applied that serve as the basis of firefly algorithm. In the present study, the firefly algorithm has been assumed to solve routing problems in WSN. The main issues in WSN are location, deployment and tracking.

The main objective of the paper is to establish a methodology that has high performance, efficient routing and coverage. Firefly algorithm based routing technique provides a complete solution for multi-hop communication between the CH and BS by considering residual energy, distance and node degree in its fitness function formulation. In experimental study, the existing conventional algorithms are compared with the proposed F ARW algorithm.

The rest of the paper is organized as follows. Problem formulation related to already existing algorithms is described in section 2. The proposed solution to the problems is described in section 3. The implementation of the proposed algorithm is described in

All rights reserved by www.ijste.org

210

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

section 4. Simulation and results are shown in section 5. The inferences are described in section 6. Finally paper is concluded in section 7.

II. PROBLEM FORMULATION This section emphasize on recent advances in routing protocols based on heuristic and meta-heuristic techniques.

Routing: The basic clustering algorithm is the LEACH, its main motive is to save adequate amount of energy in WSN [7]. In this data is transmitted from each sensor to its respective CH from which it is directly transmitted to BS. In practical scenario, the direct communication between CH and BS is impossible in large sized network. So, to settle down the limitations of LEACH and its corresponding algorithms, a number of routing methods are discussed in literature. One of the basic algorithms is HEED that has been proposed by O. Younis et al [15]. In this CH is selected by considering residual energy. If two or more nodes have equal residual energy, one of them is selected by ARM P cost function. Therefore instead of transmitting data directly it is transmitted via multiple nodes for communication. Therefore, life of the network is increased. M.R. Seneca et al. [13], improve the operation of HEED by multi-hop connectivity within the cluster. Therefore, by reducing the energy draining of each CH node during transmission the network life is improved. W.K. Lai et al. [10] have propounded an unequal cluster formation method. Unequal sized clusters are formed using cluster load. Thus therefore, it enhances the performance of network and reduces the possibility of cluster reformation. Hakan Bagci et al. [25] presented a fuzzy approach that used asymmetrical clustering in WSN. The efficiency and accuracy of data in wireless sensor networks is maintained by Clustering. The hotspot problem occurs at the base station when the data is received. The hotspot problem is resolved by decreasing the intra cluster nodes in WSN, a fuzzy algorithm based approach is implemented. A.E.A.A. Abdulla et al.[l], have proposed hybrid routing mechanism. Its motive is to eradicate hot-spot issue. In communication, the network performance is not evaluated on the basis of position of hybrid boundary. The data is transmitted using flat routing in the hot-spot zone and using hierarchical outwards.J.Yu et al. [16], have proposed an EADC protocol. For the selection of next node, the input criterion is energy and node degree. Therefore, network life is improved.

However, by taking distance in consideration, the performance of the network can be further improved. S. Maryam et al. [12] have proposed a DHCR algorithm. It takes node degree, residual energy and transmission power into consideration in the next-hop selection. Sensor nodes have limited energy supply and bandwidth. In order to eradicate the energy inefficiencies there is need of innovative techniques which will also increase the network life. These innovative techniques pose many challenges to the management and design of large number of sensors in WSNs [17]. In Minimum Energy Routing (MER) for sending data-packet from source node to destination node a least amount of energy is consumed. To know the information about minimum energy routes, routes link energy should be provided by existing minimum energy routes and frequent maintenance of energy cost [18].

Coverage: In wireless sensor networks, the sensor nodes are usually situated at random positions in sensor field. The biggest challenge is the coverage that has impact on monitoring and tracking of object. Reference [19] put forward the polynomial time algorithms to obtain the maximal support path and maximal breach path that are least and best observed within the sensor network. A coveragepreserving node scheduling scheme is discussed in Document [20] .Reference [21] discusses a probe-based density control algorithm to assure long lived and robust sensing coverage by taking some nodes in a sensor-dense area to doze mode. The efficacy of cluster-based distributed sensor networks rely to a large extent on the coverage provided by the sensor deployment in [22] and the paper put forward a virtual force algorithm (VFA). Document [23] labels how to dynamically keep up two important measures in the quality of the coverage of a sensor network: the best-case coverage and worst-case coverage distances, and gives relevant algorithm.

III. PROPOSED SOLUTION The Fire-Fly algorithm was proposed by Xin-She Yang. Fire-Fly algorithm is a meta-heuristic algorithm that came into existence because of the flashing behaviour of the Fire-Flies. The flash of the Fire-Flies act as a signal to attract other Fire-Flies with the aim of finding the location of the particle that results in the best evaluation of the given fitness function. This algorithm works on three main rules as given below: 1) Fire –Flies will be attracted towards each other regardless of their sex i.e. all Fire-Flies are unisex.

2) The attractiveness of a Fire-Fly is directly proportional to the brightness i.e. on increase of the brightness of Fire-Fly attractive between the Fire-Flies increases and inversely proportional to the distance i.e. on increase of distance the attractiveness decreases.

3) Objective function determines the brightness of a fire fly.

The objective function of the Fire-Fly algorithm is given below 2 I = Io e[âˆ’Îłr ] ... (1) Where Iois the original fluorescence intensity at the location of đ?‘&#x; = 0, and đ?›ž is the fluorescence absorption coefficient. This equation gives the Florescence Intensity of the Fire-Flies.

All rights reserved by www.ijste.org

211

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

The attractiveness of a Fire-Fly is defined as below 2

ď ˘  ď ˘ exp[  ď § r ]

‌ (2)

0

Where β0is their attractiveness at đ?‘&#x; = 0 that is the two Fire-Flies are located at same point, đ?›ž is the fluorescence absorption coefficient and r is the distance between two Fire-Flies.

The displacement of Fire-Fly i on getting attracted to another more attractive firefly đ?‘—is determined by 2 ď „ x  ď ˘ exp[  ď § r ]( x  x )  ď Ą ò i j i i

‌ (3)

Where r  ( x j  x i ) is the Cartesian distance between two fireflies i and j at xi and xj locations, ι is the step size controlling parameter and while ξi is a vector drawn from a Gaussian distribution or other distribution.

Quality of Service determines the capability of the network to provide better service to selected network over all technologies.

(QOS) parameters are different for different networks. Some networks emphasize on having as fast as possible delivery of data in which the delay is to be minimized where as some networks focus on providing accurate data in which robustness is emphasized.

So we can say lot of parameters can be set to determine the quality of service (QOS) of an algorithm. Here Fitness function determines the Quality of Service (QOS) of the algorithm. Actually Fitness function determines the network lifetime which determines the (QOS).The goal of Quality of Service (QOS) is to provide guarantees on the ability of a network to deliver predictable results.

IV. IMPLEMENTATION Fire-Fly Algorithm Pseudo Code: The existing Fire-fly algorithm proposed is represented by pseudo code as below:  Objective Function; f(x)  Generate initial population of Fireflies (i = 1,2,3,4,‌‌‌,N)  Intensity of Florescence I of a firefly Ui at location Xi is determined by I  I

0

       

Define Light absorption Coefficient Îł while t < Max Generation for i = 1 : n (all n fireflies) for j = 1 : n (all n fireflies) if ( Ij >Ii ) Move Fire-Fly i towards Fire-Fly j; end if Vary attractiveness with distance r via

ď ˘ ď&#x20AC;˝ ď ˘

0

exp[ď&#x20AC;­ď § r

2

]

exp[ ď&#x20AC;­ď § r

2

]

;

ď&#x20AC;­ ď&#x20AC;­ ď&#x20AC;­ ď&#x20AC;­ ď&#x20AC;­ ď&#x20AC;­ ď&#x20AC;­

Evaluate new solutions and update light intensity; end for j end for i Rank fireflies and find the current best; end while Post-Processing the results and visualizations; End As we see in the code the firstly, the fire flies are generated that is the initial population is set. After the generation of fireflies the objective function is called and the florescence intensity of each firefly is determined. The firefly having more florescence intensity will become the master and all other fireflies having less florescence intensity will get attracted towards this firefly. The attractive of the firefly with less florescence intensity towards higher one is determined by equation ď ˘ ď&#x20AC;˝ ď ˘ e x p [ ď&#x20AC;­ ď § r 2 ] . The 0

distance between initial location and the final location is determined with the help of equation given by 2 ď &#x201E; x ď&#x20AC;˝ ď ˘ e x p [ ď&#x20AC;­ ď § r ]( x ď&#x20AC;­ x ) ď&#x20AC;Ť ď Ą ò . After this the fireflies are ranked in order of the maximum florescence intensity and i

j

i

i

the firefly which is current best that is the firefly having maximum intensity is obtained.

V. SIMULATION AND RESULTS In this part we will check the results of the algorithms implemented. We will calculate average residual energy, coverage probability, number of sensor nodes used to obtain full coverage and fitness function value using all the three algorithms conventional greedy algorithm, conventional firefly algorithm and modified firefly algorithm when number of nodes (NN) = 50 and number of POIâ&#x20AC;&#x2122;s (NP) =10.

All rights reserved by www.ijste.org

212

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

Modified Fire-Fly vs. Greedy Algorithm: Average Residual Energy:

Fig. 1: Average Residual Energy

Here we took into account two algorithms which are modified firefly algorithm and greedy algorithms. Greedy algorithm is conventional one and modified algorithm is proposed one. In the first simulation we keep the simulation time as the x-axis and vary it according to residual energy of the sensors. A stationary network with a fixed number of targets and sensors randomly deployed around the targets is simulated. The range of each sensor is considered fixed and so is the initial energy of the sensor which is kept equal for all sensors without any loss of generality. As the slope of the modified meta-heuristic algorithm is greater than already existing heuristic algorithm we can say that the modified algorithm consumes less energy as a result modified firefly algorithm is energy efficient algorithm than greedy algorithm and this proves soundness of algorithm.

Coverage Probability: In second simulation we keep the simulation as the x-axis and varied it according to coverage probability. Assuming we are doing the simulations in ideal conditions, the coverage probability achieved by modified firefly algorithm is almost equal to 100% and is much better than the greedy algorithm as shown by graph in Fig.2.

Fig. 2: Coverage Probability

Number of Sensors Used to cover POIâ&#x20AC;&#x2122;s: We know that because of cost, labour, physical work like constraints number of sensor deployed must be less. In this simulation part we will show that using modified firefly algorithm numbers of sensor nodes used to cover the area are less than that of greedy algorithm. Thus this will reduce complexity during the deployment of sensor nodes. The cost figure will also be reduced by a margin. The graph in Fig.3 shown is having simulation time along x- axis and number of sensor required to deploy in order to cover all Point Of Interests (POIâ&#x20AC;&#x2122;S) along Y-axis

All rights reserved by www.ijste.org

213

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

Fig. 3: Number of sensor nodes used to cover POI’s

Fitness Function: In fourth simulation we have calculated the value of fitness function which is equal to product of residual energy possessed by the network and the probability of covering the POI’s for both modified firefly algorithm and greedy algorithm. Because of greed in greedy algorithm, this algorithm always try to achieve better probability using more sensor nodes as a results the energy consumed is more. But keeping the energy constraint in mind the energy of sensor nodes must be conserved to the larger extent. The modified algorithm because of no greed gives equal preference to both the parameters resulting in better results. Also convergence parameter (ξ) in the fitness function converges the computations as early as possible which in turn results in increase in energy of the network.

The graph is shown in Fig.4

Fig. 4: Fitness Function Value

Modified Fire-Fly vs. Conventional Fire-Fly Algorithm: In this section modified firefly algorithm is compared with conventional algorithm when number of nodes (NN) = 50 and number of POI’s (NP) =10.

All rights reserved by www.ijste.org

214

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

Average Residual Energy:

Fig. 5: Average Residual Energy

Coverage Probability:

Fig. 6: Coverage Probability

Number of Sensors Used to cover POIâ&#x20AC;&#x2122;s:

Fig. 7: Number of Sensors Used to cover POIâ&#x20AC;&#x2122;s

All rights reserved by www.ijste.org

215

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

Fitness Function:

Fig. 8: Fitness Function Value

VI. INFERENCES From the graph in Fig. 5 it is clear that the average residual energy of a sensor node is more in new modified meta-heuristic algorithm proposed than conventional one. In fact careful observation reveals that because of the addition of the new convergence parameter (ξ), the modified algorithm provided better sensor utilization than the conventional one.

The graph in Fig.6 shows that the coverage probability is also greater when using modified firefly algorithm as compared with the conventional one.

The graph in Fig.7 determines the length of set, which is the number of sensor required to cover all the POI’s in the given area.

From the graph we have in case of modified algorithm the number of sensors required is less than that of conventional algorithm.

This is an important parameter that needs to be taken care of.

The graph in Fig.8 shows the fitness function value of each of the algorithm is calculated. Because of the convergence parameter the fitness function value is far better than the conventional fire fly algorithm Modified Fire-Fly vs. Conventional Fire-Fly vs. Conventional Greedy Algorithm: In this section all the three algorithms conventional greedy algorithm, conventional firefly algorithm and modified firefly algorithm are compared with each other. The number of nodes (NN) = 50 and number of POI’s = 10.

Average Residual Energy:

Fig. 9: Average Residual Energy

All rights reserved by www.ijste.org

216

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

Coverage Probability:

Fig. 10: Coverage Probability

Number of Sensors Used to cover POIâ&#x20AC;&#x2122;s:

Fig. 11: Number of Sensors Used to cover POIâ&#x20AC;&#x2122;s

Fitness Function:

Fig. 12: Fitness Function Value

VII. CONCLUSION This paper applied firefly algorithm to improve the performance parameters of already existing heuristic algorithms such as residual energy, coverage probability, the no. of sensors required to obtain full coverage and fitness function. From simulation and results

All rights reserved by www.ijste.org

217

Efficient Coverage and Routing in Wireless Sensor Network using Firefly Algorithm (IJSTE/ Volume 4 / Issue 5 / 038)

we have shown that modified firefly algorithm and firefly algorithm is energy efficient algorithm than the greedy algorithm. The coverage probability achieved by modified firefly algorithm is almost 100% and is much better than greedy algorithm. The cost and complexity during deployment will be reduced because the no. of sensors required to cover full area of POI’s is less with modified firefly algorithm and firefly algorithm compared with other conventional algorithms. In fourth simulation we have calculated the value of fitness function which is equal to product of residual energy possessed by the network and the probability of covering the POI’s for both modified firefly algorithm and greedy algorithm. Because of greed in greedy algorithm, this algorithm always try to achieve better probability using more sensor nodes as a results the energy consumed is more. But keeping the energy constraint in mind the energy of sensor nodes must be conserved to the larger extent. The modified algorithm because of no greed gives equal preference to both the parameters resulting in better results.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

Ahmed EAA Abdulla, Hiroki Nishiyama, and Nei Kato, Extending the lifetime of wireless sensor networks: A hybrid routing algorithm. Computer Communications, 35(9):1056-1063, 2012.

Pankaj K Agarwal and Cecilia Magdalena Procopiuc. Exact and approximation algorithms for clustering. Algorithmica, 33(2):201-226,2002, Ian F Akyildiz, Weilian Su, YogeshSankarasubramaniam, and ErdalCayirci, Wireless sensor networks: a survey. Computer networks, 38(4):393-422,2002, SelimBayrakl and SenolZaferErdogan, Genetic algorithm based energy efficient clusters (gabeec) in wireless sensor networks, Procedia Computer Science, 10:247-254,2012.

Khalil Bennani and Driss EI Ghanami. Particle swann optimization based clustering in wireless sensor networks: the effectiveness of distance altering, In Complex Systems (ICCS), 2012 International Conference on, pages 1-4. IEEE, 2012.

Marco Dorigo, Mauro Birattari, and Thomas Stiitzle. Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4):28-39,2006.

Wendi RabinerHeinzelman, AnanthaChandrakasan, and HariBalakrishnan. Energy-efficient communication protocol for wireless microsensor networks. In System sciences, 2000. Proceedings of the Yucheng Kao and Kevin Cheng, An aco-based clustering algorithm. In Ant Colony Optimization and Swarm Intelligence, pages 340-347. Springer, 2006, Wendi B Heinzelman, Anantha P Chandrakasan, and HariBalakrishnan, An application-specific protocol architecture for wireless microsensor networks, Wireless Communications, IEEE Transactions on, 1(4):660-670,2002.

Wei Kuang Lai, Chung Shuo Fan, and Lin Yan Lin, Arranging cluster sizes and transmission ranges for wireless sensor networks.Information Sciences, 183(1):117-131,2012.

NM Latiff, Charalampos C Tsimenidis, and Bayan S Sharif. Energy- aware clustering for wireless sensor networks using particle swarm optimization.

InPersonal, Indoor and Mobile Radio Communications, 2007, PIMRC 2007, IEEE i8th international Symposium on, pages 1-5. IEEE, 2007, Maryam Sabet and Hamid Reza Naji. A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEUinternational Journal of Electronics and Communications, 69(5):790-799,2015, Mustapha RedaSenouci, AbdelhamidMellouk, HadjSenouci, and Amar Aissani. Performance evaluation of network lifetime spatial-temporal distribution for wsn routing protocols. Journal of Network and Computer Applications, 35(4):1317-1328, 2012.

Xin-She Yang. Firefly algorithms for multimodal optimization. In Stochastic algorithms: foundations and applications, pages 169-178. Springer Berlin Heidelberg, 2009, [15] OssamaYounis and Sonia Fahmy. Heed: a hybrid,energy-efficient, distributed clustering approach for ad hoc sensor networks, Mobile Computing, IEEE Transactions on, 3(4):366-379,2004.

Jiguo Yu, Yingying Qi, Guanghui Wang, and Xin Gu, A cluster- based routing protocol for wireless sensor networks with nonuniform node distribution.

AEU-International Journal of Electronics and Communications, 66(1):54-61,2012.

Baghyalakshmi, et al, "Low Latency and energy Efficient Routing Protocol for Wireless Sensor Network" IEEE 978-1-4244-5137-1/10/$26.00, 2010.

I. Solis and K. Obraczka, "The Impact of Timing in DataAggregation for Sensor Networks”, Proc. of the IEEE International Conference on Communications (ICC), pp. 1-8, 2015.

S.Meguerdichian, F. Koushanfar, G. Qu and M. Potkonjak. Coverage problem in wireless ad-hocnetworks. IEEE INFOCOM, 2001:1380-1387.

D. Tian and N.D. Georganas. A coverage-preserving node scheduling scheme for large wireless sensor networks. ADM Int’l Workshop on Wireless sensor Networks and applications, 2002.

F. Ye, G. Zhong, S. Lu et al. Peas: a robust energy conserving protocol for long-lived sensor networks. Int’l Conf. On Distributed Computing Systems, 2003.

Yi Zou, KrishnenduChakrabarty. Sensor deployment and target localization in distributed sensornetworks.ACM Transactions on Embedded Computing.

HaiHuang,AndreaW.Richa.Dynamiccoveragein Ad-Hoc sensor networks. Mobile Networks and Applications, 2005, 10:9-17.

Seapahn Meguerdichian, FarinazKoushanfar, MiodragPotkonjak and srivastava. “Coverage problems in wireless ad-hoc sensor networks,” in IEEE INFOCOM’01, 2001.

Bagci, Hakan and Adnan Yazici, “An energy fuzzy approach to unequal clustering in wireless sensor networks,” Applied Soft Computong, 2013, 13(4), pp: 1741-1749.

All rights reserved by www.ijste.org

218