International Journal of Computer Networks & Communications (IJCNC)



                                Reza Azizi1, Hasan Sedghi2, Hamid Shoja3, Alireza Sepas-Moghaddam4
1Young Researchers and Elite Club, Bojnourd Branch, Islamic Azad University  Bojnourd, Iran
2Department of Information Technology Engineering, PNU, Assaluye, Iran
3Department of Computer Engineering and Information Technology, PNU, Tehran, Iran
4Young Researchers and Elite Club, Science and Research Branch, Islamic AzadUniversity, Tehran, Iran


Clustering problems are considered amongst the prominent challenges in statistics and computationalscience. Clustering of nodes in wireless sensor networks which is used to prolong the life-time of networksis one of the difficult tasks of clustering procedure. In order to perform nodes’ clustering, a number ofnodes are determined as cluster heads and other ones are joined to one of these heads, based on differentcriteria e.g. Euclidean distance. So far, different approaches have been proposed for this process, whereswarm and evolutionary algorithms contribute in this regard. In this study, a novel algorithm is proposedbased on Artificial Fish Swarm Algorithm (AFSA) for clustering procedure. In the proposed method, theperformance of the standard AFSA is improved by increasing balance between local and global searches.Furthermore, a new mechanism has been added to the base algorithm for improving convergence speed inclustering problems. Performance of the proposed technique is compared to a number of state-of-the-art

Wireless Sensor Networks, Artificial Fish Swarm Algorithm, Node Clustering, Energy


Optimization technique is used in order to minimize or maximize the outcome of a function byadjusting its factors. All proper values for this problem are called possible solutions and the bestone is optimal solution. Up to now, different approaches were proposed to perform optimizationprocess e.g. swarm intelligence methods [1]. Swarm intelligence techniques are based on relationsbetween animals and insects swarms which tries to detect the solutions that are close to optimalone. Particle Swarm Optimization (PSO) [2], Ant Colony Optimization (ACO) [3], Shuffled FrogLeaping Algorithm (SFLA) [4] and Artificial Fish Swarm Algorithm (AFSA) [5] are someexamples of swarm intelligence algorithms. All swarm intelligence algorithms are based onpopulation, where their iterative procedure leads to improve the position of individual inpopulation and subsequently, their movement toward the better positions.International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.3, May 201592As mentioned above, Artificial Fish Swarm Algorithm (AFSA) is one of the swarm intelligencetechniques [5]. The procedure of AFSA has been inspired from social behaviors of fish in nature,based on random search, population and behaviorism. This algorithm involves somecharacteristics i.e. high convergence speed, insensitivity to the initial values of artificial fish,flexibility, and fault tolerant which make it useful for solving optimization problems. Fish canfind the zones involving more foods in underwater world by individual or swarm searches.According to this property, AFSA model was proposed by Free-Movement, Food-Search,Swarm-Movement and Follow Behaviors in order to search the problem space. This algorithm isused in different applications [6] such as neural networks [7, 8], color quantization [9], dynamicoptimization problems [10], physics [11], global optimization [12-15], and data clustering [16].In recent years, Wireless Sensor Network (WSN) has been significantly considered byresearchers. Each sensor network includes a number of nodes, where each node involvesprocessing resources and a limited amount of energy. Sensor nodes can sense, measure andcollect information by some local decision and transmit it to the main station [17]. Intelligentsensor node consist of one or several sensors, one processor, a memory unit, a type of energysupply with low energy utilization, transmitter, receiver, and a stimulator. Battery is the primarysource in a sensor node which is not commonly rechargeable, so the node will be dead byfinishing batteries’ energy. Network lifetime is determined by two criteria: 1) the time in whichthe first node dies (FND) and 2) the time in which the last node dies (LND). Utilizing different
mechanisms to increase the lifetime of the network is considered amongst most challengeablesubjects in this domain.Many researches aim at enhancing the network lifetime by proposingduty cycling schemes [18], coverage targeted protocols [19] or novel routing protocols [20].One of the efficient solutions to increase the network’s lifetime is clustering of the nodes [21].
Clustering is especially important for sensor networks which contain a substantial number ofwireless sensors. Each network can be divided into smaller clusters and contain a cluster head(CH). Sensor nodes in every cluster transfer their informationto the relevant CH and CH collectsinformation and sends them to a primary station. In this way, cluster nodes can conserve moreenergy by reducing the range and length of the communication. Swarm and evolutionaryalgorithms can be used in the primary station to determine the members of each cluster along withthe cluster heads.In this research, a novel algorithm for clustering of nodes in wireless sensor networks has beenproposed, based on AFSA. The proposed algorithm along with Leach algorithm [22] and severalother ones have been used for the clustering process. The results of the simulations showed thatthe network’s lifetime has been increased by using the proposed method, compared to other ones.The rest of the paper is organized as follow: in the second section Artificial Fish SwarmAlgorithm is reviewed. The third section describes the proposed method and the forth one showsthe experiments and the results. The fifth sectionconcludes the paper.


AFSA is one of the swarm intelligence methods and evolutionary optimization techniques [5].The framework of this algorithm is based on functions that are modelled from social interactions of fish groups in the nature.In this algorithm, there are four functions modelled from fish behaviors in fish swarms. The firstfunction is free-move behavior: as in nature, fishes move freely in the swarm when they do notprey. The second function is prey behavior: certainly, every fish searches for its prey individuallyby means of its senses. These senses are sense of vision, smell and available sensors on theirbodies. In AFSA, the area where an artificial fish can sense a prey is modeled as a neighborhoodwith a visual-sized radius. The next function is follow behavior: when a fish finds food, otherswarm members also follow it to reach the food. The last function is swarm behavior: in nature,fish always try to be in the swarm and not to leave it in order to be protected from hunters.In the underwater world, a fish can discover areas that have more food, which is done byindividual or swarm search by the fish. According to this characteristic, an artificial fish (AF)model is expressed by four aforementioned behaviors. AFs search the problem space by thesebehaviors. The environment, where an AF lives, is basically the solution space and other AFsdomain. Food consistency degree in the water area is AFSA objective function. Finally, AFsreach to a point where its food consistency degree is maxima (global optimum).As it is observed in Fig. 1, AF perceives external concepts with a sense of sight. The currentposition of an AF is shown by vector X=(x1, x2, …,xn). The visual is equal to sight field of the AFand Xv is a position in visual where the AF wants to go. Then if Xv has better food consistencythan the current position of AF, it goes one step toward Xv which causes change in the AFposition from X to Xnext, but if the current position of AF is better than Xv, it continues searchingin its visual area. Food consistency in position X is fitness value of this position and is shownwith f(X). The step is equal to the maximum length of the movement. The distance between twoAFs which are in Xi and Xj positions is shown by Disi,j=|| Xi-Xj|| (Euclidean distance) that iscomputed by Eq (1)

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