Hybrid differential evolution particle swarm optimization de pso based clustering energy optimizatio by IJRTER

Hybrid Differential Evolution - Particle Swarm Optimization (DE-PSO) Based Clustering Energy Optimization Algorithm for WSN Pooja Keshwer1, Mohit Lalit2 1 2

Dept. of CSE, Geeta Institute of Management and Technology, Kurukshetra, India Dept. of CSE, Geeta Institute of Management and Technology, Kurukshetra, India

Abstractâ&#x20AC;&#x201D; Wireless Sensor Network is a network which formed with a maximum number of sensor nodes which are positioned in an environment to monitor the physical entities in a target area, For example, temperature monitoring environment, water level, monitoring pressure, health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which can perform the adequate operation and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor network, energy conservation measures are essential for improving the performance of wireless sensor network. In this paper proposes Hybrid differential evolution particle swarm optimization (Hybrid DE-PSO) algorithm in wireless sensor network for better clustering and cluster head election with respect to minimizing the power consumption in wireless sensor network and maximizing the lifetime of the Wireless Sensor networks. The results are compared with competitive clustering optimization algorithm to validate the reduction in energy consumption. Keywordsâ&#x20AC;&#x201D; Wireless Sensor network, Energy efficient Hybrid DE-PSO optimization algorithm and network lifetime. I. INTRODUCTION Wireless sensor network is made of several number of tiny sensor nodes [8]. Each node has limited number of resources. Wireless sensor nodes is a battery-operated device, capable of sensing physical quantities, data storage, limited amount of computational , signal processing capability and wireless communication. Sensor nodes are usually set up in a large area and communicate with each other in short distance through wireless communication [1]. The best features of wireless sensor nodes include small size, low cost and computation power, multi functional and easy communication within short distance. However, various research techniques are carried out for preserving energy in sensor nodes to extend the network lifetime [1]. The architecture of WSN shows in Figure 1. It comprises wireless sensor nodes in huge number which has been arranged and installed based on the application and a sink that is located very near to or within the radio range. The sink transmits the queries to the neighboring nodes which perform the sensing task and return the data to the BT as an answer to the transmitted query.

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Figure 1. Network architecture of Wireless Sensor

Clustering in wireless sensor network plays a vital role. The life time of the sensor node (SN) can be increased if clustering techniques is being adapted in wireless sensor network (WSN) [10].Many clustering and routing algorithms are available for efficient data aggregation and transmission. Clustering method of data aggregation and transmission result in better lifetime as it eliminates the data redundancies. Generally in clustering networks, sensor nodes are grouped into various clusters and each cluster has a cluster-head (CH).All cluster nodes transmit the sensed data to its respective CH. Cluster Head aggregate the cluster nodeâ&#x20AC;&#x2122;s data and the aggregated data is directed to the sink node. Various goal of clustering in wireless sensor network [2]. 1. Data Aggregation. 2. Scalability. 3. Network lifetime maximization. 4. Connectivity guarantee. 5. Avoidance of energy holes. 6. Load balancing. 7. Latency reduction. Various traditional clustering algorithm to enhance the performance and throughput of the networks like low energy adaptive clustering hierarchy (LEACH), hybrid energy distributed clustering approach (HEED), energy efficient hierarchical clustering (EEHC) etc. But by the use of optimization algorithm We can find the optimal solution .so, various optimization algorithms are like particle swarm optimization (PSO), Differential Evolution (DE).but we propose Hybrid Differential Evolution particle swarm optimization (DE-PSO) algorithm the combination of both DE and PSO used for cluster formation and cluster head election to reduce the residual node in wireless sensor network and increase the network lifetime. II. LITERATURE SURVEY Many research works are concentrated on efficient clustering, data gathering, aggregation and routing techniques. Some of the major existing cluster based protocols are discussed below. Fuad Bajaber, Irfan Awan et.al, 2011[3] proposed an adaptive clustering protocol for wireless sensor network. This was called adaptive decentralized re-clustering protocol.In ADRP the cluster head and next heads are elected based on residual energy of each nodes and the average energy of

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

each cluster. This clustering algorithm was a technique used to reduce energy consumption. It could improve the scalability and lifetime of wireless sensor network. Selim Bayrakl, Senol Zafer Erdogan 2012 [4] presented genetic algorithm based method (GABEEC) was proposed to optimize the lifetime of wireless sensor network. Genetic algorithm was used to maximize the lifetime of the network by means of rounds. The method had two phases which are set-up and steady-state. Ablolfazl Afsharzadeh Kaz erooni 2015[5] proposed two clustering algoÂŹrithms LEACH and HEED. Low Energy Adaptive Clustering Hierarchy (LEACH): LEACH is a clustering mechanism that distributes energy consumption all along its network, the network being divided into clusters and cluster heads which are purely distributed in manner and the randomly selected CHs, collect the information from the nodes. LEACH protocol involves four main steps for each round such as advertisement phase, cluster set-up phase, program creation and data transmission phase. Distributed Clustering (HEED) is a distributed algorithm which selects the cluster head based on both residual energy and communication cost. Basically HEED was proposed to avoid the random selection of CHs. Manal Abdullah,Hend Nour Eld et.al, 2015 [11] Presented Clustering is one of the most effective techniques used to solve the problem of energy consumption in WSN. Grid based clustering had proven its efficiency especially for high dynamic networks. The grid's strategy used in this research was implemented on dense network and divided the networks area into multiple grid cells with different densities i.e., high, low, and empty. Then grids were combined to form clusters called normal and advanced clusters. Cluster head was elected for each cluster which was based on high energy. J. Rejina Parvin and C. Vasanthanayaki 2015 [6] proposed Particle swarm optimization (PSO)based effective clustering in wireless sensor networks. With the help of PSO, clustering is performed until all the nodes become a member of any of the cluster. This eliminates the residual node formation which results in comparatively better network lifetime. C. Vimalarani 2016 [7] proposed an Enhanced PSO-Based Clustering Energy Optimization (EPSOCEO) algorithm for Wireless Sensor Network to form clusters and cluster head selection with a combination of centralized and distributed method using static sink node. The enhanced PSO algorithm constructs clusters in a centralized manner within a base station and the cluster head are selected by using PSO in distributed manner. III. PROPOSED HYBRID DE-PSO BASED CLUSTERING In this paper effective cluster formation take place using Hybrid differential evolution particle swarm optimization to reduce the residual node formation. We propose a hybrid algorithm that is named Hybrid Differential Evolution Particle Swarm Optimization. This is a combination two algorithms. A) DE (Differential Evolution). B) PSO (Particle Swarm Optimization). The slow convergence in differential evolution can be resolved by PSO while easily trapping to local optimum of PSO improved by DE. The hybrid DE-PSO algorithm combines DE into PSO by separating the logic algorithm in the different round of work by odd and even round, but it shares the particle for a better solution because of most of the time the PSO value becomes the local value. It is probably not the best value, so DE helps this problem and most of the time DE computes with a long

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

period of time. DE-PSO works step by step by passing a particle to the PSO to separate the particle into many groups.Then,it compute one round and the result is sent to DE to do the Mutation and Crossover and the new result is sent back to PSO until it reaches the maximum generation. A. Differential Evolution (DE): A. Differential Evolution (DE): The DE algorithm was introduced by Storn and Price in 1995. Differential Evolution (DE) is optimization algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality [9]. Such methods are commonly known as meta heuristics as they make few assumptions about the problem being optimized and search very large space of candidate solutions. DE algorithm works by having a population of candidate solutions .These agents are moved around in the searchspace by using simple mathematical formula to combine the position of existing agent from the population. If the new position of agent is improve if it is accepted from part of the population, otherwise the new position is simply discarded. DE use distance and direction information from the current population to guide search process. For a D-dimensional search space, each target vector xi,k a mutant vector is generated by vi,k+1 = xr1,k + F (xr2,k-xr3,k) (1) where r1,r2,r3 {1,2,....,NP} are randomly chosen integers, must be different from each other and also different from the running index i. F (>0) is a scaling factor which controls the amplification of the differential evolution (xr2,k − xr3,k). In order to increase the diversity of the parameter vectors, crossover is introduced. The parent vector is mixed with the mutated vector to produce a trial vector uji, k+1. uji,k+1 ={ vji,k+1 (if randj <=CR) or ( j= jrand) Xji, k (if randj > CR) and (j≠jrand)

(2)

Where j = 1, 2……, D; rand ; CR is the crossover constant takes values in the range [0,1] and jrand (1,2,.....,D) is the randomly chosen index. B. PSO (Particle Swarm Optimization): PSO was introduced in 1995 by Kennedy and Eberhart.PSO simulates the behaviors of bird flocking. PSO is used to solve the optimization problems. In PSO, each single solution is a "bird" in the search space. We call it particle. All particle has fitness value which are evaluated by the fitness function to be optimized, and have velocity which direct the flying of the particles. The particles fly through the problem space by following the current optimal particle. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. For a D dimensional search space the position of the ith particle is represented as Xi = (xi1,xi2,..xiD). Each particle maintain a memory of its previous best position Pi = (pi1, pi2… piD) and velocity Vi = (vi1,vi2,…viD) along each dimension. At each iteration, the P vector of the particle with best fitness 'g' in the local neighborhood, and the P vector of the current particle are combined to adjust the velocity along each dimension and a new position of the particle is calculated using that velocity. The two basic equations which govern the working of PSO. The velocity vector and position vector are given by: vid=wvid+c1r1(pid-xid)+c2r2(pgd-xid) xid=xid+vid (4)

(3)

The first part of equation (3) represent the inertia of the previous velocity, the second part is tell us about the personal thinking of the particle.The third part represent the cooperation among particles and is therefore named as the social component. And c1, c2 are accleration constants and inertia

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

weight w are predefined by the user and r1, r2 are the uniformly randomly generated number in the range of [0, 1]. Objective Function. The objective function is calculated on the basis of energy consumption. According to optimization algorithms improve the lifetime of a network minimize the energy consumption. The fitness function is calculated by following steps:  Input:-individual [00000010010010010001001001……]1*n ,energy Where 1 represents the cluster head, 0 represent the sensor neighbor nodes  Output:- Fitness Value Step 1:- CH=find (individual==1) select the cluster head. Step 2:- Calculate the neighbor of cluster head (CH). nb Step 3:-Total IC = ∑dist (i, CH). i=1 where nb=neighbor node, CH=cluster head, dist=distance CH Step 4:-Total BSD = ∑bsdist (i). i=1 where BSD=base station distance n Step 5:-RemEnergy =∑energy (i). i=1 Step6:-Fitness value=(remEnergy+(totalIC/n) + (totalBSD/n)). IV. SIMULATION RESULTS The algorithms are compared on the basis of these graphs which are the result of their simulation. The comparisons of existing and proposed algorithms are in terms of number of dead nodes with total number of rounds shown with the help of graph. A. Number of Dead Nodes per Round: Figure 2 gives the graph which compares the performance of PSO and Hybrid DE-PSO in terms of number of remaining energy with total number of rounds. Green line represents the Hybrid DE-PSO and blue line represents the PSO. Graph shows that particle swarm optimization (PSO) have almost same residual energy up to initial 900 rounds as Hybrid differential evolution particle swarm optimization is having. The Hybrid DE-PSO shows improved performance of the remaining energy over random deployment after 900 rounds. B. Remaining Energy per Round: Figure 3 gives the graph which compares the performance of PSO and Hybrid DE-PSO in terms of number of remaining energy with total number of rounds. Green line represents the Hybrid DE-PSO and blue line represents the PSO. Graph shows that particle swarm optimization (PSO) have almost same residual energy up to initial 900 rounds as Hybrid differential evolution particle swarm optimization is having. The Hybrid DE-PSO shows improved performance of the remaining energy over random deployment after 900 rounds. C. Network Lifetime Comparison: The Figure 4 shows the graph of lifetime comparison of particle swarm optimization algorithm and differential evolution particle swarm optimization algorithm. Hence Hybrid DE- PSO algorithm is energy efficient than PSO algorithm .The DE-PSO efficiently utilize the energy than PSO. In the graph shows that in PSO at 1200 round all nodes are dead but in Hybrid DE-PSO nodes are dead at 1600 round.

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Figure 2. Comaprison of the performance of PSO and Hybrid DE-PSO in terms of number of dead nodes & number of rounds

Figure 3. Comaprison of the performance of PSO and Hybrid DE-PSO in terms of number of dead remaining energy with total number of rounds

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Figure 4. Shows the life time comparison of PSO and Hybrid DE-PSO

V. CONCLUSION In this paper, proposed an energy efficient Hybrid Differential Evolution Particle Swarm Optimization (DE-PSO) technique for clustering and better cluster head election. The network performance of the WSN is enhanced by DE-PSO algorithms in terms of increasing residual energy and active node. The simulation outcome shows that the projected Hybrid Differential Evolution Particle Swarm Optimization (DE-PSO) scheme gives improved performance in order to minimize the total consumed energy and increase the lifetime of WSN. In future, this work can be extending to improve the network lifetime and data transmission using multiple sink or mobile sink. REFERENCES G. Anastasi, M. Conti, M. Di Francesco, and A.Passarella,“Energy conservation in wireless sensor networks: a survey” Ad Hoc Networks, vol. 7, no. 3, pp. 537–568, 2009. 2. Suhas k.pawar, "A survey of cluster formation protocols in wireless sensor network" Multidisciplinary Journal of Research in Engineering and Technology, Volume 1, Issue 1 (April 2014). 3. Fuad Bajaber, Irfan Awan,"Adaptive decentralized re-clustering protocol for wireless sensor networks”, Journal of Computer and System Sciences 77 (2011) 282–292. 4. Selim Bayrakl, Senol Zafer Erdogan,"Genetic Algorithm Based Energy Efficient Clusters (GABEEC) in Wireless Sensor Networks”, The 3rd International Conference on Ambient Systems 2011 Published by Elsevier Ltd. Selection. 5. Ablolfazl Afsharzadeh Kaz erooni, "LEACH AND HEED clustering algorithm for wireless sensor network", Advances in Science and Technology Research Journal Volume 9, No. 25, March 2015. 6. J. RejinaParvin and C. Vasanthanayaki ," Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks", ieee sensors journal, vol. 15, no. 8, august 2015. 7. C. Vimalarani,"An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network", Hindawi Publishing Corporation Scientific World Journal Volume 2016. 8. Shabbir Hasan,"A Survey of Wireless Sensor Network”, International Journal of Emerging Technology and Advanced Engineering Volume 3, Issue 3, March 2013. 9. Malwinder singh,"survey on clustering and optimization techniques to develop hybrid clustering technique", International Journal of Computer Engineering and Applications, Volume VII, Issue I, July 14. 10. Parneet kaur,"Analysis of Various Clustering Techniques for Wireless Sensor Networks", International Journal of Computer Trends and Technology (IJCTT) – Volume 19 ,Jan 2015. 11. Manal Abdullah , Hend Nour Eldin et.al, "Density Grid- Based Clustering for Wireless" Sensors Networks ", Procedia Computer Science 65 ( 2015 ) 35 – 47. 1.

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