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  1. 1. ISSN: 2277 – 9043 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING VOLUME 1, ISSUE 2, APRIL 2012 Energy Efficient Clustering and Routing Techniques for Wireless Sensor Networks Anish Kumar U, Anita Panwar, Ashok Kumar self-organize into clusters and each cluster is managed by a Abstract—Minimization of the number of cluster heads in a set of associates called head-set. Using round-robinwireless sensor network is a very important problem to reduce technique, each associate acts as a cluster head (CH). Thechannel contention and to improve the efficiency of the sensor nodes transmit data to their cluster heads, whichalgorithm when executed at the level of cluster-heads. In this transmit the aggregated data to the base station. Moreover,paper, an efficient method based on genetic algorithms (GAs) to the energy-efficient clusters are retained for a longer periodsolve a sensor network optimization problem is proposed. Longcommunication distances between sensors and a sink in a of time; the energy-efficient clusters are identified usingsensor network can greatly drain the energy of sensors and heuristics-based approach. In this paper an improvementreduce the lifetime of a network. By clustering a sensor network over HCR protocol is achieved by using a Genetic Algorithminto a number of independent clusters using a Genitic (GA) to determine the number of clusters, the cluster heads,Algorithm, the total communication distance can be the cluster members, and the transmission schedules. Jin et.minimized, thus prolong the network lifetime. Simulation al [9] have also used GA for energy optimization in wirelessresults show the proposed algorithm can find a optimal solution sensor networks. In their work, GA allowed the formation offor multihop transmission scenario. a number of pre-defined independent clusters which helped in reducing the total minimum communication distance. Their results showed that the number of cluster-heads is Index Terms— Genetic algorithm, Wireless sensor networks, about 10% of the total number of nodes. The pre-definedshortest distance, clustering. cluster formation also decreased the communication distance by 80% as compared with the distance of direct transmission. I. INTRODUCTION Ferentionos et. al [10] extended the attempts proposed by Jin Wireless Sensor Networks (WSNs) are becoming an et. al [9] by improving the GA fitness function. The focus ofessential part of many application environments that are used their work is based on the optimization properties of geneticin military and civilians. The key applications of WSN are algorithm. However, we use GA to determine the energyhabitat monitoring, target tracking, surveillance, and security efficient clusters and then cluster heads choose theirmanagement [1], [2]. The application of WSN consists of associates for further improvement. Finally, to increase thesmall sensor nodes that are low-cost, low-power and overall performance, simple heuristics are used to retain amulti-functional. These small sensor nodes communicate few energy efficient clusters for a longer duration than thewithin short distances. Since energy consumption during other ones.communication is a major energy depletion factor, thenumber of transmissions must be reduced to achieve In this paper, we use a radio model described in [6]. In thisextended battery life [3], [4]. model, for a short range transmission such as within clusters, the energy consumed by a transmit amplifier is proportionalCluster-based approaches are suitable for continuous to d2, where d is the distance between nodes. However, for amonitoring applications [5], [6]. For instance, Heinzelman et long range transmission such as from a cluster head to theal. [6], describe the LEACH protocol, which is a hierarchical base station, the energy consumed is proportional to d4.self organized cluster-based approach for monitoring Using the given radio model, the energy consumed ETij toapplications. The data collection area is randomly divided transmit a message of length l bits from a node i to a node j isinto several clusters, where the numbers of clusters are given by Equation 1 and Equation 2 for long and shortpre-determined. Based on time division multiple accesses distances, respectively.(TDMA), the sensor nodes transmit data to the cluster heads, ETij  lEe  l l di j .......... 4which aggregate and transmit the data to the base station. .......... .......... 1) ..(Bandyopadhyay and Coyle [5], describe a multi-level ETij  lEe  l s di j .......... 2 .......... .......... 2) ..(hierarchical clustering algorithm, here the parameters forminimum energy consumption are obtained using stochastic Moreover, ER, the energy consumed in receiving the l-bitgeometry. Hussain and Matin [7], [8] propose a hierarchical message, is given by:cluster based routing (HCR) protocol where nodes E R  lEe  lE BF .......... .......... .......... 3) .(Anish Kumar, Dept. of Electronics and communication NIT Hamirpur, Where EBF represents the cost of beam forming approach to(e-mail: anishkumar_u@yahoo.com.). H.P, India reduce the energy consumption. The constants used in theAnita panwar, Dept. of Electronics and communication radio model are as follows: a) energy consumed by theNIT Hamirpur,(e-mail:anitapanwar01@gmail.com).H.P, India amplifier to transmit at a shorter distance is εs = 10 pJ/bit/m2,Ashok kumar, Dept. of Electronics and communicationNIT Hamirpur, (e-mail:ashoknitham@gmail.com ). H.P, India b) energy consumed by the amplifier to transmit at a longer 141 All Rights Reserved © 2012 IJARCSEE
  2. 2. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 1, March 2012distance is εl = 0.0013 pJ/bit/m4, c) energy consumed in the In this, one-point crossover is used. If a regular nodeelectronics circuit to transmit or receive the signal is Ee = 50 becomes a cluster-head after crossover, all other regularnJ/bit, and d) energy consumed for beam forming EBF = 5 nodes should check if they are nearer to this newnJ/bit. cluster-head. If so, they switch their membership to this newThe remainder of this paper is organized as follows: Section head. This new head is detached from its previous head. If aII describes the proposed technique of GA-based intelligent cluster-head becomes a regular node, all of its members musthierarchical clusters. Section III discusses the simulation and find new cluster-heads. Every node is either a cluster-head orprovides the results and discussion. Finally, Section 1V a member of a cluster-head in the network (Fig 1).concludes the paper and provides a few directions for thefuture work. II. INTELLIGENT HIERARCHICAL CLUSTERS The HCR protocol [7], [8] is enhanced by using GA tocreate energy-efficient clusters for a given number oftransmissions. The GA outcome identifies the suitablecluster heads for the network. The base station assigns Fig.1. Single point crossovermember nodes to each cluster head using the minimumdistance strategy. The base station broadcasts the complete Mutationnetwork details to the sensor nodes. The broadcast message The mutation operator is applied to each bit of anincludes: the number of cluster heads, the members individual with a probability of mutation rate. When applied,associated with each cluster head, and the number of a bit whose value is 0 is mutated into 1 and vice versa (fig. 2).transmissions for this configuration. All the sensor nodesreceive these packets transmitted by the base station andclusters are configured accordingly; this completes thecluster formation phase. Next comes the data transfer phase,where nodes transmit messages to their cluster heads for agiven number of transmissions. Fig.2. An example of mutation The base station uses a genetic algorithm to create Selectionenergy-efficient clusters for a given number of The selection process determines which of thetransmissions. The node is represented as a bit of a chromosomes from the current population will matechromosome. The head and member nodes are represented as (crossover) to create new chromosomes. These new1s and 0s, respectively. A population consists of several chromosomes join to the existing population. This combinedchromosomes. The best chromosome is used to generate the population will be the basis for the next selection. Thenext population. Based on the survival fitness, the population individuals (chromosomes) with better fitness values havetransforms into the future generation. Initially, each fitness better chances of selection. There are several selectionparameter is assigned an arbitrary weight; however, after methods, such as: “Roulette-Wheel” selection, “Rank”every generation, the fittest chromosome is evaluated and the selection, “Steady state” selection and “Tournament”weights for each fitness parameter are updated accordingly. selection. In Roulette-Wheel, which is going to use,The genetic algorithms outcome identifies suitable clusters chromosomes with higher fitness compared to others andfor the network. The base station broadcasts the complete they will be selected for making new offspring. Then, amongnetwork details to the sensor nodes. These broadcast these selected chromosomes, the ones with lesser fitness thanmessages include: the query execution plan, the number of others will be removed and new offspring would be replacedcluster heads, the members associated with each cluster head, with the former ones.and the number of transmissions for this configuration. Allthe sensor nodes receive the packets broadcasted by the base Fitness parametersstation and clusters are created accordingly; thus the cluster The total transmission distance is the main factor we needformation phase will be completed. This is followed by the to minimize. In addition, the number of cluster heads candata transfer phase. factor into the function. Given the same distance, fewer cluster heads result in greater energy efficiency.Problem representation Finding appropriate cluster heads is critically important The total transmission distance (total distance) to sink:for minimizing the distance. I use binary representation in The total transmission distance, TD, to sink is the sum of allwhich each bit corresponds to one sensor or node. “1“means distances from sensor nodes to the Sink (Fig 3).that corresponding sensor is a cluster-head; otherwise, it is aregular node. The initial population consists of randomlygenerated individuals. Genetic algorithms are used to selectcluster-heads. Crossover 142
  3. 3. ISSN: 2277 – 9043 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING VOLUME 1, ISSUE 2, APRIL 2012 100 TD  RCSD  10 * N  TCH  F   E TD N As Explained here E means the essential energy for sending information from cluster to sink, TD is the total distance of all the nodes to sink, RCSD is the total distance of Regular nodes to clusters and the total distances of all the clusters to sink, N and TCH are the number of all the nodes and clusters. For calculating this function, the amounts of N and TD are fixed, but the amounts of E, TCH and RCSD are changing. The shorter the distance, or the lower the number Fig.3. Total distance from sensor node to sink of cluster-heads, the higher the fitness value of an individual is. My Genetic algorithm tries to maximize the fitness value Cluster distance (regular nodes to cluster head, cluster to find a good solution.head to sink distance): The cluster distance, RCSD, is thesum of the distances from the nodes to the cluster head and III. RESULTS AND DISCUSSIONSthe distance from the head to the sink. (Fig 4) In this section, the performance of the suggested method will be evaluated. For this, we use MATLAB software. In our experiment we suppose that the base station with one interval is out of wireless sensor network area. The number of sensor nodes for this experiment is about 200. The following parameters have been taken from LEECH protocol. The used parameters in this experiment are shown in table 1. Fig 4—RCSDTransfer energy (E): Transfer energy, E, represents theenergy consumed to transfer the aggregated message fromthe cluster to the sink. For a cluster with k member nodes,cluster transfer energy is defined as follows: K E   ETjh  KER  EThs Table I. Parameter table J 1 The nodes are distributed in an environment like Fig. 5. The first part of Equation shows the energy consumed to The place of sink is considered at the middle of thistransmit messages from k member nodes to the cluster head. environment, i.e. [0, 0].The second part shows the energy consumed by the clusterhead to receive k messages from the member nodes. Finally,the third part represents the energy needed to transmit fromthe cluster head to the sink. The number of cluster-heads is represented with TCH andthe total number of nodes with N. Fitness function The used energy for conveying the message from clusterto sink and the sending distance are the main factors that weneed to minimum them. In addition to these, it is possibleinsert the decreasing number of clusters in our function that it Fig.5. Distributed nodescan affect the energy function like the decreased sendingdistance, because the clusters use more energy in spite of Fig.6 shows the method of the formation of clusters afterother nodes. So the chromosome fitness or F is a function doing the genetic algorithm. In this figure, the sink is shown(Fitness Function) of all the above fitness at the middle of the environment with a red star. The regular Parameters that I define it like Equation (2). nodes and the corresponding clusters are shown with different colors circles and cluster heads are shown by the 143 All Rights Reserved © 2012 IJARCSEE
  4. 4. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 1, March 2012star which is bigger than the normal nodes. Fig .6 selection of cluster group Fig.9. RCSD Fig.7 shows the amount of fitness function after each Fig.10 shows the movement of data during single hopgeneration. As we can see, after each generation the transmission scenario. In this scheme the member nodes ofsuggested algorithm increases the amount of fitness and the associated cluster head sends the data to thealmost after 50 circuits of doing algorithm, the amount of corresponding cluster heads which eventually sends the datafitness will reach to its maximum. to the sink Fig.7. The amount of fitness The following figure is related to the number of clusterheads. As it is expected, after each generation the number of Fig.10. Path showing movement of data during single hopcluster heads is decreased (Fig.8). transmission Fig.11 shows the movement of data during multi hop transmission scenario. In this scheme the member nodes of the associated cluster head sent the data to the corresponding cluster heads and data moves through different cluster heads and eventually reaches the sink Fig.8. The number of cluster heads Fig.9 shows the distance which data takes to reach sinknode decreases after each generation. With figure 8 we canunderstand that the total distance is decreased after eachgeneration. Fig.11. Path showing movement of data during multi hop transmission Fig.12 shows the performance evaluation for single hop and multi-hop transmission scenario. Here the evaluation is 144
  5. 5. ISSN: 2277 – 9043 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING VOLUME 1, ISSUE 2, APRIL 2012done based on the number of live nodes for each [9] S. Jin, M. Zhou, and A. S. Wu, “Sensor network optimization using a genetic algorithm,” in Proceedings of the 7th World Multiconferencetransmission rounds. Fig.12 shows the number of live nodes on Systemics, Cybernetics and Informatics, 2003.of a network during 1000 circuits.Data1 shows the single hop [10] K. P. Ferentinos, T. A. Tsiligiridis, and K. G. Arvanitis, “Energytransmission and data 2 shows multi hop situation after optimization of wirless sensor networks for environmental measurements,” in Proceedings of the International Conference ongenetic algorithm optimization. As seen in a figure, the Computational Intelligence for Measurment Systems and Applicatonssensor nodes for multi hop transmission method stay alive (CIMSA), July 2005.longer than sensor nodes for single hop scenario. Anish kumar was born at kollam town in Kerala, INDIA. He received his B.Tech Degree in Electronics & Communication Engineering with Honors from Kerala University. Presently he is Pursuing M.Tech from National Institute of Technology Hamirpur, Himachal Pradesh, INDIA from Communication Systems & Networks in Electronics & Communication Engineering Department. His current research areas of interest include wireless communication and wireless sensor networks. Fig.12 performance evaluation for single hop and multihop transmission IV. RESULTS AND DISCUSSIONSIn this paper, a new clustering algorithm for minimizing thenumber of cluster heads is presented. GA-based method tominimize communication distance in sensor networks viaclustering is being proposed. The algorithm begins by Anita Panwar was born at Jaipur town in Rajasthan ,randomly selecting nodes in a network to be cluster heads. INDIA. She received her B.E Degree in Electronics & CommunicationBy adjusting cluster-heads based on fitness function, our Engineering with Honors from Rajasthan University. Presently she is Pursuing M.Tech from National Institute of Technology Hamirpur,algorithm is able to find an appropriate number of Himachal Pradesh, INDIA from Communication Systems & Networks incluster-heads and their locations. Simulation results show Electronics & Communication Engineering Department. Her currentthat the new approach is an efficient and effective method for research areas of interest include wireless communication and wirelesssolving this problem. sensor networks. She has published 3 research papers in International Journals and conference in these areas V. REFERENCES[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Computer Networks, vol. 38, no. 4, pp. 393– 422, March 2002.[2] D. Estrin, D. Culler, K. Pister, and G. Sukhatme, “Connecting the physical world with pervasive networks,” IEEE Pervasive Computing, pp. 59 – 69, January-March 2002.[3] V. Mhatre, C. Rosenberg, D. Koffman, R. Mazumdar, and N. Shroff, “A minimum cost heterogeneous sensor network with a lifetime constraint,” IEEE Transactions on Mobile Computing (TMC), vol. 4, Ashok Kumar is working as Associate Professor in no. 1, pp. 4 – 15, 2005. the Department of Electronics and Communication Engineering, National[4] N. Trigoni, Y. Yao, A. Demers, J. Gehrke, and R. Rajaramany, Institute of Technology Hamirpur Himachal Pradesh- INDIA. Presently he is “Wavescheduling: Energy-efficient data dissemination for sensor pursuing PhD from National Institute of Technology. His current research networks,” in Proceedings of the International Workshop on Data areas of interest include wireless communication and wireless sensor Management for Sensor Networks (DMSN), in conjunction with the networks. He has published more than 20 research papers in International Confernece on Very Large Data Bases (VLDB), August International/National journals and conferences in these areas. He is life 2004. member of ISTE.[5] S. Bandyopadhyay and E. J. Coyle, “An energy efficient hierarchical . clustering algorithm for wireless sensor networks.” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM), 2003.[6] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energyefficient communication protocol for wireless microsensor networks,” in Proceedings of the Hawaii International Conference on System Sciences, January 2000.[7] S. Hussain and A. W. Matin, “Base station assisted hierarchical clusterbased routing,” in Proceedings of the International Conference on Wireless and Mobile Communications (ICWMC). IEEE Computer Society, July 2006.[8] A. W. Matin and S. Hussain, “Intelligent hierarchical cluster-based routing,” in Proceedings of the International Workshop on Mobility and Scalability in Wireless Sensor Networks (MSWSN) in IEEE International Conference on Distributed Computing in Sensor Networks (DCOSS), June 2006, pp. 165–172. 145 All Rights Reserved © 2012 IJARCSEE