Student, Dept of ECE, IFET College of Engineering, Villupuram 2. Associate Professor, Dept of ECE, IFET College of Engineering, Villupuram

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1 Enhancing the lifetime of WSN using distributed algorithm and balancing the cluster size. S.Divyadharshini. 1 and R.Murugan 2 1 Student, Dept of ECE, IFET College of Engineering, Villupuram 2 Associate Professor, Dept of ECE, IFET College of Engineering, Villupuram ABSTRACT: Balancing the cluster is the major problem in networks. This leads to the degradation of efficiency of the network. In order to avoid this problem clustering algorithms are used. These clustering algorithms makes the load balanced. In this paper, distributed clustering approach is used. In addition to this RLNC (Random Linear Network Coding) is used in order to select the cluster head. This is done in order to reduce the node death rate. Simulation results show the enhanced lifetime of the network and balanced cluster size. Index terms: Balanced clusters, Distributed algorithm, RLNC technique, Energy efficient, network lifetime.. I. INTRODUCTION WSN (Wireless Sensor Networks) are used in places where human interventions are not possible. By using these types of sensor nodes the applications can be done easily. The main disadvantage in this is the battery life of the nodes.it is impossible to change the batteries of these nodes frequently. Hence nodes with higher lifetime is needed which is achieved by using nodes with high energy. These nodes can be identified using RLNC method. Clustering algorithm plays a major role in balancing the cluster size. These algorithms makes network load balanced. There are three key factors in clustering algorithm. They are reduced communication distance, time division multiple access (TDMA) schedule, and data aggregation/fusion.these are used

2 inorder to reduce the energy consumption of nodes. When energy is consumed less the lifetime of the node increases. The network consist of both small and large size clusters,and these clusters makes the network load unbalanced. In this paper the Distributed clustering approach is used in order to make the network load balanced. By using this algorithm the network lifetime is increased and the node death rate is decreased when compared with traditional clustering algorithms. This paper is organized as follows. Section II describes the proposed method and Section III compares the cluster formation and performance of proposed method with existing clustering algorithm. Finally, Section IV concludes the work presented in this paper. EXISTING SYSTEM: A. Problem statement: The important aspect of the clustering algorithms is the cluster size. In any network the cluster size is important because this might cause the network load unbalanced. There is a common assumption that each and every cluster has same number of nodes but distributed and random nature of algorithm does not provide equal cluster size. The unequal cluster size gradually decreases the efficiency of the network. In most of the clustering algorithms the transmission phase is divided into frames and these frames are divided into slots. One time slot is assigned for each node in the frame. At the end of each frame the cluster head sends the aggregated data to the required base station. Frame length can be calculated by, Frame time = (n Ttrans) + TDA The time slot for a node is calculated as, Time slots per node =Tround/Frame time =Tround/(n Ttrans) + TDA Where n- No.of.Nodes,Ttrans-Time required to transmit the data,tda-time required to aggregate the data, Tround-Time of data transmission phase. Each network

3 has both small and large clusters.the nodes in the small cluster consumes more energy than large clusters. This is because nodes in the small cluster sends data more times to the base stations. In this system the clusters are balanced by using the distributed clustering algorithm. Figure 1: Operation of existing system. This is one of the energy efficient algorithm which increases the lifetime of the network by considering the size of the cluster. The cluster head for each cluster is being selected randomly.and these cluster heads are used to communicate with the base stations.this clusters are said to be balanced by having equal cluster sizes. A network has both small and large clusters. The large cluster are efficient than small ones. So large clusters makes the network balanced. II. PROPOSED SYSTEM. In this paper in order to provide the balanced clusters distributed clustering approach is used. The cluster heads broadcasts messages in the network.those information is being received by all other cluster heads.each and every cluster head has details about the total number of CHs present.these cluster heads are selected by using the RLNC (Random Linear network Coding).This is used in order to provide cluster head with high energy so that it avoids the node death. Here the cluster formation is done based on two phases. They include Initial cluster formation and the Rescue phase. The rescue phase is done to again join the remaining nodes which are left over after the initial cluster formation to join the existing cluster. Rescue phase algorithm: Input: No.of.cluster heads, Distance of node i to CHs,No.of.Nodes in each cluster. Output: cluster head selected. 1: Arrange DIST[] of node i in increasing order, CLUSTER[] with corresponds of DIST 2: JOIN=1 3: for j:=2 to X do 4: if DIST[j] Thdistance then 5: if CLUSTER[j] < Thcluster then 6: JOIN=j 7: end if 8: end if 9: end for 10: node i selects cluster head JOIN

4 \ International Journal of Advanced Research in Computer Science and Thus the cluster head is selected by using the RLNC method. This decreases the node death rate and increases the efficiency of the network. RESULT AND DISCUSSION: A network with 30 nodes is considered and has been simulated in NS2.All the nodes in the network are fixed and these nodes does not know about their location. All the nodes used to form the cluster are the homogeneous nodes and their initial energy is considered as 2.0 joules. A sample NS2 code to assigning the nodes in the network in order toform the cluster.when tne program is being processed then the obtained output is,

5 node death rate and also due to this the network lifetime is increased.. III. CONCLUSION The simulation is processed in the ubuntu operating system.this is because NS2 supports only the linux. This proposed system is used in large real world applications. Some of the examples include the monitoring of pollution content in air, soil,etc.this is used because it reduces the Clustering approach proposed in this paper has a limit set on various group individuals and separation edge for unclustered hubs. Proposed technique has adjusted groups furthermore, better group quality. Reproduction comes about demonstrate that the proposed arrangement outflanks existing bunching calculation and has broadened arrange lifetime and low hub demise rate. than the ideal number of CHs per round. The estimation of The stays in place all through the proposed calculation, while Th separate shifts in understanding to the variety in the number of CHs chose in each round, thusly retaining the effect of shifting nature of CHs and giving better adjusted size bunches. bunch The proposed arrangement can be acknowledged in an expansive number of certifiable applications where vitality efficient operation of remote sensor systems is extremely vital. For instance, in the area of

6 ecological observing and reconnaissance it can be utilized for air contamination and water quality observing, woodland fire discovery, and catastrophic event anticipation applications. REFERENCE [1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: A survey, Comput. Netw., vol. 38, no. 4, pp , [2] G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad Hoc Netw.,vol.7, no. 3, pp , May [3] A. A. Abbasi and M. Younis, A survey on clustering algorithms for wireless sensor networks, Comput. Commun., vol. 30, no , pp , [4] H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks. Hoboken, NJ, USA: Wiley, [5] R. Rajagopalan and P. K. Varshney, Data-aggregation techniques in sensor networks: A survey, IEEE Commun. Surveys Tuts., vol. 8, no. 1 4, pp , [6] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, An applicationspecific protocol architecture for wireless microsensor networks, IEEE Trans. Wireless Commun., vol. 1, no. 4, pp , Oct [7] N. Amini, A. Vahdatpour, W. Xu, M. Gerla, and M. Sarrafzadeh, Cluster size optimization in sensor networks with decentralized cluster-based protocols, Comput. Commun., vol. 35, no. 2, pp , [8] A. Wang, D. Yang, and D. Sun, A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks, Comput. Electr. Eng., vol. 38, no. 3, pp , May [9] X. Wu, Y. Xiong, W. Huang, H. Shen, and M. Li, An efficient compressive data gathering routing scheme for largescale wireless sensor networks, Comput. Electr. Eng., vol. 39, no. 6, pp , Aug

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