A DISTRIBUTED ENERGY AWARE CLUSTERING APPROACH FOR LARGE SCALE WIRELESS SENSOR NETWORK

Wireless sensor networks consist of many micro sensor nodes that are dispersed in a limited geographical area. The nodes are wireless interconnected. Each node works independently and without human intervention and typically, it is physically very small with limitations in processing power, memory capacity, power supply and etc. The nodes in these networks carry limited and mainly irreplaceable energy resources. Given that a node acts also as a router, the node dysfunction eliminates them from the network topology and hence the network reorganization and rerouting the transmitting packet occurs. It will increase energy consumption and may also cause a part of the environment to get out of the supervision and control of the network. Since battery life virtually specifies the node life cycle, these network needs to work under energyefficient protocols and structures. In this paper, a Energy Aware Clustering Approach (EACA ) is presented for wireless sensor networks. To reduce overhead in this study, a multilevel and distributed clustering algorithm is proposed, that converts a flat network into a hierarchical multilevel structure and provides an appropriate infrastructure to rout and gather the data correctly. Also, In this protocol the best Cluster Head is selected periodically by considering a series of criteria, including the residual energy, lower communication costs and the minimum distance between the cluster head and the cluster members which consequently offers an energy efficient clustering protocol that increases the network lifetime. Our new approach uses the least amount of energy in the clustering process and will quickly terminate the clustering process. In addition, there is no assumption about the density, capabilities or synchronization of the node. The simulation results demonstrate that the clustering algorithm can effectively reduce the energy consumption and increase the system lifetime compared to the LEACH protocol, which is one of the most efficient clustering protocols.