A Power Aware Three Tier Hierarchical Clustering Algorithm for WSN

Abstract: Wireless Sensor Network (WSN) is the network of tiny sized sensor nodes. Due to the limited size, sensor node is limited in processing, energy and memory. The restriction of energy and memory in sensor node create challenges for routing in WSN. Recently clustering increases interest for energy efficiency in WSNs. A sensor node represents all other sensor nodes in the cluster called cluster head (CH) and collects sensed data from them. The load on cluster head is larger than normal sensor node there for two type of sensor node, first is heterogeneous (more energy than normal sensor node) sensor node and second is normal sensor node are considered. To balance the energy consumption among sensor nodes in the network, the CH should be rotated among all sensor nodes. The computation and communication process is the major source of energy consumption. In this paper proposes, Power Aware Three Tier Hierarchical Clustering Algorithm (PA-THC). PA-THC elects CH based on residual power (energy) of the sensor nodes. The network arrange in three tire architecture: at the lower level normal sensor nodes are connected, at the mi ddle level cluster head are reside to receive data from normal sensor nodes and at the higher level superior nodes are work as the interface between cluster heads and base station. In WSNs data reporting to base station over long distance consumed large amount of energy that reduce lifetime of the network. In this paper, energy heterogeneity is considered in the cluster head selection in order to increase the network stability and lifespan. Moreover, the re-clustering of cluster head conserves the energy in data reporting to base station. The simulation results show that PA-THC conserves more energy in data reporting compared to the well-known exiting clustering algorithms SEP and TL-LEACH. PA-THC also prolongs the network stability period up to 2020 rounds in medium area (compare to TL-LEACH) and 300 rounds for large area (compare to SEP).

Keywords: Wireless sensor network, clustering, energy conservation.