Joint scheduling and clustering to balance power consumption for high-node-density sensor networks with directional data traffic

Abstract

Many scheduling and clustering schemes have been proposed for sensor networks to improve energy efficiency. However, most traditional scheduling and clustering schemes do not take into account directional and uneven data traffic, which makes higher burden nodes drain energy more rapidly. This paper combines clustering and scheduling technologies to improve energy efficiency for homogeneous sensor networks with high node density by balancing power consumption throughout the network. In this algorithm, because the node density is high, only the clusterhead keeps active, while others are in sleep state with negligible power consumption. By rotating the role of the clusterhead, the power consumption is balanced among the nodes. Furthermore, the clusters are partitioned in such a way that their total energy stored is proportional to their burdens in order to equalize cluster lifetime. Simulation results show that the proposed algorithm indeed improves energy efficiency.