An Energy-Efficient Clustering Algorithm for Large-Scale Wireless Sensor Networks

Abstract

Clustering allows hierarchical structures to be built on the nodes and enables more efficient use of scarce resources, such as frequency spectrum, bandwidth, and energy in wireless sensor networks (WSNs). This paper proposes an energy efficient clustering algorithm for self-organizing and self-managing high-density large-scale WSNs, called SNOW cluster. It introduces region node selection as well as cluster head election based on the residual battery capacity of nodes to reduce the costs of managing sensor nodes and of the communication among them. Each sensor node autonomously selects cluster heads based on a probability that depends on its residual energy level. The role of cluster heads or region nodes is rotated among nodes to achieve load balancing and extend the lifetime of every individual sensor node. To do this, SNOW cluster clusters periodically to select cluster heads that are richer in residual energy level, compared to the other nodes, according to clustering policies from administrators. To prove the performance improvement of SNOW cluster, the ns-2 simulator was used. The results show that it can reduce the energy and bandwidth consumption for clustering and managing WSNs.

This research was funded by Dual Use Technology Program and ADD Korea and has been conducted by the Research Grant of Kwangwoon University in 2007.