An adaptive competition-based clustering approach for wireless sensor networks

Abstract

Hierarchical architecture is an effective scheme to make wireless sensor networks (WSNs) scalable and energy-efficient. Clustering the sensor nodes is a well-known two-layered architecture suitable for WSNs and has been extensively explored for different purposes and applications. In this paper a novel clustering approach called the adaptive competition-based clustering approach (ACCA) is proposed for WSNs. Selecting the cluster heads in the proposed ACCA is performed based upon a hybrid of local competition and the distances among the cluster heads. First, by the new proposed competition scheme, the nodes which are with the high residual energy and closer to the centre of the density of the nodes are elected and form an initial set of cluster head candidates. Then the candidates with suitable distances to other neighbor candidates are elected as the cluster heads. The proposed algorithm is fast with a low time and message complexity. It offers a longer lifetime for the networks, and at the same time, a proper level of fault tolerance. The proposed ACCA is simple enough to be implemented in real systems. Different simulation experiments are performed on different states and the algorithm is compared with some well-known and related clustering approaches. The experiments suggest that in terms of longevity and fault-tolerance, ACCA outperforms the well-known and relevant clustering protocols LEACH, HEED, and EECABN.