Bio-inspired low-complexity clustering in large-scale dense wireless sensor networks

Abstract

To enhance network scalability and increase network lifetime in large-scale wireless sensor networks (WSNs), clustering has been recognized as an effective solution for hierarchical routing, topology control and data aggregation. Inspired by the collective behavior of flocks and schools, we propose a Bio-inspired self-organizing Low-Complexity Clustering (B-LCC) algorithm for large-scale dense WSNs. The B-LCC algorithm does not require sensor locations, time synchronization nor any priori knowledge of the network. It is completely distributed and can achieve a well-distributed cluster heads. The processing time complexity of the B-LCC algorithm is O(1) per cluster, which outperforms most of the existing clustering algorithms as they have processing time complexity of O(n) per node in the worst case. Additionally, the B-LCC algorithm has a stable performance in topology control and the formed topology is robust to node failure.