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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
