A Clustering Algorithm Based on Topological Anomalies Applied to Wireless Body Area Networks

Abstract Wireless Body Area Networks (WBANs) are formed by autonomous devices that collect biometric data from the human body. Such devices (sensors) commonly use low transmitting power. Thus, even small postural variations can cause disconnections. Based on this problem, we propose an algorithm for cluster topology generation, based on anomalous topologies. The proposed algorithm determines metrics for the quality and stability of the clusters, based on a recent history of previous clusters. In order to perform a more realistic evaluation of the proposed algorithm, we consider a real-life typical movement pattern. The Omnet++ was considered, and simulation results showed that the proposed algorithm outperformed the LEACH protocol concerning both: i) energy conservation, and ii) packet delivery ratio in a WBAN with mobility.