Robust energy harvesting aware clustering with fuzzy petri net reasoning algorithm
Aya Mostafa, Khaled Hassan · 2014
Wireless Sensor Networks (WSNs) propose solutions to problems like health, environmental monitoring, and natural disaster detection. Still, such solution is obstructed by the limited battery lifetime. In applications where changing or recharging the battery is nearly impossible due to the deserted environment of such applications. Energy harvesting offers a potential solution to this problem. This paper introduces an energy harvesting aware clustering algorithm that considers the harvested energy, number of neighbors and the centrality during clustering of WSN. The algorithm presented uses a knowledge-based inference approach for selecting cluster heads using fuzzy petri nets. Moreover, the algorithm builds a three-level hierarchy offering a network with better energy consumption. This paper addresses also the robustness of the network by introducing primary and secondary backups for each level in the hierarchy. Such approach reduces unnecessary computations in case a Cluster Head (CH) is dead. The paper evaluated the performance of this algorithm against the famous HEED algorithm. It was observed that the results of the proposed algorithm outperforms the HEED keeping most of the WSN alive for much longer time.