Optimized fuzzy clustering using moth-flame optimization algorithm in wireless sensor networks

Energy consumption is one of the main concerns in wireless sensor networks (WSNs). In this context, congestion is one of the problems which by dropping the data packets, increases the energy consumption of WSN, and reduces its lifetime. In this paper, we deal with these problems and present a distributed fuzzy clustering scheme that uses two Fuzzy Logic Controllers (FLCs) to organize WSN into some clusters. Besides, in this scheme, we consider multiple mobile sink nodes and provide another FLC for fuzzy sink selection used by cluster heads (CHs). In this scheme, CHs cooperate in multi-hop routing of data packets to minimize the energy consumption of WSN. However, in the data routing step, congestion may happen in the data forwarding nodes. In this scheme, we deal with the congestion problem by proposing a distance-based version of the Random Early Detection (RED) congestion control method to drop the data packets more intelligently. Besides, to increase the effectiveness of the proposed FLCs, we tune them using the Moth-Flame Optimization algorithm and minimize their rules. Simulation results indicate the effectiveness of the proposed clustering and distance-based RED congestion control method in improving the WSN’s lifespan, reducing the number of retransmissions, and mitigating the percentage of packet loss.