Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

Abstract

SummaryIn this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection.