An evolving α‐dependent mobility model for a fleet of unmanned aerial vehicles in wireless sensor networks

Abstract

SummaryIn this paper, we represent an innovative α‐based mobility model for a fleet of unmanned aerial vehicles (UAVs) using Sugeno type‐2 fuzzy inference system (T2FIS) in wireless sensor networks (WSNs). Specifically, the spotlight is on the deployment of suchlike energy level, neighbor count, and hop count to base station (BS) solution to critical applications, like monitoring applications for public security, border control enforcement, and so on. A type‐2 fuzzy logic (T2FL)‐dependent α mobility controller is introduced to support on the variation of a followship weighting metric α to select that close by UAV is the relevant to be pursued. Modification the proper values for the followship weight α is somewhat a demanding function. We have selected T2FL app for variation the values of the followship metric α choosing appropriate type‐2 fuzzy signifiers essentially energy level, neighbor count, and hop count to BS. T2FIS is utilized to pick the appropriate followship weight computation. Efficacy of the suggested framework is made by way of analysis of the statistics and multiple linear regressions (MLRs).