Premium
An evolving α ‐dependent mobility model for a fleet of unmanned aerial vehicles in wireless sensor networks
Author(s) -
Ghosh Ramkrishna,
Mohanty Suneeta,
Pattnaik Prasant Kumar
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4926
Subject(s) - computer science , weighting , base station , metric (unit) , fuzzy logic , wireless sensor network , mobility model , software deployment , real time computing , wireless , computer network , artificial intelligence , telecommunications , medicine , operations management , economics , radiology , operating system
Summary In 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).