Open AccessAdaptive fuzzy‐based message dissemination and micro‐artificial bee colony algorithm optimised routing scheme for vehicular ad hoc network
Author(s) -
Patil Saurabh Dadasaheb,
Ragha Lata
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0388
Subject(s) - computer science , fuzzy logic , vehicular ad hoc network , scheme (mathematics) , wireless ad hoc network , computer network , routing (electronic design automation) , process (computing) , operations research , artificial intelligence , wireless , telecommunications , engineering , mathematical analysis , mathematics , operating system
Nowadays, researches focus on providing services for the emergency vehicle and also providing potential applications. Vehicular ad hoc networks (VANETs) are considered as an emerging research area. In heavily populated cities, the emergency vehicles not being able to reach their destination (hospitals) on time due to the slow‐moving traffic. Hence, it results in loss of lives many times. Therefore, the important concern in this area is reaching the destination in an emergency situation. In this study, an adaptive fuzzy‐based message dissemination and micro artificial bee colony algorithm (MABC) optimised routing scheme for VANET is proposed. An adaptive fuzzy‐based multi‐criteria decision‐making approach is adopted for the cluster head selection. This method combines the fuzzy decision‐making trial and evaluation laboratory model‐based analytical network process (ANP) termed as fuzzy DANP (i.e. Decision Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Processes (ANP)) and fuzzy Višekriterijumska Optimizacija I kompromisno Rešenje methods for optimal decision making. For emergency broadcast, a vehicular emergency dissemination protocol is used in vehicle‐to‐vehicle communication. Finally, an MABC is used to select the best minimum route from start to the endpoint. The proposed method is simulated in MATLAB, and the results show outstanding improvements compared to other existing approaches.