Open AccessCombination of HF set and MCDM for stable clustering in VANETs
Author(s) -
Chettibi Saloua
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0283
Subject(s) - cluster analysis , multiple criteria decision analysis , ranking (information retrieval) , topsis , computer science , vehicular ad hoc network , scalability , data mining , ideal solution , network topology , reliability (semiconductor) , similarity (geometry) , wireless ad hoc network , mathematical optimization , machine learning , operations research , engineering , artificial intelligence , mathematics , computer network , telecommunications , power (physics) , physics , image (mathematics) , database , quantum mechanics , wireless , thermodynamics
Fast‐moving nodes in vehicular ad hoc network (VANET) make network topology very dynamic, which deteriorates communication reliability and scalability. To overcome this problem, a hierarchical topology can be created using a clustering algorithm. This study presents a novel hesitant fuzzy (HF) multi‐criteria ranking framework to deal with cluster‐heads (CHs) election problem in VANET. An analogy is suggested between ‘ranking of HF elements’ and ‘CH selection in VANET’. Within the proposed framework, a multiple criteria decision‐making (MCDM) method should be used to calculate vehicle eligibility to become CH. In this study, technique for order of preference by similarity to ideal solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and EVAluation of MIXed data (EVAMIX) MCDM methods are applied. A simulation study, under a highway scenario, is conducted to investigate the performance of HF‐TOPSIS, HF‐VIKOR and HF‐EVAMIX‐based clustering algorithms. Obtained results show that CH election based on HF‐EVAMIX leads to more stable clustering in comparison with HF‐TOPSIS, HF‐VIKOR and the conventional threshold‐based method.