Combination of HF set and MCDM for stable clustering in VANETs

Abstract

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.