Open AccessBio‐inspired metaheuristic framework for clustering optimisation in VANETs
Author(s) -
Alsuhli Ghada H.,
Fahmy Yasmine A.,
Khattab Ahmed
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.2019.0366
Subject(s) - cluster analysis , computer science , metaheuristic , context (archaeology) , network packet , vehicular ad hoc network , overhead (engineering) , intelligent transportation system , data mining , wireless ad hoc network , distributed computing , machine learning , algorithm , computer network , engineering , wireless , paleontology , telecommunications , civil engineering , biology , operating system
Vehicular ad‐hoc network (VANET) is a key enabling technology of intelligent transportation systems. VANETs are characterised by the rapidly changing topology and the unbounded network size. These characteristics present a range of challenges to different VANET applications such as routing and security. Clustering has strongly presented itself as an efficient solution to such challenges. In this study, the authors formulate the clustering algorithm as a many‐objective optimisation problem. Then, they propose a unified framework to optimise the configuration parameters arbitrary clustering algorithms. Three many‐objective metaheuristic optimisation techniques, ESPEA, MOEA/DD and NSGA‐III, are compared in context of this framework, and various commonly used quality indicators are utilised to identify the metaheuristic with the best quality of solutions. The proposed framework is then used to optimise a recent clustering algorithm. Using the optimal configuration resulting from the proposed framework significantly improves the performance of the clustering algorithm under‐test compared to the non‐optimised algorithm as well as other clustering approaches. This is demonstrated by the simulation results which showed up to 182% improvement in the cluster head lifetime and a reduction of 36% in the clustering packets overhead in the highway environment.