Research Area:  Vehicular Ad Hoc Networks

Abstract:

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.

Keywords:  

Author(s) Name:  Ghada H. Alsuhli, Yasmine A. Fahmy, Ahmed Khattab

Publisher name:  Wiley

DOI:  10.1049/iet-its.2019.0366

Volume Information:  Volume14, Issue10 October 2020 Pages 1190-1199

Paper Link:   https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-its.2019.0366