Open AccessAn SDN empowered location aware routing for energy efficient next generation vehicular networks
Author(s) -
Renuka Kumari,
Roy Diptendu Sinha,
Reddy K. Hemant Kumar
Publication year - 2021
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/itr2.12026
Subject(s) - computer science , vehicular ad hoc network , energy consumption , cloud computing , computer network , distributed computing , efficient energy use , integer programming , wireless ad hoc network , engineering , telecommunications , algorithm , electrical engineering , wireless , operating system
With the ever expanding and all pervasive growth of information and communication technologies, vehicular ad‐hoc networks (VANETs) have been found wanting for sophistication. The fifth generation (5G) communication has brought about uncharted bandwidth capabilities SDN have enabled real time network control while cloud and fog computing have brought unprecedented computation and storage capabilities for leveraging analytics on massive data volumes and bringing down response times. These information and communication technologies can effectively handle the challenges of next generation autonomous vehicular networks including maintaining road disciplines and safety in VANETs. Moreover, energy efficient operations is the key for any upcoming technology. To this end, this paper assumes the use of 5G and fog computing based vehicular network and using SDN controller's cognizance of global vehicular topology, it proposes an SDN enabled location‐aware routing that intelligently manages workload at fog nodes for reduced energy consumption while satisfying bandwidth and delay constraints. The VANET energy minimization has been formulated as an integer linear programming problem and simulations has been carried out to test the efficacy of the proposed model and the results shows the efficacy of the proposed model 15.74% of improvement of energy consumption as compared that of the optimal algorithm.