Intelligent Resource Monitoring and Control Method in Vehicular Ad-hoc Networks for Electric Vehicle Enabled Microgrids
Dr. Arasu RamanFaculty of Business and Communications, INTI International University, Malaysia. arasu.raman@newinti.edu.my0000-0002-8281-3210
Nick Wong Yi TingFaculty of Business and Communications, INTI International University, Malaysia. nick.wongyt@gmail.com0009-0008-4588-5363
Dr. Rajani BalakrishnanFaculty of Business and Communications, INTI International University, Malaysia. rajani.balakrishnan@newinti.edu.my0000-0002-0467-2053
Dr. Baskar SanjeeviSchool of Engineering and Technology, Vels Institute of Science, Technology & Advanced Studies, India. baskar133.se@velsuniv.ac.in0000-0002-0810-3755
Dr. Vijayesvaran ArumugamSchool of Business and Technology, IMU University, Malaysia. vijayesvaran@imu.edu.my0000-0001-9307-1307
Keywords: Resource Monitoring, Electric Vehicle, Vehicular Ad-hoc Networks, Smart Microgrids.
Abstract
Software-Defined Networks (SDN) and Cloud Radio Access Networks (CRANs) are added to vehicle ad hoc networks (VANETs). The goals include making data transfer and resource sharing more efficient, achieving the shortest possible wait and response times, and ensuring the network is reliable even when conditions change. The usual ways of handling and grouping loads in Electric Vehicles (EVs), which are made for stable environments, need to be changed to work with VANETs, which are constantly evolving. To regularly meet these high service levels, focus on more adaptable and durable solutions. This study shows a software-defined EV fog computing design that improves VANETs' resource sharing. The suggested design uses smart controls placed strategically in the network to make the flow of data and use of resources as efficient as possible. The system uses parallel processing to split up computing tasks among EV stations. This makes the network more mobile and lessens the chance of jams. Simulations and real-world tests of the model show that it makes the network much more efficient. The study found that compared to traditional methods, the average response time went up by 29%, network delay went down by 23%, and it took 27% less time to get to the best assets spread.