A secure IoT and edge computing based EV selection model in V2G systems using ant colony optimization algorithm

Abstract

Purpose

The fluctuations that occurred between the power requirements have shown a higher range of voltage regulations and frequency. The fluctuations are caused because of substantial changes in the energy dissipation. The operational efficiency has been reduced when the power grid is enabled with the help of electric vehicles (EVs) that were created by the power resources. The model showed an active load matching for regulating the power and there occurred a harmonic motion in energy. The main purpose of the proposed research is to handle the energy sources for stabilization which has increased the reliability and improved the power efficiency. This study or paper aims to elaborate the security and privacy challenges present in the vehicle 2 grid (V2G) network and their impact with grid resilience.

Design/methodology/approach

The smart framework is proposed which works based on Internet of Things and edge computations that managed to perform an effective V2G operation. Thus, an optimum model for scheduling the charge is designed on each EV to maximize the number of users and selecting the best EV using the proposed ant colony optimization (ACO). At the first, the constructive phase of ACO where the ants in the colony generate the feasible solutions. The constructive phase with local search generates an ACO algorithm that uses the heterogeneous colony of ants and finds effectively the best-known solutions widely to overcome the problem.

Findings

The results obtained by the existing in-circuit serial programming-plug-in electric vehicles model in terms of power usage ranged from 0.94 to 0.96 kWh which was lower when compared to the proposed ACO that showed power usage of 0.995 to 0.939 kWh, respectively, with time. The results showed that the energy aware routed with ACO provided feasible routing solutions for the source node that provided the sensor network at its lifetime and security at the time of authentication.

Originality/value

The proposed ACO is aware of energy routing protocol that has been analyzed and compared with the energy utilization with respect to the sensor area network which uses power resources effectively.

Citation

Anjinappa, G. and Bangalore Prabhakar, D. (2022), "A secure IoT and edge computing based EV selection model in V2G systems using ant colony optimization algorithm", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-06-2022-0245