A Gaussian Map Honey Badger Algorithm (GMHBA) Based Energy Aware Routing and Load Balancing Method in Vehicular Fog Computing

Abstract

A cluster of vehicles enabled by the Internet of Things (IoT) not only facilitates effective collaboration through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure connectivity, but it also offers a wealth of computational resources. Vehicular (FC) fog computing (VFC), in which automobiles serve as Fog Nodes (FN) and offer cloud-like services to the IoT, makes this possible. To finish the tasks together, it is further connected with the conventional cloud. However, the dynamic nature of the Vehicular Ad-Hoc Network (VANET) makes effective load management in vehicular FC (VFC) difficult. In this research, a Gaussian Map Honey Badger Algorithm (EA-GMHBA) is used to offer an Energy-Aware (EA) approach for load balancing (LB) in fog-based VANET. Two stages are involved in its operation: Initially, it considers the mobility of fog devices when doing clustering using GMHBA. Then, the load is effectively distributed across the vehicle nodes using a capacity-based load-distribution method. A new method called dynamic clustering (DC) is presented, which uses the location, velocity, and path of the vehicles to create clusters that serve as a computer resource pool. A method to find a vehicle's leaving time from the cluster is suggested. This makes it possible to predict where the vehicle will be in the dynamic network in the future. As the network energy consumption (EC) was balanced, the VFC's network utilization improved and the network delay decreased, based on the Network Simulator 2 (NS2) background's simulated findings.