Abstract
Due to the constrained battery ability and computing functionality of cellularcustomers, the Resource allocation approach in D2D-assisted edge computingsystem with hybrid electricityharvesting is investigated on thisdocument.By using magnetic induction-primarily based on wireless reverse charging technology, cellular consumer can complement more electricity from nearby users whilst the electricity gathered from the surrounding radio frequency is set to be exhausted. Due to the constrained computing resource of MEC server the MEC server reaches the limit of its computing capability the adjacent Base station’s user can act as a relay node and by setting up the D2D relay links the computing responsibilities of the users which are left under previous base station can now be transferred to new base station’s MEC server which has enough resources. The goal of the resource allocation approach is to improvise the energy efficiency under computation delayconstraint and energy harvestingconstraints.An optimal answer is produced by adopting the Quantum behaved Particle Swarm Optimization (QPSO) algorithm, weighted genetic algorithm (WGA/GA) and Ant-colony optimization (ACO) algorithm, Simulation outcomes display that overall performance of theapproach that is proposed is superior than different benchmark strategies, and weighted genetic algorithm (WGA), Ant-colony optimization (ACO) algorithmcan attain better energy efficiency than the quantum behaved particle swarm optimization and standard particle swarm optimization algorithm also Simulation studiesshows that the self-learning nature of these methods(i.e WGA and ACO) gives better results even for higher complexity problems.
Resource Allocation Strategy for D2D-Assisted Edge Computing System Using Weighted Genetic and Ant Colony Optimization Algorithms