Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency

Abstrak

The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading.