
The primary objective is to minimize the average system cost, considering both latency and energy consumption, by optimizing computational offloading decisions in a two-tier MEC and vehicular-fog architecture.


coremsg

optimizing-computational-offloading-in-multi-access-edge-computing-and-vehicular-fog-systems-using-a-distributed-reinforcement-learning-approach


Optimizing Computational Offloading in Multi-Access Edge Computing and Vehicular-Fog Systems using a Distributed Reinforcement Learning Approach
