DRL Model for Distributed Agent-based IoT on Multi-Access Edge Computing for Accident Forecast

This paper introduces a state-of-the-art Deep Reinforcement Learning (DRL) model designed for IoT devices in Multi-Access Edge Computing (MEC), with a focus on urban accident prediction. The Edge server serves as a central hub, collecting reinforcement learning data from distributed IoT devices and distributing custom deep learning models for reinforcement learning back to these devices, all within the network. The study also addresses privacy concerns by combining Differential Privacy (DP) with Federated Learning (FL) in DRL within MEC environments. DRL combines deep learning and reinforcement learning principles, enabling wireless IoT devices to optimize actions for maximum rewards in their environments. The surge in IoT devices in networks not only generates massive data but also raises privacy concerns. Differential Privacy, rooted in Google's FL technology, safeguards data privacy. IoT devices utilize DRL and FL to improve learning efficiency and uphold privacy policies. The paper outlines how wireless IoT devices accumulate traceable data tables through reinforcement learning within a Grid-world framework. Within the MEC architecture, Edge servers efficiently execute deep neural networks tailored for reinforcement learning data and distribute results to other IoT devices upon request. This paper presents a tailored DRL model for MEC and a federated DRL framework for maintaining differential privacy while handling shared data generated through deep learning in Edge servers.