Design and Implementation of the Deep Reinforcement Energy Efficient Routing for the Fog-BAN-Cloud of Things using Smart Health care applications

Abstract The integration of Fog Computing, Body Area Networks (BANs), and the Cloud of Things (CoT) has revolutionized smart healthcare applications, enabling real-time data processing, seamless connectivity, and efficient resource management. However, the growing demands for energy-efficient operations and reliable data transmission in these systems present significant challenges. This study proposes the development of a Deep Reinforcement Learning (DRL)-based energy-efficient routing algorithm tailored for Fog-BAN-Cloud architectures in healthcare applications. The proposed solution leverages DRL models to dynamically optimize routing paths and scheduling policies, minimizing energy consumption while maintaining high Quality of Service (QoS). The routing algorithm prioritizes low-energy paths in BAN and Fog networks. The paper specifically employs Proximal Policy Optimization (PPO), a reinforcement learning technique, to optimize the routing decisions by considering factors including energy consumption, network congestion, and data traffic conditions. PPO is used to dynamically adjust the policy updates, ensuring stability while reducing power usage and improving data transmission efficiency. Extensive simulations highlights the performance of the proposed model, demonstrating potential improvements in energy efficiency, reduced latency, and enhanced data reliability compared to traditional methods. This work highlights the potential of intelligent algorithms to address the unique challenges of healthcare-driven IoT ecosystems, providing a scalable and sustainable solution for energy-efficient routing in Fog-BAN-Cloud environments. The proposed approach is a promising strategy for optimizing IoT-driven smart healthcare systems.