A task offloading algorithm for internet of medical things based on reinforcement learning
Jinyue Zhang, Chao Li · 2024
Addressing the limitations of computational and storage resources in the current Internet of Medical Things (IoMT), this study establishes a model of an IoMT heart disease classification system leveraging UAVs equipped with edge computing. A novel algorithm based on deep reinforcement learning is proposed to optimize system energy consumption and reduce delay as the primary objectives within a multi-constrained optimization framework. This optimization problem is formulated as a Markov Decision Process (MDP). Subsequently, a rational reward function is designed in accordance with the optimization objectives to learn the optimal task offloading strategies by maximizing long-term rewards. The model uses a dataset for classifying heart disease to train the classification model. Simulation experiments demonstrate that the proposed algorithm outperforms DDPG and SAC algorithms in terms of performance while effectively reducing system energy consumption and delay.