A task offloading algorithm for internet of medical things based on reinforcement learning

Jinyue Zhang; Chao Li

doi:10.1117/12.3036845

13 July 2024 A task offloading algorithm for internet of medical things based on reinforcement learning

Jinyue Zhang, Chao Li

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 1320824 (2024) https://doi.org/10.1117/12.3036845
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinyue Zhang and Chao Li "A task offloading algorithm for internet of medical things based on reinforcement learning", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 1320824 (13 July 2024); https://doi.org/10.1117/12.3036845

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available