Multi-user multi-objective computation offloading for medical image diagnosis

Qi Liu; Zhao Tian; Guohua Zhao; Yong Cui; Yusong Lin

doi:10.7717/peerj-cs.1239

Multi-user multi-objective computation offloading for medical image diagnosis

PeerJ Comput Sci. 2023 Mar 8:9:e1239. doi: 10.7717/peerj-cs.1239. eCollection 2023.

Authors

Qi Liu^{1

2}, Zhao Tian³, Guohua Zhao⁴, Yong Cui⁵, Yusong Lin^{2

3

6}

Affiliations

¹ School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.
² Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China.
³ School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China.
⁴ Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁵ School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
⁶ Hanwei IoT Institute, Zhengzhou University, Zhengzhou, China.

Abstract

Computation offloading has effectively solved the problem of terminal devices computing resources limitation in hospitals by shifting the medical image diagnosis task to the edge servers for execution. Appropriate offloading strategies for diagnostic tasks are essential. However, the risk awareness of each user and the multiple expenses associated with processing tasks have been ignored in prior works. In this article, a multi-user multi-objective computation offloading for medical image diagnosis is proposed. First, the prospect theoretic utility function of each user is designed considering the delay, energy consumption, payment, and risk awareness. Second, the computation offloading problem including the above factors is defined as a distributed optimization problem, which with the goal of maximizing the utility of each user. The distributed optimization problem is then transformed into a non-cooperative game among the users. The exact potential game proves that the non-cooperative game has Nash equilibrium points. A low-complexity computation offloading algorithm based on best response dynamics finally is proposed. Detailed numerical experiments demonstrate the impact of different parameters and convergence in the algorithm on the utility function. The result shows that, compare with four benchmarks and four heuristic algorithms, the proposed algorithm in this article ensures a faster convergence speed and achieves only a 1.14% decrease in the utility value as the number of users increases.

Keywords: Computation offloading; Distributed optimization; Exact potential game; Multi-objective; Prospect theory; Risk awareness.

Grants and funding

This work was supported by the National Natural Science Foundation of China under Grant No. 81772009, the Collaborative Innovation Major Project of Zhengzhou under Grant No. 20XTZX06013, No. 20XTZX05015, and the Key Technologies R&D Program of Henan Province No. 212102210409, No. 212102310039. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.