An ADMM-based incentive approach for cooperative data analysis in edgecomputing

Authors

WEIWEI FANG
XUE WANG
QINGLI WANG
YI DING

DOI

10.3906/elk-2011-151

Abstract

Edge computing is a new paradigm that provides data processing capabilities at the network edge. In view of the uneven data distribution and the constrained onboard resource, an edge device often needs to call for a number of neighboring devices as followers to cooperate on data analysis tasks. However, these followers may be rational and selfish, having their private optimization objectives such as energy efficiency. Therefore, the leader device needs to incentivize the followers to achieve a certain global objective, e.g., maximizing task accomplishment, rather than their own objectives. In this paper, we model the aforementioned challenges in edge computing as a Stackelberg game, and integrate this Stackelberg game with alternating direction method of multipliers (ADMM) to solve the conflict between the global and individual objectives. Through rigorous analysis and extensive experiments, we verify that the proposed approach can quickly converge to the optimum regardless of the number of followers and is very robust to parameter variations.

Keywords

Edge computing, ADMM, game theory, computation offloading

First Page

2374

Last Page

2384

Recommended Citation

FANG, WEIWEI; WANG, XUE; WANG, QINGLI; and DING, YI (2021) "An ADMM-based incentive approach for cooperative data analysis in edgecomputing," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29: No. 5, Article 8. https://doi.org/10.3906/elk-2011-151
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss5/8