InEdge: Interest-Driven Service Incentive Mechanism Based on Stackelberg Game in Edge-Empowered IIoT

Bohai Zhao, Zhiying Tu, Kai Peng, Yongchao Xing, Kai Zhang, Hongliang Sun, Dianhui Chu

Abstract: The integration of Mobile Edge Computing (MEC) into the Industrial Internet of Things (IIoT) has markedly improved resource accessibility and propelled digital-intelligent advancements. Nevertheless, the substantial costs of MEC infrastructure also pose critical challenges to incentive mechanism design. Specifically, there is still an absence of a standardized and widely recognized incentive framework for the dynamic and non-cooperative interactions between edge service requesters and providers. Furthermore, the highly complicated characteristics of IIoT necessitate a greater reliance on dependable and trustworthy edge resource provision than other paradigms, which implies that human-centric factors (e.g., credit) are equally crucial as profit-driven metrics (e.g., price) in incentive design. To tackle these challenges, we propose In3Edge, a Stackelberg game-based incentive mechanism that systematically considers the interplay between profit-driven and interest-oriented indicators while accommodating heterogeneous peers, subjective interest divergences, and objective resource disparities. Particularly, leveraging convex optimization theory, we provide rigorous proofs and in-depth analyses of the intrinsic properties of In3Edge, encompassing the concavity/convexity of utility functions, equilibrium solution boundaries, optimal responses under peer/interest heterogeneity, and closed-form solutions for symmetric multipeer scenarios while articulating a series of propositions and theorems to underpin future research. Finally, extensive experiments are constructed under diverse dynamically changing scenarios with distinct characteristics, confirming the strong motivational capabilities of In3Edge in MEC-empowered IIoT.

External IDs:dblp:conf/icws/ZhaoTPXZSC25