Joint Deep Reinforcement Learning and Stackelberg Game With Intervention for Heterogeneous Edge Computing in Industrial Internet of Things

Edge computing, as a novel computing paradigm, is expected to significantly enhance the productivity of the Industrial Internet of Things (IIoT). However, in a practical IIoT environment consisting of multiple edge servers, the supply of computing services (edge servers) may involve different business strategies or pricing schemes, while the demand side (user terminals) can choose the service that best suits their needs based on quotes from different suppliers. The competition between the supply and demand sides for their respective interests leads to strategic games between service suppliers and users. Therefore, how to design reasonable pricing mechanisms and offloading strategies to balance service supply and resource demand has become an urgent issue. Furthermore, during these games, self-interested service providers may collude to form mutually beneficial cartels. To address the intricacies of these games, this paper integrates deep reinforcement learning (DRL) with game theory. Specifically, a heterogeneous system model is first constructed in the Stackelberg game framework. Subsequently, we leverage the Double-Dueling Deep Q-Network (D3QN) and Deep Deterministic Policy Gradient (DDPG) algorithms to formulate users’offloading decisions and service providers’ pricing strategies, respectively. Consequently, a joint D3QN-DDPG and Stackelberg algorithm is proposed to maximize the benefits for both users and servers. Moreover, to disrupt the formation of cartels among service providers, we introduce an intervention mechanism and present an intervention-based benefit maximization algorithm. Finally, simulation experiments demonstrate that the total system utility with intervention surpasses that without intervention by approximately 16%, validating the necessity of the intervention. Comparisons with other benchmark algorithms confirm that the proposed algorithm achieves superior system benefits.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-04-09
DOI
https://doi.org/10.1109/jiot.2025.3559080
Citations
2
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
4.15

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Joint Deep Reinforcement Learning and Stackelberg Game With Intervention for Heterogeneous Edge Computing in Industrial Internet of Things

Weijun Cheng, Xintao Huan, Wenjing Wei, Xiaoshi Liu
2 citations
IEEE Internet of Things Journal
Customer churn and segmentation
4.15
article

Joint Deep Reinforcement Learning and Stackelberg Game With Intervention for Heterogeneous Edge Computing in Industrial Internet of Things

Weijun Cheng, Xintao Huan, Wenjing Wei, Xiaoshi Liu
article en
2 citations

Abstract

Edge computing, as a novel computing paradigm, is expected to significantly enhance the productivity of the Industrial Internet of Things (IIoT). However, in a practical IIoT environment consisting of multiple edge servers, the supply of computing services (edge servers) may involve different business strategies or pricing schemes, while the demand side (user terminals) can choose the service that best suits their needs based on quotes from different suppliers. The competition between the supply and demand sides for their respective interests leads to strategic games between service suppliers and users. Therefore, how to design reasonable pricing mechanisms and offloading strategies to balance service supply and resource demand has become an urgent issue. Furthermore, during these games, self-interested service providers may collude to form mutually beneficial cartels. To address the intricacies of these games, this paper integrates deep reinforcement learning (DRL) with game theory. Specifically, a heterogeneous system model is first constructed in the Stackelberg game framework. Subsequently, we leverage the Double-Dueling Deep Q-Network (D3QN) and Deep Deterministic Policy Gradient (DDPG) algorithms to formulate users’offloading decisions and service providers’ pricing strategies, respectively. Consequently, a joint D3QN-DDPG and Stackelberg algorithm is proposed to maximize the benefits for both users and servers. Moreover, to disrupt the formation of cartels among service providers, we introduce an intervention mechanism and present an intervention-based benefit maximization algorithm. Finally, simulation experiments demonstrate that the total system utility with intervention surpasses that without intervention by approximately 16%, validating the necessity of the intervention. Comparisons with other benchmark algorithms confirm that the proposed algorithm achieves superior system benefits.

IEEE Internet of Things JournalVol. 12(13)
Beijing Institute of Technology (CN), Minzu University of China (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Customer churn and segmentation
4.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.