Joint Optimization of Energy-Efficiency and Delay for IIoT with Satellite-Terrestrial Integrated CPN

Author First name, Last name, Institution

Meng Li, Beijing University of Technology
Meihui Li, Beijing University of Technology
F. Richard Yu, Carleton University
Ruizhe Yang, Beijing University of Technology
Enchang Sun, Beijing University of Technology
Zhuwei Wang, Beijing University of Technology
Anwer Al-Dulaimi, Zayed University

Document Type

Conference Proceeding

Source of Publication

IEEE International Conference on Communications

Publication Date

9-26-2025

Abstract

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex environments of communication, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a dueling double deep Q network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms comparison schemes significantly.

DOI Link

10.1109/icc52391.2025.11161363

ISBN

[9798331505219]

ISSN

1550-3607

Publisher

IEEE

First Page

5169

Last Page

5174

Disciplines

Computer Sciences

Keywords

computing power network, Industrial Internet of Things, resource management, satellite-terrestrial integrated network, task offloading

Scopus ID

105018452364

Recommended Citation

Li, Meng; Li, Meihui; Yu, F. Richard; Yang, Ruizhe; Sun, Enchang; Wang, Zhuwei; and Al-Dulaimi, Anwer, "Joint Optimization of Energy-Efficiency and Delay for IIoT with Satellite-Terrestrial Integrated CPN" (2025). All Works. 7586.
https://zuscholars.zu.ac.ae/works/7586

Indexed in Scopus

yes

Open Access

no