Task Offloading and Resource Management for IIoT With Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

• Published in: IEEE internet of things journal Vol. 12; no. 12; pp. 21770 - 21783
• Main Authors: Li, Meng, Li, Meihui, Wang, Kan, Yu, F. Richard, Wang, Zhuwei, Si, Pengbo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Artificial intelligence; Computation offloading; computing power network (CPN); Data processing; Delays; Energy consumption; Heuristic algorithms; Industrial applications; Industrial Internet of Things; Industrial Internet of Things (IIoT); Machine learning; Markov processes; Optimization; Resource management; satellite-terrestrial integrated network (STIN); Satellites; Space-air-ground integrated networks; task offloading
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3548056

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 communication environments; and 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated CPN (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 the comparison schemes significantly.