Age-of-Information Aware Scheduling for Edge-Assisted Industrial Wireless Networks

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, wher...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2021-08, Vol.17 (8), p.5562-5571
Main Authors: Li, Mingyan, Chen, Cailian, Wu, Huaqing, Guan, Xinping, Shen, Xuemin
Format: Article
Language:English
Subjects:
Age of information (AoI)
Constraints
Decision making
Delivery services
device-to-device (D2D) communication
edge computing
industrial wireless network (IWN)
Job shop scheduling
Learning
Nonstationary environments
Optimization
Performance evaluation
Policies
Processor scheduling
Quality of service
Reinforcement learning
Scheduling
Simulation
Wireless networks
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Summary:Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors' decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3037299