Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information

WANG Heng; YU Lei; XIE Xin

doi:10.11999/JEIT220088

Volume 45 Issue 3

Mar. 2023

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WANG Heng, YU Lei, XIE Xin. Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088

Citation:

WANG Heng, YU Lei, XIE Xin. Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088

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Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information

doi: 10.11999/JEIT220088

WANG Heng^{1
,
,},
YU Lei¹,
XIE Xin²

1.
Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61972061), The Natural Science Foundation of Chongqing, for Distinguished Young Scholars (cstc2019jcyjjqX0012), The Fundamental Research and Frontier Exploration of Chongqing (cstc2021ycjh-bgzxm0017)

Received Date: 2022-01-19
Rev Recd Date: 2022-04-21

Available Online: 2022-04-26

Publish Date: 2023-03-10

Abstract

Abstract

In Industrial Wireless Sensor Networks (IWSN), timely delivery of periodic control/sensing data flows and aperiodic event data flows is crucial to ensure production safety and efficiency. As a new metric of data freshness, Age of Information (AoI) can comprehensively measure the real-time performance of data delivery from the perspective of destination node. For industrial wireless sensor networks with hybrid periodic and aperiodic data, the data freshness metric of whole network is introduced. Considering that the freshness of periodic data exceeding the threshold may have a negative impact on industrial production, a joint optimization model is established, which minimizes the system average AoI and the probability of AoI overdue for periodic data, and then the optimization problem is formulated as a Markov Decision Process (MDP). Since the traditional optimal solution method based on relative value iteration is difficult to implement in large-scale networks result from dimensional disasters, Deep Reinforcement Learning (DRL) is used to reduce the state space dimension of the optimization problem. Moreover, the decision exploration mechanism is improved to speed up the learning speed, and a scheduling method of deep reinforcement learning based on Optimal Decision Exploration (DRL-ODE) is proposed. Simulation results show that the proposed method can improve the timeliness of network data delivery while reducing the probability of AoI overdue for periodic data effectively.
- Network scheduling,
- Industrial Wireless Sensor Networks (IWSN),
- Age of Information(AoI)

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References(25)

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