Channel Scheduling Method For Industrial Internet Of Things Oriented To Information Freshness Optimization

In industrial production,the central controller needs to monitor the equipment’s operational status in real-time,collect production data,and make real-time control decisions to adjust the production process,avoid production interruptions,and other issues.Traditional performance metrics,such as delay and throughput,are no longer sufficient to reflect data’s real-time nature.Therefore,researchers have introduced the concept of information age into the industrial Internet of Things to describe the time difference between data generation and reception by the target node,focusing more on measuring data’s timeliness from the central controller’s perspective.As a large number of sensor nodes are deployed in industrial Io T,adopting a centralized scheduling strategy would result in a significant amount of signaling overhead.Additionally,sensor nodes cannot obtain instantaneous channel status information in a timely manner,and due to the independent distribution of each node,competition for channel resources is inevitable.Therefore,it is urgent to explore how to improve the freshness of information in the network and reduce the Ao I while ensuring the fairness of all nodes under the premise that sensor nodes cannot obtain instantaneous channel status information.This thesis presents the following research content:To address the information age optimization problem caused by sensor nodes’ inability to obtain channel prior knowledge in a timely manner,a multi-channel scheduling algorithm based on reinforcement learning and matching is proposed.This algorithm allows sensor nodes to independently optimize their channel selection strategy based on local information only.First,the optimization problem is modeled as a multi-armed bandit problem,and sensor nodes jointly consider exploration and exploitation strategies,establishing a preference list for channels.From the perspective of multi-user reinforcement learning,the algorithm proposed in this section does not require information exchange between nodes.When a channel conflict occurs,the central controller selects nodes based on their freshness of information.Simulation results show that the proposed algorithm can effectively improve the freshness of information in the network.To address the fairness issue between sensor nodes caused by competition for wireless channels,a distributed multi-channel fair matching mechanism optimized for information age is proposed.First,the optimization problem is modeled as a multiarmed bandit model,and a distributed channel matching mechanism is established to enable sensor nodes to continue exploring and learning channel status.Secondly,to address the problem of sensor nodes competing for channel resources to pursue maximum gain in the distributed algorithm,the mechanism constrains the average transmission success probability of each node to ensure the transmission success probability of the matching between nodes and channels.The thesis proposes fair matching algorithms with known probability constraints and unknown probability constraints to establish matching pairs that meet the fairness requirements of nodes.Finally,simulation results show that the proposed method can effectively reduce the average Ao I in the network while ensuring the fairness of all nodes when channel status information is unknown.The thesis has 20 figures,9 tables and 78 references.