Hypergraph-Based Channel Effects on Age of Information in D2D-Enabled Social Industrial IoT Networks

Due to the increasing pervasiveness of various wireless machines in Industrial Internet of Things (IIoT) networks, the need for timely updates on information freshness has become more critical. Social IIoT (SIIoT) framework facilitates the development of interconnected networks in smart factories that can establish social relationships between humans and machines. The widespread implementation of device-to-device (D2D) communication in SIIoT networks introduces cross-talk interference, leading to an untimely update on information freshness has become a critical challenge. The Age of Information (AoI) performance metric is used to measure the Information freshness. Dynamic channel behavior plays a crucial role in enhancing the performance of D2D-enabled SIIoT Networks. This article addresses the impact of AoI and user channel behavior on network performances. We propose a mean field game theoretic optimization algorithm (PMOA) that utilizes a Hypergraph model and game theoretic concept. The problem is solved in two stages: first, a hypergraph-based channel selection model is developed to minimize interference under outdated channel state information (CSI). Second, AoI is optimized by adopting the Fokker-Planck equation (FPK). This approach meets constraints on maximum transmission power, data rate variability, and timeliness of information freshness of the devices. The PMOA algorithm is validated through simulation and achieves a significant improvement with a 13.41% increase in network throughput and a reduction in AoI by 39.38% compared to existing benchmark schemes. The implementation of the proposed algorithm may be suitable for updating the information freshness of industrial devices driven by SIIoT-based recommendation systems.