A Non-Cooperative Game-based and Multi-Hop data transmission optimization strategy for industrial wireless networks in flexible manufacturing environments

With the rapid development of the Industrial Internet of Things (IIoT), flexible manufacturing systems impose stringent requirements on network lifetime and communication reliability in industrial wireless networks. However, in complex industrial environments, the inherent energy constraints of sensor nodes and the highly dynamic nature of network topology significantly hinder the prolongation of network lifetime and the improvement of Quality of Service. To address these issues, this paper proposes a Non-Cooperative Game-based and Multi-Hop (NCG-MH) data transmission optimization strategy. Specifically, the cluster head (CH) selection process is formulated as a non-cooperative game, where nodes determine their probability of becoming CHs via utility maximization. A CH retention mechanism is introduced to reduce unnecessary role switching during the effective service period. In addition, an AGV-assisted Directional Greedy Forwarding (A-DGF) strategy is designed for multi-hop data transmission, enabling efficient path selection while effectively avoiding the high exploration cost inherent in reinforcement learning-based approaches. The simulation results verify that the proposed strategy improves network lifetime, energy efficiency, and communication performance compared with existing schemes.