In the Industrial Internet of Things (IIoT), energy efficiency is critical for effective management of physical systems. To achieve stable control of IIoT with minimal energy consumption, it is essential to co-design the controller and the wireless network. In this paper, we present a novel reinforcement learning (RL) approach called the Learning-enabled Self-triggered Wireless Networked-Control System (LS-WNCS). LS-WNCS learns complex interdependence between control and network systems, generating near-optimal control commands and sampling periods simultaneously to minimize energy consumption and maximize control performance. Compared with conventional RL algorithms, LS-WNCS reduces network energy consumption by up to 66% while maintaining a high level of control performance.

As various wireless devices share the same frequencies in the unlicensed 2.4-GHz industrial scientific medical band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the guide busy tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multinode case as well.

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