Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Wireless sensor networks are widely used in industrial cyber-physical system installations, where high reliability and the need for real-time data are the two main characteristics. A large amount of real-time data can be transmitted to its destination on time using a reasonable periodic allocation of a node’s transmission slots. However, a flow may miss its deadline when flow conflicts occur. When such missed deadlines occur regularly, system performance may degrade, and when the flow is critical, such data losses can result in errors or cause disasters. To address this issue, we introduce multi-user multiple-input and multiple-output technology and a mixed-critical system into an industrial cyber-physical system. When an error occurs or when demand changes, the multi-user multiple-input and multiple-output nodes can switch their transmission mode, changing to a high-criticality configuration to meet the system’s new needs. Hence, we first propose a heterogeneous multi-user multiple-input and multiple-output system model. Based on this model, we propose a slot analyzing algorithm that guarantees system schedulability by reallocating slots for each node after replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By considering both system schedulability and cost, the slot analyzing algorithm also reduces the number of multi-user multiple-input and multiple-output nodes required. Then, to further reduce the number of multi-user multiple-input and multiple-output nodes in an industrial cyber-physical system, we propose a priority inversion algorithm that improves schedulability by adjusting slot allocations before replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By reducing the use of multi-user multiple-input and multiple-output nodes, the priority inversion algorithm achieves better performance than the slot analyzing algorithm when the system is in a high-criticality mode. Evaluation results show the effectiveness and efficacy of our approaches.