NIDCETMay 28

Scheduling Mechanisms in Wireless Sensor-Actuator Networks for Multi-rate Periodic Control in Industry 4.0

arXiv:2605.3052038.1h-index: 16
Predicted impact top 31% in NI · last 90 daysOriginality Incremental advance
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This work addresses the problem of reliable and efficient real-time scheduling for multi-rate control systems in Industry 4.0, which is an incremental improvement for industrial automation engineers.

This paper tackles real-time scheduling in wireless sensor-actuator networks for multi-rate control systems in Industry 4.0. The proposed framework, featuring four strategies, improves schedulability by up to 97%, reduces execution time by up to 92%, and minimizes communication and storage costs by up to 99% in simulations.

This paper investigates scheduling strategies for wireless sensor-actuator networks (WSANs) in Industry 4.0 scenarios. In particular, we address the problem of real-time scheduling for multi-rate control systems by proposing a novel framework. Our framework features four strategies that improve reliability, schedulability and execution time, and reduce communication and storage costs. Two-phase scheduling is our first strategy, devised to improve communication reliability. Our second strategy is the least-laxity-first with remaining conflicts (LLF-RC) scheduling algorithm, which has high schedulability and affordable execution time. LLF-RC also keeps the maximum queue length at a moderate level, making it suitable for storage-constrained devices. Our third and fourth strategies are opportunistic aggregation and repetitive scheduling. Opportunistic aggregation performs simple and effective packet aggregation, enhancing schedulability by up to 97% and reducing execution time by up to 29%, in our simulation. Repetitive scheduling has negligible execution time, and contributes to minimize communication and storage costs. It reduces the maximum execution time by 92% and the maximum communication and storage cost by 99%, in our simulation. We compare our proposed framework against existing approaches, and evaluate the advantages of our strategies in realistic scenarios.

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