Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction DOI Creative Commons
Gu‐In Kwon,

Young-Chul Shim,

Kyungwoon Cho

и другие.

Mathematics, Год журнала: 2025, Номер 13(11), С. 1842 - 1842

Опубликована: Май 31, 2025

The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored scheduling, they often relax deadlines for lower-criticality tasks, which not well suited IIoT systems with strict deadline constraints. In this paper, instead treating as prediction problem, we model it deterministic planning response explicit, observable user input. To end, precompute feasible plans anticipated through offline optimization and switch the appropriate plan at runtime. During process, our approach jointly optimizes processor speeds, memory allocations, edge/cloud offloading decisions, are mutually interdependent. Simulation results show that proposed framework achieves up 73.1% energy savings compared baseline system, 100% compliance production low-latency responsiveness user-interaction tasks. We anticipate will contribute design efficient, adaptive, sustainable manufacturing systems.

Язык: Английский

Dynamic task allocation in fog computing using enhanced fuzzy logic approaches DOI Creative Commons

Wanying Jin,

Amin Rezaeipanah

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 27, 2025

Язык: Английский

Процитировано

0
Fault-tolerant and mobility-aware loading via Markov chain in mobile cloud computing DOI Creative Commons
Ning Wang, Ya Li, Ye Li

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 29, 2025

With the development of better communication networks and other related technologies, IoT has become an integral part modern IT. However, mobile devices' limited memory, computing power, battery life pose significant challenges to their widespread use. As alternate, cloud (MCC) makes good use resources boost storage processing capabilities. This involves moving some program logic cloud, which improves performance saves power. Techniques for mobility-aware offloading are necessary because device movement affects connection quality network access. Depending on less-than-ideal mobility models, insufficient fault tolerance, inaccurate offloading, poor task scheduling just a few limitations that current methods often face. Using fault-tolerant approaches user patterns defined by Markov chain, this research introduces novel decision-making framework offloading. The evaluation findings show compared approaches, suggested method achieves execution speeds up 77.35% faster energy down 67.14%.

Язык: Английский

Процитировано

0
Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction DOI Creative Commons
Gu‐In Kwon,

Young-Chul Shim,

Kyungwoon Cho

и другие.

Mathematics, Год журнала: 2025, Номер 13(11), С. 1842 - 1842

Опубликована: Май 31, 2025

The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored scheduling, they often relax deadlines for lower-criticality tasks, which not well suited IIoT systems with strict deadline constraints. In this paper, instead treating as prediction problem, we model it deterministic planning response explicit, observable user input. To end, precompute feasible plans anticipated through offline optimization and switch the appropriate plan at runtime. During process, our approach jointly optimizes processor speeds, memory allocations, edge/cloud offloading decisions, are mutually interdependent. Simulation results show that proposed framework achieves up 73.1% energy savings compared baseline system, 100% compliance production low-latency responsiveness user-interaction tasks. We anticipate will contribute design efficient, adaptive, sustainable manufacturing systems.

Язык: Английский

Процитировано

0