ArticleSensors (Basel, Switzerland)2025
Interruption-Aware Computation Offloading in the Industrial Internet of Things.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multi-Agent Deep Reinforcement Learning for Joint Task Offloading and Resource Allocation in IIoT with Dynamic Priorities.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Designing an efficient task offloading system is essential in the Industrial Internet of Things (IIoT). Owing to the limited computational capability of IIoT devices, offloading tasks to edge servers enhances computational efficiency. When an edge server is overloaded, it may experience interruptions, preventing it from serving local devices. Existing studies mainly address interruptions by rerouting, rescheduling, or implementing reactive strategies to mitigate their impact. In this study, we introduce an interruption-aware proactive task offloading framework for IIoT. We develop a load-based interruption model in which the probability of server interruption is formulated as an exponential function of the total computational load, which provides a more realistic estimation of service availability. This framework employs Multi-Agent Advantage Actor-Critic (MAA2C)-a simple yet efficient approach that enables decentralized decision-making while handling large action spaces and maintaining coordination through the centralized critic to make adaptive offloading decisions, taking into account edge availability, resource limitations, device cooperation, and interruptions. Experimental results show that our approach effectively reduces the average total service delay by optimizing the tradeoff between system delay and availability in IIoT networks. Additionally, we investigate the impact of various system parameters on performance under an interruptible edge task offloading scenario, providing valuable insights into how these parameters influence the overall system behavior and efficiency.
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Registered trials
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