The Industrial Internet of Things (IIoT) utilizes IoT technology and information technology to center around industrial equipment, achieving the collection, transmission, analysis, and application of industrial data, thereby improving industrial production efficiency. It is an important driving force for the upgrade of industrial informationization. In the IIoT, a large amount of data needs to be computed for real-time control. Cloud computing, as a solution, can provide powerful computing and storage capabilities. Offloading tasks that require substantial computational resources from terminal devices to the cloud can shorten execution time. However, cloud computing also faces challenges such as high latency, network congestion, and data security. Edge computing, as an extension and complement to cloud computing, can enhance computational efficiency by deploying computing resources closer to the data source, meeting the demands of the IIoT in data analysis and real-time control while ensuring data security and privacy. Nevertheless, task offloading in edge computing also presents numerous challenges. The IIoT encompasses a wide variety of tasks, each with different priority levels and deadlines, while edge node resources are limited and heterogeneous. In this context, designing efficient computation offloading strategies to reasonably allocate computational tasks between terminal devices and edge servers to improve system performance becomes a critical issue. Therefore, it is necessary to comprehensively consider task attributes and system states to make offloading decisions that ensure the timeliness and effectiveness of tasks. To this end, Hua-Zhen Zhai from Shandong University has conducted research on the problem of heterogeneous multi-task computation offloading strategies in the IIoT.
References:[1] Hua-Zhen Zhai. Research on Heterogeneous Multi-Task Computation Offloading Strategies in Industrial IoT [D]. Shandong University, 2023.
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