Abstract
Due to restricted resources such as computing power and battery capacity of Industrial Internet of Things (IIoT) equipments, computation-intensive tasks need to be migrated to edge or cloud servers for execution. To improve the processing efficiency of tasks with limited computation and network resources, we study the problem of joint allocation of network and computational resources in the cloud-edge collaborative IIoT, with the goal of minimizing the average task delay and total system energy consumption. To address this issue, we propose a prioritized action sampling-based Dueling DQN (PASD) algorithm to determine task offloading and resource allocation strategies. Finally, we evaluate PASD through large-scale simulation experiments and NBUFlow, which is an IoT experimental platform equipped with object recognition and pose detection applications. Compared with baselines, PASD has significant advantages in reducing the total energy consumption of the system, and has a good performance in reducing task delay and task throw rate.
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Acknowledgements
This work was supported by the Natural Science Foundation of Ningbo City (2021J090) and Ningbo Manicipal Commonweal S &T Project (2022S005).
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Qin, W., Chen, H., Wang, L. (2022). PASD: A Prioritized Action Sampling-Based Dueling DQN for Cloud-Edge Collaborative Computation Offloading in Industrial IoT. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_2
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DOI: https://doi.org/10.1007/978-981-19-8350-4_2
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