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RIS-assisted device-edge collaborative edge computing for industrial applications

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Abstract

In the Industrial Internet of Things (IIoT), a significant amount of perceived data is generated from massive IoT devices, which requires timely computing for value maximization. Multi-access edge computing (MEC), which deploys computing nodes close to the data source, is a promising computing paradigm for IIoT applications. However, due to the limited computation resource, it is challenging for edge nodes to provide a low delay to massive data. In addition, the wireless transmission environment varies with IoT devices over time. Some data even cannot be uploaded to the edge server due to the worse link quality. Reconfigurable intelligent surface (RIS), which deploys passive reflecting elements between end users and base station to reflect wireless signals, is a new technique for changing the wireless transmission performance via reconfiguring the phase shift of RIS. It is beneficial to apply RIS in MEC for reducing transmission delay and achieving green edge computing. This paper considers a RIS-assisted device-edge collaborative MEC for industrial applications. We propose to minimize the energy consumption of IoT devices constrained to the delay requirements via jointly optimizing the offloading decisions between end and edge computing nodes, the phase shift of RIS, CPU resource allocation of edge server, and transmission power of IoT devices. A distributed and cooperative scheme, called RIS-assisted DAEM, which includes the DAECO and DCEM algorithms for CO and PORA subproblems, respectively, is proposed to solve the formulated problem. The simulation results have illustrated the efficiency of the proposal for energy consumption reduction constrained to the delay requirements.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62273109 and 61901128, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (21KJB510032).

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Authors and Affiliations

  1. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510660, China

    Mian Guo & Chengyuan Xu

  2. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Mithun Mukherjee

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  1. Mian Guo
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  2. Chengyuan Xu
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  3. Mithun Mukherjee
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Contributions

Mian Guo and Mithun Mukherjee wrote the main part of the manuscript. Mian Guo developed the model and performed experiments. Chengyuan Xu performed the experiments. All authors read and approved the final manuscript.

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Correspondence to Mian Guo.

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Guo, M., Xu, C. & Mukherjee, M. RIS-assisted device-edge collaborative edge computing for industrial applications. Peer-to-Peer Netw. Appl. 16, 2023–2038 (2023). https://doi.org/10.1007/s12083-023-01522-9

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