<div class="csl-bib-body">
<div class="csl-entry">Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J. C. S., Dustdar, S., & Liu, J. (2023). Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things. <i>IEEE Transactions on Industrial Informatics</i>, <i>19</i>(1), 480–490. https://doi.org/10.1109/TII.2022.3158974</div>
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dc.identifier.issn
1551-3203
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/136160
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dc.description.abstract
Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this work, we investigate task cooffloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.
en
dc.language.iso
en
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dc.publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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dc.relation.ispartof
IEEE Transactions on Industrial Informatics
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dc.subject
device-to-device (D2D) offloading
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dc.subject
industrial Internet of Things (IIoT) devices
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dc.subject
Mobile edge computing (MEC)
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dc.subject
multi-armed bandit (MAB)
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dc.title
Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things