Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things
Abstract
Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, 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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
With more devices connected, network congestion can happen, causing packet loss, increased latency, and delays in data transmission.
“With more devices connected, network congestion can happen, which can cause packet loss, increased latency, and delays in data transmission [35].”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [35]
Cites
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033 · see citing works
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607 · see citing works
Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3061981 · Cited by 697 · see citing works
Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing
Jiang, H., Dai, X., Xiao, Z. Q., & Iyengar, A. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3150432 · Cited by 413 · see citing works
Self-citationCooperative Service Caching and Workload Scheduling in Mobile Edge Computing
Ma, X., Zhou, A., Zhang, S., & Wang, S. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155455 · Cited by 246 · see citing works
Tan, L., Kuang, Z., Zhao, L. Y., & Liu, A. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3108641 · Cited by 232 · see citing works
Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing
Saleem, U., Liu, Y., Jangsher, S., Li, Y., & Jiang, T. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024538 · Cited by 226 · see citing works
D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks
He, Y., Ren, J., Yu, G., & Cai, Y. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2896999 · Cited by 223 · see citing works
Wang, Y., Wang, K., Huang, H., Miyazaki, T., & Guo, S. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2883991 · Cited by 213 · see citing works
Vehicular Task Offloading via Heat-Aware MEC Cooperation Using Game-Theoretic Method
Xiao, Z. Q., Dai, X., Jiang, H., et al. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2960631 · Cited by 176 · see citing works
Self-citationDistributed and Dynamic Service Placement in Pervasive Edge Computing Networks
Ning, Z., Dong, P., Wang, X., Wang, S., Hu, X., Guo, S., Qiu, T., Hu, B., & Kwok, R. Y. K. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3046000 · Cited by 171 · see citing works
Offloading Time Optimization via Markov Decision Process in Mobile-Edge Computing
Yang, G., Hou, L., He, X., He, D., Chan, S., & Guizani, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3033285 · Cited by 145 · see citing works
An Online Framework for Joint Network Selection and Service Placement in Mobile Edge Computing
Gao, B., Zhou, Z., Liu, F., et al. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3064847 · Cited by 143 · see citing works
An Online Incentive Mechanism for Collaborative Task Offloading in Mobile Edge Computing
Li, G., & Cai, J. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2947046 · Cited by 139 · see citing works
Learning-Based URLLC-Aware Task Offloading for Internet of Health Things
Zhou, Z., Wang, Z., Yu, H., Liao, H., Mumtaz, S., Oliveira, L., & Frascolla, V. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020680 · Cited by 121 · see citing works
Blockchain and Learning-Based Secure and Intelligent Task Offloading for Vehicular Fog Computing
Liao, H., Mu, Y., Zhou, Z., Sun, M., Wang, Z., & Pan, C. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3007770 · Cited by 114 · see citing works
Incentive-Driven Deep Reinforcement Learning for Content Caching and D2D Offloading
Zhou, H., Wu, T., Zhang, H., et al. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087232 · Cited by 113 · see citing works
Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles
Xu, X., Wu, Q., Qi, L., Dou, W., Tsai, S., & Bhuiyan, M. Z. A. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2995622 · Cited by 111 · see citing works
Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing
Jošilo, S., & Dán, G. (2019) · doi:10.1109/infocom.2019.8737480 · Cited by 101 · see citing works
Collaborate or Separate? Distributed Service Caching in Mobile Edge Clouds
Xu, Z., Zhou, L., Chau, C.-K., Liang, W., Xia, Q., & Zhou, P. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155365 · Cited by 100 · see citing works
Peng, J., Qiu, H., Cai, J., et al. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3062616 · Cited by 90 · see citing works
Online Computation Offloading and Resource Scheduling in Mobile-Edge Computing
Liu, T., Zhang, Y., Zhu, Y., Tong, W., & Yang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3051427 · Cited by 88 · see citing works
Sun, Y., Zhou, S., & Niu, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3030889 · Cited by 78 · see citing works
Adaptive Offloading for Time-Critical Tasks in Heterogeneous Internet of Vehicles
Liu, C., Liu, K., Guo, S., Xie, R., Lee, V. C. S., & Son, S. H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2997720 · Cited by 75 · see citing works
D2D-Enabled Mobile-Edge Computation Offloading for Multiuser IoT Network
Yang, Y., Long, C., Wu, J. B., Peng, S., & Li, B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3068722 · Cited by 74 · see citing works
Latency Minimization for Mobile Edge Computing Networks
Chen, C.-L., Brinton, C. G., & Aggarwal, V. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3117511 · Cited by 74 · see citing works
Tang, J., Tang, H., Zhang, X. Y., Cumanan, K., Chen, G., Wong, K., & Chambers, J. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2954127 · Cited by 67 · see citing works
Sun, M., Xu, X., Huang, Y., Wu, Q., Tao, X., & Zhang, P. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3041673 · Cited by 64 · see citing works
Rahman, A., Jin, J., Cricenti, A., et al. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2874693 · Cited by 59 · see citing works
DIYA: Tactile Internet Driven Delay Assessment NOMA-Based Scheme for D2D Communication
Budhiraja, I., Tyagi, S., Tanwar, S., Kumar, N., & Rodrigues, J. J. P. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2910532 · Cited by 59 · see citing works
Tao, O., Chen, X., Zhou, Z., Li, L., & Tan, X. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3106746 · Cited by 54 · see citing works
Hamdi, M., Hamed, A. B., Yuan, D., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3110319 · Cited by 44 · see citing works
Quality of Experience-Aware User Allocation in Edge Computing Systems: A Potential Game
Lai, P., He, Q., Cui, G., Chen, F., Abdelrazek, M., Grundy, J., Hosking, J., & Yang, Y. J. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00036 · Cited by 40 · see citing works
D2D Task Offloading: A Dataset-Based Q&A
Chatzopoulos, D., Bermejo, C., Haq, E.-U., Li, Y., & Hui, P. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700873 · Cited by 36 · see citing works
Energy-Efficient D2D-Assisted Computation Offloading in NOMA-Enabled Cognitive Networks
Cheng, Y., Liang, C., Chen, Q., & Yu, F. R. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3093892 · Cited by 33 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38 · see citing works
- Authors
- Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J. C. S., Dustdar, S., & Liu, J.
- Affiliations
- Hunan University (CN)
- Charles Darwin University (AU)
- Chinese University of Hong Kong (HK)
- TU Wien (AT)
- Simon Fraser University (CA)
- ORCID
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2023-01
- Volume
- 19
- Issue
- 1
- Pages
- 480-490
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4226490845
- Cited by
- 304
- References
- 35
- Citations by year
- 2026: 30 · 2025: 71 · 2024: 102 · 2023: 96 · 2022: 5
- FWCI
- 30.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (U20A20181)
- Key Research and Development Project of Hunan Province of China (2022GK2020)
- Hunan Natural Science Foundation of China (2022JJ2059)
- Funding Projects of Zhejiang (2021LC0AB05)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- device-to-device communication, mobile edge computing, Industrial Internet of Things, migration costs, edge node selection, task delay
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
