The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability
Abstract
Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.
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“If an edge node experiences a failure or disruption, the cloud server can seamlessly take over the processing tasks, which ensures continuous operation and minimizes downtime [27].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [27]
Citations
Cites
A survey of mobile cloud computing: architecture, applications, and approaches
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“One recent popular research field for providing high-computational capability service is mobile cloud computing (MCC) [3], which can offload the computation tasks of MDs to the more powerful cloud center(CC) and centralized process the tasks.”
BackgroundEnergy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
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User Association for Load Balancing in Heterogeneous Cellular Networks
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Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
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Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
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Collaborative Cloud and Edge Computing for Latency Minimization
Ren, J., Yu, G., He, Y., & Li, G. Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2904244 · Cited by 685
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Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
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Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges
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Flagged first, then by citations
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- 2014, Mobile-Edge Computing-Introductory Technical White Paper
- grant, 2014, CVX Matlab Software for Disciplined Convex Programming Version 2 1
- ikuno, 2010, 3GPP TR 36 814 v9 0 0-evolved universal terrestrial radio access (e-UTRA) further advancements for e-utra physical layer aspecs
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Sorted by citations
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655
Liu, J. B., Xiang, J., Jin, Y., Liu, R., Yan, J., & Wang, L. (2021) · Remote Sensing · doi:10.3390/rs13214387 · Cited by 212
Akhlaqi, M. Y., & Hanapi, Z. M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103568 · Cited by 172
Reverse Auction-Based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing
Zhou, H., Wu, T., Chen, X., He, S., Guo, D., & Wu, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3189050 · Cited by 165
A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing
Wang, B., Wang, C., Huang, W., Song, Y., & Qin, X. (2020) · IEEE Access · doi:10.1109/access.2020.3029649 · Cited by 157
Metadata
- Authors
- Kai, C., Zhou, H., Yi, Y., & Huang, W.
- Affiliations
- Hefei University of Technology (CN)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2021-06
- Volume
- 7
- Issue
- 2
- Pages
- 624-634
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W3080122404
- Cited by
- 249
- References
- 40
- Citations by year
- 2026: 30 · 2025: 50 · 2024: 52 · 2023: 63 · 2022: 38 · 2021: 14
- FWCI
- 13.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- Funders
- National Natural Science Foundation of China (61971176, 61901156)
- Anhui Provincial Natural Science Foundation (2008085QF281)
- Fundamental Research Funds for the Central Universities of China (PA2020GDKC0008, JZ2019HGBZ0144)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- mobile edge computing, task offloading, cloud-edge-end collaboration, latency reduction, power allocation, successive convex approximation, non-convex optimization, mobile devices
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