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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.1203 · Cited by 2,391 · cited 1×
“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
You, C., Huang, K., Chae, H., & Kim, B. (2016) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2633522 · Cited by 1,516
User Association for Load Balancing in Heterogeneous Cellular Networks
Ye, Q., Rong, B., Chen, Y., Al‐Shalash, M., Caramanis, C., & Andrews, J. G. (2013) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2013.040413.120676 · Cited by 1,223
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Sardellitti, S., Scutari, G., & Barbarossa, S. (2015) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2015.2448520 · Cited by 889
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., Li, P., Maharjan, S., & Zhang, K. (2016) · IEEE Access · doi:10.1109/access.2016.2597169 · Cited by 858
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
Beck, A., Ben‐Tal, A., & Tetruashvili, L. (2009) · Journal of Global Optimization · doi:10.1007/s10898-009-9456-5 · Cited by 600
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Wang, J., Zhao, L., Liu, J., & Kato, N. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902661 · Cited by 460
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
Ren, J., Yu, G., Cai, Y., & He, Y. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2845360 · Cited by 452
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges
Abolfazli, S., Sanaei, Z., Ahmed, E., Gani, A., & Buyya, R. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.070813.00285 · Cited by 441
Flagged first, then by citations
Research works printed but not linked: 3
- 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
Cited by
Sorted by citations
Items 201–249 of 249
Ke, H., Zhao, J., Ding, Y., & Pan, L. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108002 · Cited by 1
Xue, T., Zhang, Y., Wang, Y., Wang, W., Li, S., & Zhang, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4845473 · Cited by 1
The Research of Cloud-Edge Collaboration Technique for Energy Internet
明, 阳. (2021) · Computer Science and Application · doi:10.12677/csa.2021.115146
Modeling and Performance Analysis of Cloud-Based Active Distribution Networks Based on EdgeCloudSim
Lin, Z., Wang, Q., Yang, S., Luo, B., Ma, Q., & Yu, C. (2023) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee56931.2023.10136018
A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory
Song, Q.-F. S. Q.-F., Song, J. W. Q.-F., Wang, J.-X. G. J., & Gao, J.-H. L. J.-X. (2023) · 電腦學刊 · doi:10.53106/199115992023103405011
Collaborative Task Processing for Multiple Edge Nodes Based on Service PlacementPreprint
Shi, L., Feng, S., Ji, R., Xu, J., & Xu, D. (2024) · Research Square · doi:10.21203/rs.3.rs-3804016/v1
Zhang, J., Meng, Z., Miao, F., Xiong, Y., Xu, Z., Yun, Y., & Wang, R. (2023) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom61073.2023.00010
Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers
Shi, L., Feng, S., Ji, R., Xu, J., Ding, X., & Zhan, B. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54521-4_21
Managing and Decreasing Power Consumption of Devices in a Smart City Environment
Salim, A. S. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3810
Trusted Task Offloading and Resource Allocation Strategy in MEC Environment
Tong, Z., Wang, J., & Li, K. (2024) · Studies in big data · doi:10.1007/978-981-97-2644-8_5
Lim, W. Y. B., Xiong, Z., Niyato, D., Zhang, J., & Shen, X. (. (2024) · Wiley eBooks · doi:10.1002/9781394188932.ref
Knowledge-Defined Internet of Things Architecture for Cloud-Edge Collaborative Industrial Monitoring
Zhou, H., Xing, F., Tong, F., Wang, M., Zhang, Y., & Yang, S. (2024) · International Conference on Frontiers of Intelligent Manufacturing and Automation · doi:10.1145/3704558.3705520
Li, Y., Wu, Y., & Wang, S. (2025) · Mathematics · doi:10.3390/math13040561
Ge, J., Song, Z., Wu, D., Li, J., Yan, Y., & Li, Z. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-1671-8_58
Metaverse: A Comprehensive Review of Concepts, Characteristics and Challenges
Mangla, A., Singh, C., Bindal, A. K., & Mangla, N. (2025) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict65753.2025.00115
Han, Y., Tan, Y., Zeng, S., Hu, D., Wang, H., & Zhu, Z. Q. (2025) · IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering (AAIEE) · doi:10.1109/aaiee64965.2025.11101582
Efficient Energy Theft Detection Utilizing Hierarchical Federated Learning
Shahid, S. S., Salman, T., Baza, M., & Srivastava, G. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106795
Tensor-based task allocation using multi-objective optimization in GECC environment
Liu, H., Huang, L., Tian, Y., Ding, J., Yin, X., & Zhang, G. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108316
Quesada: A Framework for Reliable and Trustworthy Data Acquisition in 6G-IoT
Xiang, Z., Ying, F., Wang, D., Tan, R., Zhang, Y., & Dustdar, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3621446
Lagnfdi, O., Myyara, M., & Darif, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-02312-4_17
Optimizing Dual-Task Q-Learning with latency and attention-aware schemes in edge computing
Swapna, B., & Mohan, V. M. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114272
Cloud Computing as a Catalyst for Technological Innovation and Industry Transformation
Bhardwaj, H., & Kashyap, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0154-9.ch002
Maritime UAV Mission Classification and Pattern Analysis Based on Edge Intelligence Framework
Wu, J., Qiu, H., Qu, L., Liu, P., Li, N., & Wu, Y. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11294516
Zhang, Z. (2025) · Frontiers in Science and Engineering · doi:10.54691/z9jxrp40
Dependent Task Offloading in Cloud-Edge-End Environments with Communication Heterogeneity
Zhang, G., Zhao, Y., & Sun, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13589-6_15
MUSES: Secure Multi-user Encrypted Speech Search in Cloud-Edge-End Environments
Zheng, K., Liu, J., Ou, S., Xu, Z., Wu, X., & Wu, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-3551-4_8
The Metaverse and The Real Estate Industry
Luna, S. M., Xu, J., Tigwell, G. W., & Papangelis, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-03296-6_12
DEOF: Discerning and Elastic Offloading for Accuracy-Efficient Video Analytics
Pan, H., Chen, N., Wang, X., Xing, Y., Zhang, S., & Wu, J. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323028
A Framework for Digital Intelligent Anomaly Control in High-End Equipment Design and Manufacturing
Sun, R., Ma, Y., Ning, S., Ma, J., Wei, C., & Shi, Y. (2025) · International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · doi:10.1109/icbaie66852.2025.11326617
Kumar, K. R., & Sivakumar, C. (2026) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/icjece.2025.3635704
5G Network Edge Intelligence for Smart Operation and Maintenance of Offshore Wind Power
Gao, Y., Yang, L., Zhu, X., Jiang, C., Wang, H., You, S., & Xu, F. (2026) · Sensors · doi:10.3390/s26041390
DACC: Discerning and adaptive offloading for coarse-grained content-aware video analytics
Pan, H., Chen, N., Huang, H., Sun, Y.-E., Wang, X., Xing, Y., Zhang, S., & Wu, J. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112130
Rizk, M., Taleb, H., Rhayem, A., Chaaya, J. A., Zaki, C., & Nasser, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8815873/v1
Hu, Y., Cheng, H., Li, H., Guo, F., Wang, H., Hu, M., & Peng, K. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3682947
Digital Twin Reconstruction Strategy for Intelligent Transportation System
Chen, J., Yin, R., Ying, K., Wu, C., Zhang, Y., Chen, X., & Lu, W. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3693332
AdaSprite: Resource-efficient Online Co-Adaptation for V2I Systems Under Large-scale Data Drifts
Wang, L., Yu, Z., Liu, S., Chen, K., Wu, F. 中., & Guo, B. (2026) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3745756.3809211
Liu, F., Huang, D., Meng, T., Luo, Q., Jin, J., & Huang, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6864516
Efficient Distributed Inference and Collaborative Optimization for Unmanned Clusters
Yang, Y., Zhao, S., & Li, S. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-4897-2_63
CHIME: Cost-Constrained Hybrid Popularity-Aware Intelligent Service Caching Framework for MEC
Zheng, T., Yang, P., Shen, K., Zhai, C., Lv, W., Ding, Y., Zeng, H., Li, H., & Wang, Q. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3709908
Optimisation of Web-Edge-Cloud Intelligent Collaborative Rendering System for Dynamic Web3D Scenes
Liu, C., Zhang, Y., Guo, J., Xie, L., & Zhang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22264-0_39
Yadav, P., & Srivastava, S. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112569
Chen, W., Duan, J., Cheng, Z., Ren, H., & Lu, R. (2026) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls71227.2026.11610071
Multiple slicing task offloading in cloud-edge elastic optical networks
Chen, B., Fan, Y., Wang, X., Chen, H., Shao, W., Peng, L., Ho, P., & Li, Y. (2026) · Journal of Optical Communications and Networking · doi:10.1364/jocn.603834
Zhang, Z., Xu, W., Xie, D., Jiang, X., Bin, Z., Dong, L., Ge, G., & Wan, Z. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3721659
Task Offloading in Marine Edge Clouds: A Lyapunov-Guided Multi-Agent DRL Approach
Luo, W., Sun, Y., Xu, W., Min, M., Peng, H., & Chen, Z. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2026.3724795
Novel networking structure for supporting user mobility in multi-access edge computing
Feng, C. Q., Wang, Y., Sun, J., Zhai, L., Zhang, J., & Li, T. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112764
Liu, Y., Madhukumar, H., Li, H., Vasilakos, X., Hussain, R., & Simeonidou, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7606725
A pipelined optimization framework for carbon-aware orchestration in the edge-cloud-HPC continuum
Alruwaili, F. F., Alsolai, H., Alotaibi, S., Khan, J., Khalil, S. F., Alnori, A. S., & Ebad, S. A. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08913-8
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
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
