CoChat
Sign Up
Cited by 9

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

Tefera, Getenet, She, Kun, Shelke, Maya, Ahmed, Awais·2021·Sustainable Computing: Informatics and Systems
DOI 10.1016/j.suscom.2021.100555
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.suscom.2021.100555

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. Chen, 2015, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Trans. Netw., 24, 2795, 10.1109/TNET.2015.2487344 · DOI · Paper page
  2. Chen, 2014, Decentralized computation offloading game for mobile cloud computing, IEEE Trans. Parallel Distrib. Syst., 26, 974, 10.1109/TPDS.2014.2316834 · DOI
  3. Chen, 2018, Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning, IEEE Internet Things J., 6, 4005, 10.1109/JIOT.2018.2876279 · DOI · Paper page
  4. Fooladivanda, 2012, Joint resource allocation and user association for heterogeneous wireless cellular networks, IEEE Trans. Wireless Commun., 12, 248, 10.1109/TWC.2012.121112.120018 · DOI
  5. He, 2017, Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach, IEEE Trans. Veh. Technol., 67, 44, 10.1109/TVT.2017.2760281 · DOI · Paper page
  6. El-Sayed, 2017, Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment, IEEE Access, 6, 1706, 10.1109/ACCESS.2017.2780087 · DOI
  7. Cao, 2019, Intelligent offloading in multi-access edge computing: a state-of-the-art review and framework, IEEE Commun. Mag., 57, 56, 10.1109/MCOM.2019.1800608 · DOI
  8. Zheng, 2018, Dynamic computation offloading for mobile cloud computing: a stochastic game-theoretic approach, IEEE Trans. Mob. Comput., 18, 771, 10.1109/TMC.2018.2847337 · DOI
  9. Cao, 2017, Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: a game-theoretic machine learning approach, IEEE Trans. Veh. Technol., 67, 752, 10.1109/TVT.2017.2740724 · DOI
  10. Min, 2019, Learning-based computation offloading for IoT devices with energy harvesting, IEEE Trans. Veh. Technol., 68, 1930, 10.1109/TVT.2018.2890685 · DOI · Paper page
  11. Hu, 2018, Mobility-aware edge caching and computing in vehicle networks: a deep reinforcement learning, IEEE Trans. Veh. Technol., 67, 10190, 10.1109/TVT.2018.2867191 · DOI · Paper page
  12. Wang, 2018, Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications, IEEE Trans. Ind. Inf., 15, 976, 10.1109/TII.2018.2883991 · DOI
  13. Bouet, 2018, Mobile edge computing resources optimization: a geo-clustering approach, IEEE Trans. Netw. Serv. Manage., 15, 787, 10.1109/TNSM.2018.2816263 · DOI
  14. Yu, 2018, Computation offloading with data caching enhancement for mobile edge computing, IEEE Trans. Veh. Technol., 67, 11098, 10.1109/TVT.2018.2869144 · DOI
  15. Taleb, 2017, On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration, IEEE Commun. Surv. Tutorials, 19, 1657, 10.1109/COMST.2017.2705720 · DOI · Paper page
  16. Alameddine, 2019, Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing, IEEE J. Sel. Areas Commun., 37, 668, 10.1109/JSAC.2019.2894306 · DOI
  17. Guo, 2018, Collaborative computation offloading for multiaccess edge computing over fiber–wireless networks, IEEE Trans. Veh. Technol., 67, 4514, 10.1109/TVT.2018.2790421 · DOI
  18. Moura, 2018, Game theory for multi-access edge computing: survey, use cases, and future trends, IEEE Commun. Surv. Tutorials, 21, 260, 10.1109/COMST.2018.2863030 · DOI · Paper page
  19. Wang, 2019, Smart resource allocation for mobile edge computing: a deep reinforcement learning approach, IEEE Trans. Emerging Top. Comput.
  20. Wei, 2018, Dynamic edge computation offloading for internet of things with energy harvesting: a learning method, IEEE Internet Things J., 6, 4436, 10.1109/JIOT.2018.2882783 · DOI
  21. Asheralieva, 2019, Hierarchical game-theoretic and reinforcement learning framework for computational offloading in UAV-enabled mobile edge computing networks with multiple service providers, IEEE Internet Things J., 6, 8753, 10.1109/JIOT.2019.2923702 · DOI
  22. Qi, 2019, Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach, IEEE Trans. Veh. Technol., 68, 4192, 10.1109/TVT.2019.2894437 · DOI
  23. Dinh, 2018, Learning for computation offloading in mobile edge computing, IEEE Trans. Commun., 66, 6353, 10.1109/TCOMM.2018.2866572 · DOI · Paper page
  24. Liu, 2017, eNB selection for machine type communications using reinforcement learning based Markov decision process, IEEE Trans. Veh. Technol., 66, 11330, 10.1109/TVT.2017.2730230 · DOI
  25. Tefera, 2020, Congestion-aware adaptive decentralised computation offloading and caching for multi-access edge computing networks, IET Commun., 14, 3410, 10.1049/iet-com.2020.0630 · DOI
  26. Qiu, 2019, Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing, IEEE Trans. Veh. Technol., 68, 8050, 10.1109/TVT.2019.2924015 · DOI
  27. Munir, 2019, When edge computing meets microgrid: a deep reinforcement learning approach, IEEE Internet Things J., 6, 7360, 10.1109/JIOT.2019.2899673 · DOI
  28. Wang, 2019, Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning, IEEE Commun. Mag., 57, 64, 10.1109/MCOM.2019.1800971 · DOI
  29. Cheng, 2018, Localized small cell caching: a machine learning approach based on rating data, IEEE Trans. Commun., 67, 1663, 10.1109/TCOMM.2018.2878231 · DOI
  30. Yang, 2018, Multi-access edge computing enhanced video streaming: proof-of-concept implementation and prediction/QoE models, IEEE Trans. Veh. Technol., 68, 1888, 10.1109/TVT.2018.2889196 · DOI
  31. Lien, 2018, Low latency radio access in 3GPP local area data networks for V2X: stochastic optimization and learning, IEEE Internet Things J., 6, 4867, 10.1109/JIOT.2018.2874883 · DOI
  32. Ning, 2019, Deep reinforcement learning for intelligent internet of vehicles: an energy-efficient computational offloading scheme, IEEE Trans. Cognit. Commun. Netw., 5, 1060, 10.1109/TCCN.2019.2930521 · DOI
  33. Chen, 2019, iRAF: a deep reinforcement learning approach for collaborative mobile edge computing IoT networks, IEEE Internet Things J., 6, 7011, 10.1109/JIOT.2019.2913162 · DOI
  34. Wang, 2019, Resource allocation in information-centric wireless networking with D2D-enabled MEC: a deep reinforcement learning approach, IEEE Access, 7, 114935, 10.1109/ACCESS.2019.2935545 · DOI
  35. Thar, 2019, A deep learning model generation framework for virtualized multi-access edge cache management, IEEE Access, 7, 62734, 10.1109/ACCESS.2019.2916080 · DOI
  36. Pham, 2019, Coalitional games for computation offloading in NOMA-enabled multi-access Edge computing, IEEE Trans. Veh. Technol.
  37. Nassar, 2019, Reinforcement learning for adaptive resource allocation in fog RAN for IoT with heterogeneous latency requirements, IEEE Access, 7, 128014, 10.1109/ACCESS.2019.2939735 · DOI
  38. Lei, 2019, Multiuser resource control with deep reinforcement learning in IoT edge computing, IEEE Internet Things J., 6, 10119, 10.1109/JIOT.2019.2935543 · DOI
  39. Wang, 2020, A game-based computation offloading method in vehicular multi-access edge computing networks, IEEE Internet Things J.

Cited by

Sorted by citations

See all 9

Metadata

Authors
Tefera, G., She, K., Shelke, M., & Ahmed, A.
ORCID
Tefera, Getenet
Venue
Sustainable Computing: Informatics and Systems
Publisher
Elsevier BV
Published
2021-06
Volume
30
Pages
100555
ISSN
2210-5379
DOI
10.1016/j.suscom.2021.100555
Cited by
9
References
2
Language
en
Licence

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