The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration
Abstract
In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user's motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% ~ 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
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
The ONE simulator for DTN protocol evaluation
Keränen, A., Ott, J., & Kärkkäinen, T. (2009) · Proceedings of the Second International ICST Conference on Simulation Tools and Techniques · doi:10.4108/icst.simutools2009.5674 · Cited by 2,315
Stochastic Network Optimization with Application to Communication and Queueing Systems
Neely, M. J. (2010) · Synthesis lectures on communication networks · doi:10.2200/s00271ed1v01y201006cnt007 · Cited by 2,218
1 A Survey on Device-to-Device Communication in Cellular NetworksPreprint
Asadi, A., Member, S., Wang, Q., Member, S., & Mancuso, V. (2016) · Cited by 2,007
A Survey on Device-to-Device Communication in Cellular Networks
Asadi, A., Wang, Q., & Mancuso, V. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2319555 · Cited by 1,824
Design aspects of network assisted device-to-device communications
Fodor, G., Dahlman, E., Mildh, G., Parkvall, S., Reider, N., Miklós, G., & Turányi, Z. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6163598 · Cited by 1,299
Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks
Yu, C.-H., Doppler, K., Ribeiro, C. B., & Tirkkonen, O. (2011) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2011.060811.102120 · Cited by 1,049
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 891
Cited by
5G d2d networks: techniques, challenges, and future prospects
Ansari, R. I., Chrysostomou, C., Hassan, S. A., Guizani, M., Mumtaz, S., Rodriguez, J., & Rodrigues, J. J. P. C. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2773633 · Cited by 350
Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information
Lyu, X., Ni, W., Tian, H., Liu, R. P., Wang, X., Giannakis, G. B., & Paulraj, A. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2760186 · Cited by 251
Task Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks
Wang, K., Jin, J., Yang, Y., Zhang, T., Nallanathan, A., Tellambura, C., & Jabbari, B. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3227102 · Cited by 95
A novel Distributed AI framework with ML for D2D communication in 5G/6G networks
Ioannou, I., Christophorou, C., Vassiliou, V., & Pitsillides, A. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108987 · Cited by 45
Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality
Ren, C., Lyu, X., Ni, W., Tian, H., & Liu, R. P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2867481 · Cited by 22
Metadata
- Authors
- Pu, L., Chen, X., Xu, J., & Fu, X.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2016-12
- Volume
- 34
- Issue
- 12
- Pages
- 3887-3901
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W2547443968
- Cited by
- 369
- References
- 26
- FWCI
- 44.85
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration. https://app.cochat.ai/papers/10.1109/jsac.2016.2624118
- The paper
- Pu, L., Chen, X., Xu, J., & Fu, X. (2016). D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/jsac.2016.2624118
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
