The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A Cloud-Edge Computing Framework for Cyber-Physical-Social Services
Abstract
Cyber-physical-social systems (CPSSs) represent an emerging paradigm encompassing the cyber world, physical world and social world. One of the main purposes of CPSSs is to provide high-quality, proactive, and personalized services for humans. For CPSSs to realize this purpose, a novel services framework is needed. In this article, we present a tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes. The cloud plane is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets. The edge plane is used to process small-scale, short-term, local data, which is used to present the real-time situation. Also, personalized services will be directly provided for humans by the edge plane according to the obtained feature, law, or rule sets and the local high-quality data obtained in the edge plane. Then a tensor-based services model is proposed to match the requirement of users in the local CPSS. Finally, a case study about CPSS services is proposed to demonstrate the application features of the proposed framework.
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
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375
Self-citationThe big challenges of big data
Marx, V. (2013) · Nature · doi:10.1038/498255a · Cited by 1,225
A Unified Framework for Street-View Panorama Stitching
Li, L., Yao, J., Xie, R., Xia, M., & Zhang, W. (2016) · Sensors · doi:10.3390/s17010001 · Cited by 873
Multilinear Analysis of Image Ensembles: TensorFaces
Vasilescu, M. A. O., & Terzopoulos, D. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47969-4_30 · Cited by 836
A survey on mobile edge computing
Ahmed, A., & Ahmed, E. (2016) · 2016 10th International Conference on Intelligent Systems and Control (ISCO) · doi:10.1109/isco.2016.7727082 · Cited by 598
Information and communications technologies for elderly ubiquitous healthcare in a smart home
Deen, M. J. (2015) · Personal and Ubiquitous Computing · doi:10.1007/s00779-015-0856-x · Cited by 244
Self-citationA Tensor-Based Approach for Big Data Representation and Dimensionality Reduction
Kuang, L., Hao, F., Yang, L. T., Lin, M., Luo, C., & Min, G. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2330516 · Cited by 177
Self-citationDeep Computation Model for Unsupervised Feature Learning on Big Data
Zhang, Q., Yang, L. T., & Chen, Z. (2015) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2015.2497705 · Cited by 153
Self-citationHuman motion signatures: analysis, synthesis, recognition
Vasilescu, M. A. O. (2003) · doi:10.1109/icpr.2002.1047975 · Cited by 134
A Tensor-Based Big Service Framework for Enhanced Living Environments
Wang, X., Yang, L. T., Feng, J., Chen, X., & Deen, M. J. (2016) · IEEE Cloud Computing · doi:10.1109/mcc.2016.130 · Cited by 82
Self-citation
Cited by
All one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368
(2019) · NORA - Norwegian Open Research Archives · Cited by 291
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024) · ACM Computing Surveys · doi:10.1145/3659205 · Cited by 130
Xu, Z., Li, J., & Zhang, M. (2021) · IEEE Access · doi:10.1109/access.2021.3077499 · Cited by 79
A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers
Long, S., Li, Z., Xing, Y., Tian, S., Li, D., & Yu, R. (2020) · 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00028 · Cited by 14
Metadata
- Authors
- Wang, X., Yang, L. T., Xie, X., Jin, J., & Deen, M. J.
- Venue
- IEEE Communications Magazine
- Published
- 2017-11
- Volume
- 55
- Issue
- 11
- Pages
- 80-85
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2770198223
- Cited by
- 292
- References
- 15
- FWCI
- 35.22
- 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: A Cloud-Edge Computing Framework for Cyber-Physical-Social Services. https://app.cochat.ai/papers/10.1109/mcom.2017.1700360
- The paper
- Wang, X., Yang, L. T., Xie, X., Jin, J., & Deen, M. J. (2017). A Cloud-Edge Computing Framework for Cyber-Physical-Social Services. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2017.1700360
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.
