The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing
Abstract
The emergence of MCS technologies provides a cost-efficient solution to accommodate large-scale sensing tasks. However, despite the potential benefits of MCS, there are several critical issues that remain to be solved, such as lack of incentive-compatible mechanisms for recruiting participants, lack of data validation, and high traffic load and latency. This motivates us to develop robust mobile crowd sensing (RMCS), a framework that integrates deep learning based data validation and edge computing based local processing. First, we present a comprehensive state-of-the-art literature review. Then, the conceptual design architecture of RMCS and practical implementations are described in detail. Next, a case study of smart transportation is provided to demonstrate the feasibility of the proposed RMCS framework. Finally, we identify several open issues and conclude the article.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566
Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1460412.1460444 · Cited by 1,271
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667
Can Deep Learning Revolutionize Mobile Sensing?
Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282
Basudan, S., Lin, X., & Sankaranarayanan, K. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2666783 · Cited by 265
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
Chen, X., Pu, L., Gao, L., Wu, W., & Wu, D. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600321 · Cited by 239
A Novel Deployment Scheme for Green Internet of Things
Huang, J., Meng, Y. S., Gong, X., Liu, Y., & Duan, Q. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2301819 · Cited by 227
A Secure Mobile Crowdsensing Game With Deep Reinforcement Learning
Xiao, L., Li, Y., Han, G., Dai, H., & Poor, H. V. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737968 · Cited by 180
Research works printed but not linked: 2
- mohan, 0, Nericell: Rich Monitoring of Road and Traffic Conditions Using Mobile Smartphones, Proc ACM Conf Embedded Networked Sensor Systems, 323
- 0, Sensorly - Unbiased Wireless Network Information
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503
“In intelligent mobile crowdsensing systems, traditional AI/ML architectures have been deployed for model training and processing [92].”
MethodFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
Pham, Q., Fang, F., Ha, V. N., Piran, M. J., Le, M., Le, L. B., Hwang, W., & Ding, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3001277 · Cited by 887
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarcı, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 610
Metadata
- Authors
- Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J.
- Affiliations
- North China Electric Power University (CN)
- Kogakuin University (JP)
- Instituto de Telecomunicações (PT)
- Venue
- IEEE Network
- Published
- 2018-07
- Volume
- 32
- Issue
- 4
- Pages
- 54-60
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2887817291
- Cited by
- 260
- References
- 14
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 12 · 2023: 28 · 2022: 36 · 2021: 41 · 2020: 58 · 2019: 74 · 2018: 2
- FWCI
- 55.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Network (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- mobile crowdsensing, deep learning, edge computing, data validation, incentive mechanisms, participant recruitment, traffic load, latency, smart transportation
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
