The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Edge server placement in mobile edge computing
Abstract
Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.
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
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
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
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Mach, P., & Becvar, Z. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2682318 · Cited by 3,258
Efficient multi-user computation offloading for mobile-edge cloud computing
Chen, X., Jiao, L., Li, W., & Fu, X. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2487344 · Cited by 2,675
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
MAUI: making smartphones last longer with code offload
Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814441 · Cited by 2,281
(2011) · doi:10.1145/1966445.1966473 · Cited by 1,870
ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading
Kosta, S., Aucinas, A., Hui, P., Mortier, R., & Zhang, X. (2012) · 2012 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2012.6195845 · Cited by 1,186
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
Cuckoo: A Computation Offloading Framework for Smartphones
Kemp, R., Palmer, N., Kielmann, T., & Bal, H. (2012) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29336-8_4 · Cited by 484
Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
“For example, the data from LiDAR and high-definition camera on CAVs is for safety purpose [10], [11]; the data from body area networks (BAN) is for health monitoring; and the data from surveillance cameras is for security [12].”
MethodSurvey on Multi-Access Edge Computing Security and Privacy
Ranaweera, P., Jurcut, A. D., & Liyanage, M. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3062546 · Cited by 356
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156
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, M., Zhou, Q., Wu, H., Lin, W., Ye, K., & Xu, C. (2022) · Software: Practice and Experience · doi:10.1002/spe.3014 · Cited by 35
Metadata
- Authors
- Wang, S., Zhao, Y., Xu, J., Yuan, J., & Hsu, C.-H.
- Venue
- Journal of Parallel and Distributed Computing
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 127
- Pages
- 160-168
- ISSN
- 0743-7315 (ISSN-L)
- OpenAlex
- W2809740924
- Cited by
- 512
- References
- 26
- FWCI
- 32.52
- 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: Edge server placement in mobile edge computing. https://app.cochat.ai/papers/10.1016/j.jpdc.2018.06.008
- The paper
- Wang, S., Zhao, Y., Xu, J., Yuan, J., & Hsu, C.-H. (2019). Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2018.06.008
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.
