The DOI matches its Crossref record.
Characterizing mobility and network usage in a corporate wireless local-area network
Abstract
Wireless local-area networks are becoming increasingly popular. They are commonplace on university campuses and inside corporations, and they have started to appear in public areas [17]. It is thus becoming increasingly important to understand user mobility patterns and network usage characteristics on wireless networks. Such an understanding would guide the design of applications geared toward mobile environments (e.g., pervasive computing applications), would help improve simulation tools by providing a more representative workload and better user mobility models, and could result in a more effective deployment of wireless network components.Several studies have recently been performed on wire-less university campus networks and public networks. In this paper, we complement previous research by presenting results from a four week trace collected in a large corporate environment. We study user mobility patterns and introduce new metrics to model user mobility. We also analyze user and load distribution across access points. We compare our results with those from previous studies to extract and explain several network usage and mobility characteristics.We find that average user transfer-rates follow a power law. Load is unevenly distributed across access points and is influenced more by which users are present than by the number of users. We model user mobility with persistence and prevalence. Persistence reflects session durations whereas prevalence reflects the frequency with which users visit various locations. We find that the probability distributions of both measures follow power laws.
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
Cited by
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
The changing usage of a mature campus-wide wireless network
Henderson, T., Kotz, D., & Abyzov, I. (2004) · Proceedings of the 10th annual international conference on Mobile computing and networking · doi:10.1145/1023720.1023739 · Cited by 566
On the validity of geosocial mobility traces
Zhang, Z., Zhou, L., Zhao, X., Wang, G., Su, Y., Metzger, M., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks · doi:10.1145/2535771.2535786 · Cited by 52
Metadata
- Authors
- Balazinska, M., & Castro, P.
- Venue
- Proceedings of the 1st international conference on Mobile systems, applications and services
- Publisher
- ACM
- Published
- 2003-05-05
- Pages
- 303-316
- OpenAlex
- W2160494326
- Cited by
- 481
- References
- 0
- FWCI
- 18.84
- Type
- conference paper
- 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: Characterizing mobility and network usage in a corporate wireless local-area network. https://app.cochat.ai/papers/10.1145/1066116.1066127
- The paper
- Balazinska, M., & Castro, P. (2003). Characterizing mobility and network usage in a corporate wireless local-area network. Proceedings of the 1st international conference on Mobile systems, applications and services. https://doi.org/10.1145/1066116.1066127
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.