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Predicting link quality using supervised learning in wireless sensor networks
Abstract
Routing protocols in sensor networks maintain information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. To address such challenges, this paper explores using supervised learning techniques to make informed decisions in the context of wireless sensor networks. We investigate the design space of both offline learning and online learning and use link quality estimation as a case study to evaluate their effectiveness. For this purpose, we present MetricMap, a metric-based collection routing protocol atop MintRoute that derives link quality using classifiers learned in the training phase, when the traditional ETX approach fails. The offline learning approach is evaluated on a 30-node sensor network testbed, and our results show that MetricMap can achieve up to 300% improvement over MintRoute in data delivery rate for high data rate situations, with no negative impact on other performance metrics. We also explore the possibility of using online learning in this paper.
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“The results reveal that DTs and rule learners achieve the highest accuracy and result in significant improvement in data delivery rates.”
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Metadata
- Authors
- Wang, Y., Martonosi, M., & Peh, L.-S.
- Affiliations
- Princeton University (US)
- Venue
- ACM SIGMOBILE Mobile Computing and Communications Review
- Published
- 2007-07
- Volume
- 11
- Issue
- 3
- Pages
- 71-83
- ISSN
- 1559-1662 (ISSN-L)
- 1931-1222
- OpenAlex
- W2037928371
- Cited by
- 101
- References
- 39
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 1 · 2022: 5 · 2021: 9 · 2020: 13 · 2019: 6 · 2018: 8 · 2017: 9 · 2016: 4 · 2015: 3 · 2014: 8 · 2013: 5 · 2012: 6
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMOBILE Mobile Computing and Communications Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Keywords
- link quality estimation, wireless sensor networks, routing protocols, supervised learning, ETX, online learning, offline learning, data delivery ratio
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 ›
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