Change Point Detection in WLANs with Random AP Forests
Authors: 
Alexis Huet, Jonatan Krolikowski, Jose Manuel Navarro, Fuxing Chen, Dario RossiAuthors Info & Claims
Alexis Huet, Jonatan Krolikowski, Jose Manuel Navarro, Fuxing Chen, Dario RossiAuthors Info & ClaimsPages 29 - 36
Abstract
Troubleshooting WiFi networks is knowingly difficult due to the variability of the wireless medium. Complementary to existing works that focus on detecting short-term fluctuations of radio signals (i.e., anomalies), we tackle the problem of reliably detecting long-term changes in statistical properties of WiFi networks. We propose a new method to reliably gain insights on such environmental changes, which we refer to as Random Access Point Forest (RAPF). RAPF identifies the changes from a forest of individual learners, each of them consisting of a random tree approximating the signal of a specific pair of APs. The biased selection of APs in a distributed manner along with the stochastic construction of each individual tree ensure its robustness to noise and biases. We conduct a measurement campaign on a real WLAN by collecting the path loss among pairs of APs in a network for which labels are available and perform an extensive comparison of our methodology against state-of-the-art change point methodologies, which conclusively shows RAPF to yield the most robust detection capabilities.
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Information & Contributors
Information
Published In
December 2023
80 pages
ISBN:9798400704079
DOI:10.1145/3624354
- General Chairs:
- Dario Rossi,
- Stefano Secci,
- Program Chairs:
- Olivier Bonaventure,
- Lili Qiu
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Published: 05 December 2023
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CoNEXT 2023
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CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies
December 5 - 8, 2023
Paris, France
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