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Article Dans Une Revue Proceedings of the VLDB Endowment (PVLDB) Année : 2020

LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization

Yiming Lin
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Daokun Jiang
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Georgios Bouloukakis
Andrew Chio
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Sharad Mehrotra
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Nalini Venkatasubramanian
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Résumé

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks-first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.
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Dates et versions

hal-03039546 , version 1 (16-12-2020)

Identifiants

Citer

Yiming Lin, Daokun Jiang, Roberto Yus, Georgios Bouloukakis, Andrew Chio, et al.. LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization. Proceedings of the VLDB Endowment (PVLDB), 2020, 14 (3), ⟨10.5555/3430915.3442432⟩. ⟨hal-03039546⟩
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