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TitleScalable and efficient clustering for fingerprint-based positioning
Author(s)Torres-Sospedra, Joaquín
Quezada Gaibor, Darwin P.
Nurmi, Jari
Koucheryavy, Yevgeni
Lohan, Elena Simona
Huerta, Joaquin
KeywordsClustering algorithms
Wireless fidelity
Computational modeling
Internet of Things
Fingerprint recognition
Bluetooth low energy (BLE)
received signal strength (RSS)
affinity propagation
indoor localization
Issue date15-Feb-2023
JournalIeee Internet of Things Journal
CitationJ. Torres-Sospedra, D. P. Quezada Gaibor, J. Nurmi, Y. Koucheryavy, E. S. Lohan and J. Huerta, "Scalable and Efficient Clustering for Fingerprint-Based Positioning," in IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3484-3499, 15 Feb.15, 2023, doi: 10.1109/JIOT.2022.3230913.
Abstract(s)Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by approximate to 7% with respect to fingerprinting with the traditional clustering models.
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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