New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting
Channel: A-WEAR
Date: 2020-12-10

This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost --around 40% lower-- than the original k-means.

For more details: http://ieeexplore.ieee.org/document/9115419
https://zenodo.org/record/3903103

This project has received funding from the European Union’s Horizon 2020 (H2020) Marie Sklodowska-Curie Innovative Training Networks H2020-MSCA-ITN-2018 call, under the Grant Agreement no 813278.

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