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• W2892657539 abstract "The Wi-Fi-based indoor positioning system compares the online signal strength indicator (RSSI) and offline stored fingerprints to find the closest match to estimate the target location of the device. However, the problem is that the process of collecting fingerprints is very laborious, time-consuming and expensive. It is challenging to solve this problem. We proposed a method to reduce fingerprint collection based on APC (affinity propagation clustering) and WKNN (Weighted K-Nearest Neighbor) interpolation algorithm to solve the problem in this paper. We use the APC algorithm to break the known RPs (Reference points) into several clusters. The cluster representative is recorded. Then, we classify the unknown RPs as in the cluster which is physically nearest to cluster representative. Finally, in each cluster, we use fingerprints of known RPs with the WKNN interpolation algorithm to calculate fingerprints of the unknown RPs. We do experiments in a real environment. The results of real environmental experiments show that our method only needs 40% of the fingerprint to restore all the fingerprints in our environment, while the average localization accuracy only lost 8%. It only needs 70% of the fingerprints to restore all the fingerprints almost without any loss of average localization accuracy in our environment." @default.
• W2892657539 created "2018-10-05" @default.
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• W2892657539 date "2018-05-01" @default.
• W2892657539 modified "2023-09-23" @default.
• W2892657539 title "Reducing Wi-Fi Fingerprint Collection Based on Affinity Propagation Clustering and WKNN Interpolation Algorithm" @default.
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• W2892657539 doi "https://doi.org/10.1109/imcec.2018.8469697" @default.
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