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First, RSSI (Received Signal Strength Indicator) data were collected using the fingerprint method. Then, Kalman Filter and Savitzky Golay Filter are used in a hybrid manner to reduce the noise on the obtained signal data and make it more stable. In the first part, using the collected data from Wi-Fi and Beacon devices, the Non-linear least squares method (NLLS), including Levenberg-Marquardt (LM), is used for indoor tracking. In the second part, a fingerprinting-based approach is tested. K Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms estimate the area where the client is located. Each algorithm\u2019s accuracy rate are calculated on different training and test data and presented.<\/jats:p>","DOI":"10.3233\/ais-220484","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T12:03:59Z","timestamp":1678795439000},"page":"63-87","source":"Crossref","is-referenced-by-count":9,"title":["Hybrid indoor positioning for smart homes using WiFi and Bluetooth low energy technologies"],"prefix":"10.1177","volume":"15","author":[{"given":"Yunus","family":"Haznedar","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Istanbul University-Cerrahpasa, Avcilar, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. 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