• Muhammad Harris Hashmi
• Bhanu Priya Mahant
• Muhammad Salik Salam
• Muhammad Ahsan

IPS (Indoor Positioning System) aims to locate individuals and objects within buildings using radio waves, magnetic fields, audio signals, images, and other sensor data. Specific positioning systems, like the Received Signal Strength Indicator (RSSI), rely on the strength of radio signals from emitting devices (beacons) in the area. Wi-Fi, which can be accessed on almost any consumer device, has recently become the most popular method for indoor localization. This research looked at how to enhance collaborations between an indoor Back Propagation-based Neural Network (BPNN) and a Random Forest (RF) by integrating open-source tool & public data repository UJIIndoorLoc databases into Jupyter Notebook. This proposed structure is offered in two stages: offline & online. BPNN trains the network with data from each reference location before turning to RF to acquire an estimated position for the end user. This study presents RF-HAIL (Random Forest-based WiFi fingerprint-based indoor location method), an indoor location technique that uses WiFi fingerprints and has a high degree of versatility so that it may be used in various contexts and with a wide range of devices. In the offline phase, a radio map database is built & BPNN is trained. An unknown point is located in the online setting using the deterministic position method. The RP coordinates of an unidentified point with the highest fingerprint similarity are ones used by the RF method. According to the results of experiments, RF has a lower average localization error and a higher localization precision than the present WKNN-based HAIL approach.

Indoor Localization, WIFI Fingerprint, Machine Learning, RSSI, Random Forest, Decision Tree.

"Machine Learning-Based Indoor Localization Using Wi-Fi Fingerprints", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.c783-c790, January-2023, Available :http://www.jetir.org/papers/JETIR2301298.pdf

"Machine Learning-Based Indoor Localization Using Wi-Fi Fingerprints", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppc783-c790, January-2023, Available at : http://www.jetir.org/papers/JETIR2301298.pdf