The indoor localization problem consists in identifying the Cartesian coordinates of an object or a personal asset in the buildings, malls, hospitals, campuses, factories, etc. To solve this problem, we consider a Wi-Fi-based localization method called fingerprinting, a two-step process, where a radio map of the monitored area is constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose an adapted Random Forest (RF) and Extreme Gradient Boosting (XGB) algorithms. This adaptation, combined with Minimum Mean Square Error (MMSE), improves the accuracy problem caused by the change of environment and extends the concept by adding a signal processing functionality as an edge cloud feature to address a dynamic cooperation clustering. By embedding the Wi-Fi Access Point (WAP) with multiple antennas, the signals sent by the Mobile User Equipment (MUE) can be processed to improve the accuracy of the bootstrap. Adding Minimum Mean Square Error (MMSE) is a kind of datacentric approach because it yields high-quality data as input. The noise inherent in the location data is reduced and thus the performance of the MMSE-aided RF and XGB improved. This enhancement is further extended by sharing data between WAPS. Thus, the MMSE processing and the sharing of such processed data between WAPS enhance the positioning model performance. The performance of these methods is evaluated through robust and extensive experiments in real-time indoor areas, with regular and reproducible scenarios. We found an interesting outcome that the proposed approach can offer better time-2-market compared to the traditional, non-Machine-Learning-based indoor positioning system approach.