BACKGROUND

Mobile devices such as cellular telephones, PDAs, etc. are proliferating like never before. Users may install applications to access several different provider networks and can access voice, text, and multimedia data from other network entities such as servers and other mobile devices.

These mobile devices additionally include Global Positioning System (GPS) receivers, which provides for a host of location-based services (LBS). Location estimation of mobile devices is important for obtaining location tagged network failure data for system optimization, location-based services, 911 services, and a variety of other location enhanced applications.

This background information is provided to reveal information believed by the applicant to be of possible relevance. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art.

SUMMARY

Crowd sourced data from mobile devices may be used to estimate site locations. In addition, machine learning models may be used to filter out any inaccurate crowd sourced data before using algorithms to estimate the cell site location.

In an example, an apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include receiving data associated with a location of plurality of devices that have connected with a base station in a geographic area; determining a machine learning model to apply to the received data based on the type of data, wherein the machine learning model is a clustering model; performing the clustering model on the received data; based on the performing the clustering model on the received data, obtaining representative data that excludes outliers in the received data; based on the representative data, determining a location of the base station in the geographic area; and sending a message with the location of the base station.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to limitations that solve any or all disadvantages noted in any part of this disclosure.

