Using crowdsourced data, the operators can cut down on the cost for their currently expensive data collection investments compared to traditional methods.
An interactive 3-D web-based map solution visualizes network traffic. These state-of-the-art web components optimize the end-user experience. Extra layers showing site details and demographic data are added to the solution to help the end-user make correction decisions.
The crowdsourced data is pre-processed and fed into an AI engine with both geographical clustering and supervised learning capabilities. It is used to identify hotspots, trace traffic patterns, and facilitate fault detection in operations.
Crowdsourced data is collected when/where/how the user is using the network. This means that the collected data can reveal rush hours, high intense areas, as well as technologies used in the network.