With the rapid development of 5G mobile networks, it＇s urgent network to identify the service types of users to allocate resources intelligently. We present SensingGO, a Mobile Crowd Sensing (MCS) system which encourages people to keep sensing large-scale network data by integrating incentive mechanisms. We proposed a system architecture to apply cellular information of user to identify user＇s mobility type. We can achieve 96% identification accuracy and reduce 37% power consumption.

We present SensingGO, a system which encourages people to keep sensing large-scale data by integrating incentive mechanisms.
For user mobility identification, we are the first to integrate cellular data and location information for machine learning technique. Around 500-hours dataset (SensingGO) and other dataset in the world, we can achieve 96% identification accuracy and reduce 37% power consumption.

This technology is mainly designed for 5G mobile network to identify user’s mobility type and allocate specific network resources intelligently. Other applications like smart navigation, carbon footprint, and elderly tracking for human. Usage based pricing, driving behavior analysis, congestion control, traffic planning, or travel time prediction can be used for smart city.