Remote Cloud or Local Crowd: Communicating and Sharing the Crowdsensing Data

With an increase in the number of mobile applications, the development of mobile crowdsensing systems has recently attracted a significant attention in both academic researchers and industries. In mobile crowdsensing system, the remote cloud (or back-end server) harvests all the crowdsensing data from the mobile devices, and the crowdsensing data can be uploaded immediately via 3G/4G. To reduce the cost and energy consumption, many academic researchers and industries investigate the way of mobile data offloading. Due to the sparse distribution of the WiFi APs, the crowdsensing data is often delayed to offloading. In this paper, compared with offloading data via WiFi APs, we investigate the communication and sharing of crowdsensing data by vehicles near the event (such as a pathole on the road), termed as a local crowd. The crowd-based approach has a lower delay than the offloading-based approach, by considering the quality of truth discovery. We define an utility function related to the crowdsensing data shared by the local crowd, in order to quantify the trade-off between the quality of the truth discovery and the user satisfaction. Our extensional simulations verify the effectiveness of our proposed schemes.