Large Scale Active Vehicular Crowdsensing

Crowd sensing, the use of everyday devices to collect and share data is paving the way for cost efficient real time data collection. Real time information can be rapidly collected and shared publicly using smart devices. Besides smart phones, smart vehicles have also shown great promise for crowd sensing. In contrast to mobile crowd sensing, vehicles possess powerful on board sensors, powerful processing ability, and greater mobility. In this paper, we propose an active crowd sensing system to improve sensor data coverage. Unlike traditional approaches, we modify the planned route of participants rather than passively utilizing existing routes. To solve this problem, our system consist of two algorithms; a distributed route generator algorithm based on partial information and a centralized route selection algorithm with full information. In it, each vehicle has the responsibility of generating multiple routes while the central server determines which route each participant should undertake. Through the use of SUMO simulation and TAPAS Cologne Large Scale Mobility Dataset, we show that our proposed approach delivers significant performance improvements compared to traditional approaches.