Location-Aware Worker Selection for Mobile Opportunistic Crowdsensing in VANETs
Yifan Xu, Jun Tao, Yang Gao, Li Zeng · 2017
Worker selection for location-based crowdsensing can be described as the strategy of choosing the proper cooperative participants to complete the allocated tasks in specified regions. Due to the mobility pattern of vehicles and regular road networks, the Vehicular Ad-hoc Networks (VANETs) are expected to provide many opportunities for task execution in opportunistic crowdsensing, enabling some emerging applications. To fulfill tasks with the least execution time under the spatial-temporal restrictions, we propose a Location-Aware Worker Selection scheme (LAWS) for mobile opportunistic crowdsensing in urban areas. Different from the traditional worker selection schemes assigning a task to one designated worker, LAWS exploits the vehicles contacts provided by taxicabs and buses and makes full advantage of prior knowledge of vehicles to promote the performance of task execution. Real-world vehicle traces are introduced to construct the extensive simulations. The simulation results show that our scheme outperforms the well-known algorithms, e.g., Epidemic, Prophet, in terms of the task execution success ratio, the execution time and the network load.