Task-oriented data collection strategy in vehicular crowdsensing
Yanglong Sun, Yuliang Tang · 2019
Data collection is the base of implementing some attractive novel applications in vehicular networks, such as real-time map updating and dynamic navigation. Crowdsensing is a good way to deploy such data collection task among a huge amount of vehicles to solve the vehicle selection problem. However, uploading sensing data is still challenged due to the high cost and shortage of communication resource. This paper explored the uploading strategy for vehicular corwdsensing, where the sensing task contains several subtasks that occupied by vehicles and needs to be completed in a limited time. A comprehensive cost model was designed and we utilized a greedy algorithm to find data giver vehicles for every subtask with minimized cost. Finally, we analyzed the influence of vehicle's communication capacity and different cost structure.