PTDU: Public transit system based framework of data upload in urban crowd sensing

How to achieve effective data transmission between different participators based on their opportunistic communication is always a hot research topic in Crowd sensing (CS). Public transit system(PTS), as a city infrastructure, has several obvious advantages, such as predictable bus mobility, large coverage area, and stable interaction time to deliver non-real time information. In this paper, we propose a new framework to improve the performance of data delivery by PTS. Firstly, several bus stops are selected to set WIFI access point (AP) according to their coverage utility values. Secondly, the trip prediction tables (TPT) of participators can be established in this framework on the basis of the records collected when the participators get on and off the buses. When a passenger gets on a bus, his probable route can be speculated out based on TPT. Thirdly, before being transferred, every task is marked with a task title, which includes an objective address, data size, and maybe the reward etc. All the potential users can receive the task title and decide whether to participate in the task, and then according to TPT, the best suitable participators can be chosen out to relay the data. At last, we adopt two uploading schemes: Directly Waiting for Upload scheme (DWU) and Prediction and Context based Greedy Uploading scheme (PCGU). The results show that averagely, PCGU consumes 68.5 minutes which is 81% less than the scheduled time, while DWU consumes 49.5 minutes which is 86.3 % less than the scheduled time.