Multi-Task oriented data diffusion and transmission paradigm in crowdsensing based on city public traffic

As mobile smart devices become increasingly popular and are equipped with increasingly powerful sensors, they have been pervasively applied in crowdsensing as effective tools to solve large-scale sensing tasks in urban areas. Task requesters can allocate sensing tasks to mobile nodes through a crowdsensing platform, eliminating the cost of deploying and maintaining large numbers of fixed sensors. However, several kinds of crowdsensing tasks (e.g., audio and video transmission) that generate large-scale sensed data may bring high network traffic costs to participants using a 3G/4G network, which may affect their satisfaction. In this paper, we build a data diffusion and transmission paradigm in crowdsensing based on City Public Traffic System (PTS), and thoroughly discuss a paradigm for Multi-Task diffusion and transmission within budget constraints. This paradigm makes full use of the advantages of a bus in PTS to realize the rapid transmission of large-scale sensed data: predictable trajectory, wide coverage area, fast moving speed and long contact duration among passengers. Further, we also propose a new data transmission algorithm called BUI-BA that chooses mobile nodes to transfer data by maximizing the transmission utility increment. The experimental results demonstrate that BUI-BA has better overall performance than widely used Greedy and effSense, achieving a tradeoff between overall transmission utility and transmission redundancy.