SpatialRecruiter: Maximizing Sensing Coverage in Selecting Workers for Spatial Crowdsourcing

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, the authors investigate the integration of these two paradigms. The authors notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, the authors propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. The authors propose two coverage estimation functions to compute the coverage potential of a worker. Then, the authors design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms.


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  • Accession Number: 01645102
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 29 2017 11:58AM