Privacy-aware Coordinated Spatial Task Assignment Sample Clauses

Privacy-aware Coordinated Spatial Task Assignment. (Chapter 3) In this chapter, we consider the spatial task assignment problem in a coordinated crowd sensing setting. We assume a tasking server is responsible for managing sensing tasks among participants who share their cloaked locations rather than their exact locations. Our goal is to efficiently assign sensing tasks to participants based on their cloaked locations to achieve a desired coverage objective with minimized cost (i.e. the total distance that participants have to travel for their assigned tasks). To this end, we propose a novel approach for a privacy-aware coordinated spatial task assignment with the following contributions. First, we propose a two-stage optimization approach for the spatial task assignment problem in the presence of cloaked locations. In the first stage, a global optimization problem is solved at the task assignment server using cloaked locations. Our approach addresses location uncertainty and can work with different spatial cloaking methods. In the second stage, participants individually fine-tune their assignments using their own exact locations. We formulate formal optimization objectives for each stage and further show the optimization problems at each stage are NP-hard. Second, we propose efficient greedy algorithms to solve the optimization problem at each stage. Finally, we implement our approach as a software and demonstrate, from both synthetic and real data, that our methods achieve high sensing coverage with low cost using cloaked participant locations.