Spatial Task Assignment for Crowd Sensing with Cloaked Locations

Distributed mobile crowd sensing is becoming a valuable paradigm, enabling a variety of novel applications built on mobile networks and smart devices. However, this trend brings several challenges, including the need for crowd sourcing platforms to manage interactions between applications and the crowd (participants or workers). One of the key functions of such platforms is spatial task assignment which assigns sensing tasks to participants based on their locations. Task assignment becomes critical when participants are hesitant to share their locations due to privacy concerns. In this paper, we examine the problem of spatial task assignment in crowd sensing when participants utilize spatial cloaking to obfuscate their locations. We investigate methods for assigning sensing tasks to participants, efficiently managing location uncertainty and resource constraints. We propose a novel two-stage optimization approach which consists of global optimization using cloaked locations followed by a local optimization using participants' precise locations without breaching privacy. Experimental results using both synthetic and real data show that our methods achieve high sensing coverage with low cost using cloaked locations.