Layla Pournajaf

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—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 crowdsourcing platforms to manage interactions between applications and the crowd (participants or workers). One of the key functions of(More)
To realize the full potential of mobile crowd sensing, techniques are needed to deal with uncertainty in participant locations and trajectories. We propose a novel model for spatial task assignment in mobile crowd sensing that uses a dynamic and adaptive data driven scheme to assign moving participants with uncertain trajectories to sensing tasks, in a(More)
In this paper, we present an overview of our ongoing project PREDICT (Privacy and secuRity Enhancing Dynamic Information Collection and moniToring). The overall aim of the project is to develop a framework with algorithms and mechanisms for privacy and security enhanced dynamic data collection, aggregation, and analysis with feedback loops. We discuss each(More)
We propose to demonstrate STAC, a tool for spatial task assignment with cloaked locations in crowd sensing applications. The need for systems such as STAC becomes critical when participants of crowd sensing applications hesitate to share their locations due to privacy concerns. In such applications, STAC enables effective task assignment capabilities(More)
Mobile crowd sensing enables a broad range of novel applications by leveraging mobile devices and smartphone users worldwide. While this paradigm is immensely useful, it involves the collection of detailed information from sensors and their carriers (i.e. participants) during task management processes including participant recruitment and task distribution.(More)
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