Defense Date


Document Type


Degree Name

Doctor of Philosophy


Computer Science

First Advisor

Dr. Eyuphan Bulut


Mobile crowdsensing (MCS) is an emerging form of crowdsourcing, which facilitates the sensing data collection with the help of mobile participants (workers). A central problem in MCS is the assignment of sensing tasks to workers. Existing work in the field mostly seek a system-level optimization of task assignments (e.g., maximize the number of completed tasks, minimize the total distance traveled by workers) without considering individual preferences of task requesters and workers. However, users may be reluctant to participate in MCS campaigns that disregard their preferences. In this dissertation, we argue that user preferences should be a primary concern in the task assignment process for an MCS campaign to be effective, and we develop preference-aware task assignment (PTA) mechanisms for five different MCS settings. Since the PTA problem is computationally hard in most of these settings, we present efficient approximation and heuristic algorithms. Extensive simulations performed on synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms produce near-optimal solutions in terms of preference-awareness, outperforming the state-of-the-art assignment algorithms by a wide margin in most cases.


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