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dc.contributor.advisorCurry, Edward
dc.contributor.authorHassan, Umair ul
dc.date.accessioned2016-09-22T12:52:41Z
dc.date.available2016-09-22T12:52:41Z
dc.date.issued2016-08
dc.identifier.urihttp://hdl.handle.net/10379/6035
dc.description.abstractSpatial crowdsourcing has emerged as a new paradigm for solving difficult problems in the physical world. It engages a large number of human workers in scenarios such as crisis management and smart cities. The utility of spatial crowdsourcing is generally dependent on who performs the task. On one hand, the workers may not perform assigned tasks within time. On the other hand, the performed tasks may not meet the prede ned quality criteria. In the case of spatial crowdsourcing, the success of an assignment depends on factors such as task location, travel distance, or expected reward. This necessitates an appropriate task assignment process to optimize the utility of spatial crowdsourcing. The design of the task assignment process faces three primary research challenges: dynamism, heterogeneity, and uncertainty. The goal of this thesis is to address these research challenges; therefore, it proposes a conceptual framework to analyze the dimensions of dynamic task assignment in spatial crowdsourcing. To formalize the research problem, this thesis de nes dynamic task assignment as a repeated decision-making problem under uncertainty and heterogeneity. Uncertainty de nes the limited knowledge about the reliability of workers for assigned tasks, and heterogeneity characterizes the di erences in reliability and expertise of workers. The proposed formalization is referred to as the adaptive assignment problem in spatial crowdsourcing. The adaptive assignment problem combines online optimization with heuristic-based learning for balancing the exploration-exploitation trade-o during repeated assignments. The problem is instantiated in four di erent scenarios of spatial crowdsourcing to highlight speci c requirements of assignment algorithms. An agent-based simulation methodology is employed to evaluate the algorithms proposed for each scenario. Empirical evaluation provides evidence of the performance of algorithms using synthetic and real-world data.en_IE
dc.subjectSpatial crowdsourcingen_IE
dc.subjectCrowdsourcingen_IE
dc.subjectTask assignmenten_IE
dc.subjectOnline algorithmsen_IE
dc.subjectMulti-armed banditen_IE
dc.subjectCombinatorial banditsen_IE
dc.subjectFractional optimizationen_IE
dc.subjectlocation diversityen_IE
dc.subjectLocation diversityen_IE
dc.subjectAgent-based simulationen_IE
dc.subjectData analyticsen_IE
dc.titleAdaptive task assignment in spatial crowdsourcingen_IE
dc.typeThesisen_IE
dc.local.noteSpatial crowdsourcing potentially employs a large number of workers performing tasks in the physical world such as taking photos in a disaster-hit region. This work investigates a novel approach to optimize the utility of spatial crowdsourcing by the automatic assignment of tasks to reliable workers.en_IE
dc.local.finalYesen_IE
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