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Daqing Zhang
Researcher at Peking University
Publications - 355
Citations - 20924
Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topics: Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publications receiving 16675 citations. Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.
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Proceedings ArticleDOI
PSAllocator: Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints
TL;DR: The proposed PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform and employs an iterative greedy process to optimize the task allocation.
Journal ArticleDOI
NextCell: Predicting Location Using Social Interplay from Cell Phone Traces
TL;DR: NextCell-a novel algorithm that aims to enhance the location prediction by harnessing the social interplay revealed in cellular call records and achieves higher precision and recall than the state-of-the-art schemes at cell tower level in the forthcoming one to six hours.
Journal ArticleDOI
TaskMe: toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing
TL;DR: A novel MCS incentive mechanism called TaskMe is proposed, an LBSN (location-based social network)-powered model is leveraged for dynamic budgeting and proper worker selection, and a combination of multi-facet quality measurements and a multi-payment-enhanced reverse auction scheme are used to improve sensing quality.
Proceedings ArticleDOI
Differential Location Privacy for Sparse Mobile Crowdsensing
TL;DR: E-differential-privacy is adopted in Sparse MCS to provide a theoretical guarantee for participants' location privacy regardless of an adversary's prior knowledge and to reduce the data quality loss caused by differential location obfuscation, a privacypreserving framework with three components.
Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces
TL;DR: In this paper, the authors focus on the problem of detecting anomalous routes by comparing against historical "normal" routes, and propose a real-time method, iBOAT, that is able to detect anomalous trajectories "on-the-fly" and identify which parts of the trajectory are responsible for its anomalousness.