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Software Engineering

Research Collection School Of Computing and Information Systems

Mobile crowdsourcing

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Artificial Intelligence and Robotics

Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau Mar 2017

Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …


Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander Nov 2014

Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander

Research Collection School Of Computing and Information Systems

We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time …