PhD Dissertation Proposal by HAN Chung Kyun | Efficient Algorithms for Trajectory-Aware Mobile Crowdsourcing

Mobile crowdsourcing, a subclass of crowdsourcing dealing with location-specific tasks, is prevalent in our daily life. From sensing urban environment such as noise, air pollution to package delivery, various location-specific tasks are posted on mobile crowdsourcing platforms to tap on the pool of crowdsourced workers. In addition to ride-hailing apps, many mobile crowdsourcing platforms have been sprung up everywhere and disappeared inattentively. One of the potential reasons for the failure is the ignorance of the mobility patterns of crowdsourced workers and the lack of personalization.

This thesis touches various optimization problems of mobile crowdsourcing and, especially, focuses on the mobility of the worker. Depending on the context of a problem, the different level of personalization is considered. All targeted problems are formally described by a mathematical model which adequately handles the uncertainty of the mobility patterns with an objective function or constraints. For large-scale problem instances, it is necessary to develop efficient algorithms based on the deeper understanding and the structure of the problems. By adequate decomposition technique, the proposed algorithms efficiently solve the problems and also take advantage of parallelization. Lastly, the performance of the algorithms are validated on practical settings and, selectively, results of sensitivity analyses are also provided.

HAN Chung Kyun is a PhD candidate in School of Information Systems, specializing in Intelligent Systems & Optimization (IS&O) under the supervision of Associate Professor CHENG Shih-Fen. He is interested in advanced optimization techniques and data analysis. His current research focuses on solving optimization problems of mobile crowdsourcing.