The proliferation of mobile terminals, such as smart phones and tablets, leverages spatial crowdsouring applications with the participatory mechanism of human power. The idea is to crowdsource spatial information in various scenarios, where traditional location-based sensing techniques fall short. Also, human participants have overwhelming advantages over mobile devices/machines in complex data processing, such as analyzing, summarizing, relevance testing, etc. It could be used for advanced applications, such as contagious disease tracking, traffic monitoring, and environmental parameter recording, by utilizing geo-tagged information collected with human power. The challenges arise with the emerging application is on how to integrate the human efforts with the start-of-art spatial database techniques. The purpose of the topic is therefore to develop an integrated spatial crowdsoucing platform, which bridges the gap between human and machine computing to support advanced applications, such as querying and analyzing spatial data. In particular, a novel data modeling method is demanded for the man-machine collaboration. Then, we need to design an intelligent optimizer to best utilize the human and machine efforts. We also have to consider the quality of data contributed by volunteered participants in order to provide the crowdsourced result with guaranteed accuracy.

Main Advisor at Aalborg Universitet (AAU)
Co‐advisor at Université Libre de Bruxelles (ULB)