Abstract: Crowd Sensing and Computing (CSC) is a new computing paradigm in smart cities, where a large group of individuals having mobile devices collectively share data and extract information or knowledge. CSC is cheaper than traditional infrastructure-based urban sensing and computing approaches, but it is still very EXPENSIVE in large-scale sensing tasks, and its cost ranges from direct ones such as incentive payment and task/app development cost, to other indirect ones such as energy consumption and privacy risk. On the other hand, the organizers of CSC tasks expect that the sensing quality/outcome to be as GOOD as possible (e.g., maximize spatial-temporal coverage, or minimize mean sensing error). Thus, how to balance the COST and QUALITY becomes a fundamental research challenge in CSC. In this talk, I will introduce the relevant techniques to tackle this challenge from three perspectives: task creation, task assignment, and data integration. Finally, I will present my visions on future research by combining both CSC and AI in the public health monitoring.
Bio: Jiangtao Wang is currently an Assistant Professor (called “Lecturer” in UK) with School of Computing & Communications, Lancaster University (Top 10 University in UK). His research interests include Mobile Crowd Sensing, Crowdsourcing, Internet of Things, Health Data Analytics, and so forth. He has published around 50 scientific papers in refereed journals and conferences, e.g., CSCW, AAAI, IJCAI, ICDM, IEEE Trans. on Mobile Computing, IEEE Trans. on Human-Machine Systems, IEEE Communications, IEEE COMPUTER, IEEE Wireless Communications, IEEE Internet of Things Journal, etc.
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