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Human mobility is an important key to many promising applications especially for the sectors of smart cities and smart environments. As advanced sensing, and computing, communication technologies are rapidly developed in the past decades, locations of crowds can be collected and further analyzed to boost these applications. Typical mobility analytics highly relies on location information such as outdoor GPS coordinates, indoor relative coordinates, or coordinates on images. However, outdoor absolute or indoor relative location information is not always available in many places, and taking images of people compromises personal privacy that is restricted in many countries. Therefore, to understand crowd mobility, social relationships among them, and their social behavior, this talk will review location-less crowd mobility analytics based on proximity sensing technology to estimate crowd sizes, infer mobility groups, monitor passenger flows, and detect human queues in crowds. The key idea is to extract important features behind wireless packets of mobile devices and correlate them to crowd mobility behavior. To verify the feasibility of the proposed location-less approaches, we conduct experiments in both indoor and outdoor places. Comprehensive experiments are conducted in indoor environments to verify the detection results of human queues and mobility groups. Moving toward more dynamic and uncertain outdoor places, we launched large-scale pilot studies in the Wellington Railway Station and Re:START Mall in New Zealand to showcase the crowd estimation results. In addition, we conducted experiments on the hanging railway system in TU Dortmund to showcase passenger flow mentoring.
Dr. Fang-Jing Wu is a junior professor at TU Dortmund. Prior to TU Dortmund, she was a research scientist at Cloud Service and Smart Things Group, NEC Laboratories Europe from 2016 to 2017. Prior to NEC Labs, she was a scientist at the Institute of Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore from 2013 to 2015. Before joining A*STAR, she was a research fellow at Nanyang Technological University in 2012. She was awarded a Ph.D. degree in Computer Science from the National Chiao Tung University in 2011. She was a visiting researcher at Imperial College London from 2010 to 2011. Her current research interests are primarily in pervasive computing, wireless sensor networks, wireless communications and networks, cyber-physical systems, mobile crowdsourcing, mobile computing, wearable sensing, and Internet-of-Things.