Statistical Machine Learning Seminar
Hosted by Department of Mathematical Sciences
The unprecedented urbanization and the staggering development of modern information technologies (IT) make Smart City attractive and achievable. Beyond the scope of traditional city services and applications, Smart Cities provide urban planners and policy makers proactively and timely information to obtain a dynamic and comprehensive understanding about the rhythm of our cities. Although Cloud Computing is considered the ideal platform for vast volume urban data storage and processing, the sustainability of Smart Cities necessitates the capability of computations and data analysis at the edge of the networks, especially for mission critical applications requiring real-time information fusion and on-site decision making. Fog Computing, an extension of Cloud Computing, enables heterogeneous mobile and smart computing devices at the edge to collaborate for instant decision making. In this talk, a smart urban surveillance platform will be introduced. Leveraging the underlay fog network, the real-time multi-target tracking task is accomplished. Compared with the Cloud Computing, the experimental results are very encouraging and validate the feasibility of smart urban surveillance for instant decision making using Fog Computing at the network edges.