Abstract
Urban computing utilizes unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to create win-win-win solutions which intelligently improve people’s lives, urban environments, and city operation systems. With the help of cloud computing, the Internet of Things, device-to-device (D2D) communication, artificial intelligence (AI), big data, and urban computing and intelligence will bridge the gap of ubiquitous sensing, intelligent computing, cooperative communication, and mass data management technologies, to create novel solutions that improve urban environments, human life quality, and smart city systems. Thus, urban computing and intelligence has recently attracted significant attention from industry and academia for building smart cities.
Highlights
Urban computing utilizes unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to create win-winwin solutions which intelligently improve people’s lives, urban environments, and city operation systems
With the help of cloud computing, the Internet of Things, device-todevice (D2D) communication, artificial intelligence (AI), big data, and urban computing and intelligence will bridge the gap of ubiquitous sensing, intelligent computing, cooperative communication, and mass data management technologies, to create novel solutions that improve urban environments, human life quality, and smart city systems
Recent advances in artificial intelligence (AI), cloud/fog/edge computing, big data, and novel communication techniques show that urban computing and intelligence still struggles with fundamental, long-standing problems
Summary
Urban computing utilizes unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to create win-winwin solutions which intelligently improve people’s lives, urban environments, and city operation systems. The article ‘‘Anomaly detection approach for urban sensing based on credibility and time-series analysis optimization model,’’ by Zhang and Li, proposes an anomaly detection method for urban sensing based on sequential data and credibility.
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