MOBILE BIG DATA FOR URBAN ANALYTICS

作     者：Hui, Pan Li, Yong Ott, Joerg Uhlig, Steve Han, Bo Tan, Kun 

作者机构：Univ Helsinki Data Sci Helsinki Finland Univ Helsinki Comp Sci Helsinki Finland Hong Kong Univ Sci & Technol Comp Sci & Engn Dept HKUST DT Syst & Media Lab SyMLab Hong Kong Hong Kong Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tech Univ Munich Connected Mobil Munich Germany IRTF DTN Res Grp Honolulu HI USA IEEE Comsoc TCCC Editorial Board Elsevier COMCOM Waikoloa HI USA Queen Mary Univ London Sch Elect Engn & Comp Sci Networks London England AT&T Laos Res Bedminster NJ USA Cent Software Inst Leuven Belgium Huawei Technol Cloud Networking Lab Software Innovat & Dev Luawers Cloud Leuven Belgium 

出 版 物：《IEEE COMMUNICATIONS MAGAZINE》 (IEEE通讯杂志)

年 卷 期：2018年第56卷第11期

页      面：12-12页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：Electronic Engineering  Tsinghua University  Beijing  China. He received his Ph.D. degree in electronic engineering from Tsinghua University in 2012. He has served as General Chair  TPC Chair  and TPC member for several international workshops and conferences. His papers have more than 4600 citations. Among them  10 are ESI Highly Cited Papers in Computer Science  and four received conference Best Paper (runner-up) Awards. He received the IEEE 2016 ComSoc Asia-Pacific Outstanding Young Researchers award and the Young Talent Program of the China Association for Science and Technology award 

主　　题：Special issues and sections Big Data Urban areas Internet of Things Smart cities Environmental factors Social network services Green design 

摘      要：The rapid progress of urbanization not only enables higher populations living in modern cities, but also engenders many “urban diseases, such as air pollution, traffic congestion, and increased energy consumption. Mobile big data, with advanced communication and information technologies, can be collected through a variety of data sources, including cellular networks, the Internet of Things (IoT), social networks, and so on. This allows detailed analysis on the moving patterns of citizens and behaviors of devices deployed in urban areas, which helps grasp the outlines and the inner flows of cities. Based on those observations, prediction of events and their involved crowds and departments in cities can lead the city to become more intelligent and greener. The articles in this special section offer a comprehensive overview of the state-of-the-art developments in technology, application, and theory for mobile big data for urban analytics.