Mobile data mining and applications

• 作者： Jiang, Hao, author.
• 其他作者：
  • Chen, Qimei, author.
  • Zeng, Yuanyuan, author.
  • Li, Deshi, author.
  • SpringerLink (Online service)
• 其他題名：
  • Information fusion and data science.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Information fusion and data science,
• 主題： Data mining. , Wireless and Mobile Communication. , Data-driven Science, Modeling and Theory Building. , Data Mining and Knowledge Discovery. , Communications Engineering, Networks. , Mobile Computing. , Urban Geography / Urbanism (inc. megacities, cities, towns)
• ISBN： 9783030165031 (electronic bk.) 、 9783030165024 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter1: Introduction -- Chapter2: Mobile Data Processing and Feature Discovery -- Chapter3: Mobile Data Application in Wireless Communication -- Chapter4: Mobile Data Application in Mobile Network -- Chapter5: Mobile Data Application in Smart City -- Chapter6: Conclusion, Remarks and Future Directions.
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• 摘要註: This book focuses on mobile data and its applications in the wireless networks of the future. Several topics form the basis of discussion, from a mobile data mining platform for collecting mobile data, to mobile data processing, and mobile feature discovery. Usage of mobile data mining is addressed in the context of three applications: wireless communication optimization, applications of mobile data mining on the cellular networks of the future, and how mobile data shapes future cities. In the discussion of wireless communication optimization, both licensed and unlicensed spectra are exploited. Advanced topics include mobile offloading, resource sharing, user association, network selection and network coexistence. Mathematical tools, such as traditional convexappl/non-convex, stochastic processing and game theory are used to find objective solutions. Discussion of the applications of mobile data mining to cellular networks of the future includes topics such as green communication networks, 5G networks, and studies of the problems of cell zooming, power control, sleep/wake, and energy saving. The discussion of mobile data mining in the context of smart cities of the future covers applications in urban planning and environmental monitoring: the technologies of deep learning, neural networks, complex networks, and network embedded data mining. Mobile Data Mining and Applications will be of interest to wireless operators, companies, governments as well as interested end users.
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