Yang Yang
Fog-Enabled Intelligent IoT Systems
Buch
This book first provides a comprehensive review of state-of-the-art IoT technologies and applications in different industrial sectors and public services. The authors give in-depth analyses of fog computing architecture and key technologies that fulfill the challenging requirements of enabling computing services anywhere along the cloud-to-thing continuum. Further, in order to make IoT systems more intelligent and more efficient, a fog-enabled service architecture is proposed to address the latency requirements, bandwidth limitations, and computing power issues in realistic cross-domain application scenarios with limited priori domain knowled…
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Beschreibung
This book first provides a comprehensive review of state-of-the-art IoT technologies and applications in different industrial sectors and public services. The authors give in-depth analyses of fog computing architecture and key technologies that fulfill the challenging requirements of enabling computing services anywhere along the cloud-to-thing continuum. Further, in order to make IoT systems more intelligent and more efficient, a fog-enabled service architecture is proposed to address the latency requirements, bandwidth limitations, and computing power issues in realistic cross-domain application scenarios with limited priori domain knowledge, i.e. physical laws, system statuses, operation principles and execution rules. Based on this fog-enabled architecture, a series of data-driven self-learning applications in different industrial sectors and public services are investigated and discussed, such as robot SLAM and formation control, wireless network self-optimization, intelligent transportation system, smart home and user behavior recognition. Finally, the advantages and future directions of fog-enabled intelligent IoT systems are summarized. Provides a comprehensive review of state-of-the-art IoT technologies and applications in different industrial sectors and public servicesPresents a fog-enabled service architecture with detailed technical approaches for realistic cross-domain application scenarios with limited prior domain knowledgeOutlines a series of data-driven self-learning applications (with new algorithms) in different industrial sectors and public services
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Produktdetails
Weitere Autoren: Zhou, Ming-Tuo / Chu, Xiaoli / Luo, Xiliang
- ISBN: 978-3-030-23187-3
- EAN: 9783030231873
- Produktnummer: 35022390
- Verlag: Springer International Publishing
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 236 S.
- Masse: H23.5 cm x B15.5 cm x D1.2 cm 365 g
- Auflage: 1st ed. 2020
- Abbildungen: Paperback
- Gewicht: 365
Über den Autor
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