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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 services Presents a fog-enabled service architecture with
detailed technical approaches for realistic cross-domain
application scenarios with limited prior domain knowledge Outlines
a series of data-driven self-learning applications (with new
algorithms) in different industrial sectors and public services
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 services Presents a fog-enabled service architecture with
detailed technical approaches for realistic cross-domain
application scenarios with limited prior domain knowledge Outlines
a series of data-driven self-learning applications (with new
algorithms) in different industrial sectors and public services
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