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