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The development of smart cities is one of the most important
challenges over the next few decades. Governments and companies are
leveraging billions of dollars in public and private funds for
smart cities. Next generation smart cities are heavily dependent on
distributed smart sensing systems and devices to monitor the urban
infrastructure. The smart sensor networks serve as autonomous
intelligent nodes to measure a variety of physical or environmental
parameters. They should react in time, establish automated control,
and collect information for intelligent decision-making. In this
context, one of the major tasks is to develop advanced frameworks
for the interpretation of the huge amount of information provided
by the emerging testing and monitoring systems. Data Analytics for
Smart Cities brings together some of the most exciting new
developments in the area of integrating advanced data analytics
systems into smart cities along with complementary technological
paradigms such as cloud computing and Internet of Things (IoT). The
book serves as a reference for researchers and engineers in domains
of advanced computation, optimization, and data mining for smart
civil infrastructure condition assessment, dynamic visualization,
intelligent transportation systems (ITS), cyber-physical systems,
and smart construction technologies. The chapters are presented in
a hands-on manner to facilitate researchers in tackling
applications. Arguably, data analytics technologies play a key role
in tackling the challenge of creating smart cities. Data analytics
applications involve collecting, integrating, and preparing time-
and space-dependent data produced by sensors, complex engineered
systems, and physical assets, followed by developing and testing
analytical models to verify the accuracy of results. This book
covers this multidisciplinary field and examines multiple paradigms
such as machine learning, pattern recognition, statistics,
intelligent databases, knowledge acquisition, data visualization,
high performance computing, and expert systems. The book explores
new territory by discussing the cutting-edge concept of Big Data
analytics for interpreting massive amounts of data in smart city
applications.
The development of smart cities is one of the most important
challenges over the next few decades. Governments and companies are
leveraging billions of dollars in public and private funds for
smart cities. Next generation smart cities are heavily dependent on
distributed smart sensing systems and devices to monitor the urban
infrastructure. The smart sensor networks serve as autonomous
intelligent nodes to measure a variety of physical or environmental
parameters. They should react in time, establish automated control,
and collect information for intelligent decision-making. In this
context, one of the major tasks is to develop advanced frameworks
for the interpretation of the huge amount of information provided
by the emerging testing and monitoring systems. Data Analytics for
Smart Cities brings together some of the most exciting new
developments in the area of integrating advanced data analytics
systems into smart cities along with complementary technological
paradigms such as cloud computing and Internet of Things (IoT). The
book serves as a reference for researchers and engineers in domains
of advanced computation, optimization, and data mining for smart
civil infrastructure condition assessment, dynamic visualization,
intelligent transportation systems (ITS), cyber-physical systems,
and smart construction technologies. The chapters are presented in
a hands-on manner to facilitate researchers in tackling
applications. Arguably, data analytics technologies play a key role
in tackling the challenge of creating smart cities. Data analytics
applications involve collecting, integrating, and preparing time-
and space-dependent data produced by sensors, complex engineered
systems, and physical assets, followed by developing and testing
analytical models to verify the accuracy of results. This book
covers this multidisciplinary field and examines multiple paradigms
such as machine learning, pattern recognition, statistics,
intelligent databases, knowledge acquisition, data visualization,
high performance computing, and expert systems. The book explores
new territory by discussing the cutting-edge concept of Big Data
analytics for interpreting massive amounts of data in smart city
applications.
The Rise of Smart Cities: Advanced Structural Sensing and
Monitoring Systems provides engineers and researchers with a guide
to the latest breakthroughs in the deployment of smart sensing and
monitoring technologies. The book introduces readers to the latest
innovations in the area of smart infrastructure-enabling
technologies and how they can be integrated into the planning and
design of smart cities. With this book in hand, readers will find a
valuable reference in terms of civil infrastructure health
monitoring, advanced sensor network architectures, smart sensing
materials, multifunctional material and structures,
crowdsourced/social sensing, remote sensing and aerial sensing, and
advanced computation in sensor networks.
The Cognitive Approach in Cloud Computing and Internet of Things
Technologies for Surveillance Tracking Systems discusses the
recent, rapid development of Internet of things (IoT) and its focus
on research in smart cities, especially on surveillance tracking
systems in which computing devices are widely distributed and huge
amounts of dynamic real-time data are collected and processed.
Efficient surveillance tracking systems in the Big Data era require
the capability of quickly abstracting useful information from the
increasing amounts of data. Real-time information fusion is
imperative and part of the challenge to mission critical
surveillance tasks for various applications. This book presents all
of these concepts, with a goal of creating automated IT systems
that are capable of resolving problems without demanding human aid.
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