Intelligent Environmental Data Monitoring For Pollution Management

Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. Introduces novel intelligent techniques needed to address environmental pollution for the well-being of the global environment Offers perspectives on the design, development and commissioning of intelligent applications Provides reviews on the latest intelligent technologies and algorithms related to state-of-the-art methodologies surrounding the monitoring and mitigation of environmental pollution Puts forth insights on future generation intelligent pollution monitoring techniques

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering. Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering

This book includes a general overview of the book series and summarizes the research results in its 13 subtopics. It systematically elaborates on how the construction and promotion of intelligent cities with Chinese characteristics could be implemented in the course of intelligent urbanization in China. Furthermore, it presents a variety of literature on urban management innovation and development, making it a valuable reference source on both the theoretic and empirical development of the new urbanization in China for intelligent-city decision-makers, c-level directors and officials in urban economy, social and environment departments and institutions all over the world.

Cognitive Models for Sustainable Environment reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent/cognitive models and algorithms to develop sustainable solutions for the mitigation of environmental pollution. It unveils intelligent and cognitive models to address issues related to the effective monitoring of environmental pollution and sustainable environmental design. As such, the book focuses on the overall well-being of the global environment for better sustenance and livelihood. The book covers novel cognitive models for effective environmental pollution data management at par with the standards laid down by the World Health Organization. Every chapter is supported by real-life case studies, illustrative examples and video demonstrations that enlighten readers. Explores the development and application of science, engineering and technology in achieving a sustainable lifestyle for humanity Provides tools, connections and proactive solutions to take sustainability programs to the next level Offers perspectives for design, development and commissioning of intelligent applications Provides reviews on the latest intelligent technologies and algorithms related to state-of-the-art methodologies of monitoring and mitigation of environmental pollution

Copper Nanostructures: Next-Generation of Agrochemicals for Sustainable Agroecosystems considers the impact of copper-based nanostructures on agri-food sectors. Sections highlight the green synthesis of copper nanoparticles, production mechanisms, eco-safety, and future perspectives, discuss the increasing importance of copper nanomaterials in plant protection applications, describe the use of copper nanostructures in plant science applications, cover antimicrobial applications, explore copper nanostructure applications, and summarize current applications in agroecosystems, such as copper nanoparticles as nanosensors, their negative ecological effects, estimation risks, and more. Assesses the impact of a large variety of copper-based nanostructures on the agri-food sector Discusses how the properties of a variety of copper-based nanomaterials make them effective for agricultural applications Explains the challenges surrounding the mass production of copper-based nanomaterials

This book introduces intelligent manufacturing system planning, design, and implementation, through the deep integration of the Internet, big data, artificial intelligence, and manufacturing process, to promote the transformation and upgrading of enterprises. This book shows the implementation of intelligent manufacturing process with 12 benchmarking enterprises, discusses the planning, implementation, and control of intelligent manufacturing system technology and method of theory, and analyzes the five hierarchies of intelligent manufacturing system, the five stages of life cycle, and five kinds of intelligent depth. The content can cultivate the reader's vocational ability to develop intelligent solutions and implementation based on complex, uncertain environment needs. This book will be interesting and useful to a wide readership in the various fields of management, information science, and engineering science.

Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. Presents tools, connections and proactive solutions to take sustainability programs to the next level Offers a practical guide for making students proficient in modern electronic data analysis and graphics Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery

This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.

Interconnecting Smart Objects with IP: The Next Internet explains why the Internet Protocol (IP) has become the protocol of choice for smart object networks. IP has successfully demonstrated the ability to interconnect billions of digital systems on the global Internet and in private IP networks. Once smart objects can be easily interconnected, a whole new class of smart object systems can begin to evolve. The book discusses how IP-based smart object networks are being designed and deployed. The book is organized into three parts. Part 1 demonstrates why the IP architecture is well suited to smart object networks, in contrast to non-IP based sensor network or other proprietary systems that interconnect to IP networks (e.g. the public Internet of private IP networks) via hard-to-manage and expensive multi-protocol translation gateways that scale poorly. Part 2 examines protocols and algorithms, including smart objects and the low power link layers technologies used in these networks. Part 3 describes the following smart object network applications: smart grid, industrial automation, smart cities and urban networks, home automation, building automation, structural health monitoring, and container tracking. Shows in detail how connecting smart objects impacts our lives with practical implementation examples and case studies Provides an in depth understanding of the technological and architectural aspects underlying smart objects technology Offers an in-depth examination of relevant IP protocols to build large scale smart object networks in support of a myriad of new services

Smart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques. This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities, and explores three key areas that can be improved using big data prediction: grid energy, road traffic networks and environmental health in smart cities. The big data prediction methods proposed in this book are highly significant in terms of the planning, construction, management, control and development of green and smart cities. Including numerous case studies to explain each method and model, this easy-to-understand book appeals to scientists, engineers, college students, postgraduates, teachers and managers from various fields of artificial intelligence, smart cities, smart grid, intelligent traffic systems, intelligent environments and big data computing.

The book is the first international reference on the field of smart device recognition and Ubiquitous Electric Internet of Things (UEIOT). It presents a range of state-of-the-art key methods and applications for smart device recognition. In future smart environments, obtaining energy consumption information for identifying every device is an effective approach to guarantee the energy efficiency of smart industrial systems. Such as, the Ubiquitous Electric Internet of Things (UEIOT) technology represents one of the most effective measures for electricity and energy management and has attracted considerable attention from scientists and engineers around the world. The realization of smart device recognition in the UEIOT framework has become the core and basis of UEIOT’s success. The device smart recognition can help governments and managers to distribute energy and power better, and help device manufacturers to improve their products regarding smart energy conservation. Accordingly, in the future smart industry, implementing smart device recognition is desired and very important. In the book, several methods, strategies, and experiments for achieving smart device recognition are presented in details. As the first monograph in the field of smart device recognition, the book can provide beneficial reference for students, engineers, scientists, and managers in the fields of power, energy, electromechanical devices, smart cities, artificial intelligence, etc.

Cost-Effective Technologies for Solid Waste and Wastewater Treatment synthesizes methods, case studies, and analyses of various state-of-the-art techniques for removing contaminants from wastewater, solid waste, or sewage and converting or reusing the waste with minimum impact on the environment. Focusing on innovative treatment strategies, as well as recent modifications to conventional processes, the book covers methods for a complex variety of emerging pollutants including organic matter, chemicals, and micropollutants resulting from developmental and industrial activities. Serving as a practical guide to state-of-the-art methods, Cost-Effective Technologies for Solid Waste and Wastewater Treatment also delivers foundational information on the practical design of treatment and reuse systems and explains the treatments in terms of scale, efficiency, and effectiveness. It focuses on cost-effective technologies that are particularly applicable to environmental clean-up, such as bioaugmentation and biostimulation of plastics, activated carbon, phytoremediation, crude oil pollution stress, adsorbents, contaminants of emerging concern, anaerobic digestion, in situ chemical oxidation (ISCO), biosorption, bioremediation, radioactive contaminants, constructed wetlands, nanoremediation, and rainwater. As such, it is a valuable and practical resource for researchers, students, and managers in the fields of environmental science and engineering, as well as wastewater management, chemical engineering, and biotechnology. • Presents low-cost treatment technologies for both solid waste and wastewater • Analyzes the efficiency and effectiveness of state-of-the-art technologies • Includes methods and case studies for practical application

The eventual aim when applying digital technologies in agriculture is to replace or reduce the human labor required for agricultural production. Large amounts of heterogeneous data are essential for integration studies of automated agriculture, and the digitalization of agriculture is helping to fulfill the demand for this data, but management of the data gathered presents its own challenges. That is where the Intelligent Environment (IE) paradigm comes into play to guide the design of the systems, techniques and algorithms able to analyze the data and provide recommendations for farmers, managers and other stakeholders. This book, Agriculture and Environment Perspectives in Intelligent Systems, is divided into 5 chapters. Chapter 1 explores the use of intelligent systems in Controlled Environment Agriculture (CEA) facilities; Chapter 2 reviews the adoption of intelligent systems in the research field of biomonitoring; Chapter 3 proposes an intelligent system to acquire and pre-process data for precision agriculture applications; Chapter 4 illustrates the use of intelligent algorithms to make more efficient use of scarce resources such as water; and Chapter 5 focuses on the generation of intelligent models to predict frosts in crops in south-eastern Spain. There is still a need to bridge the gap between the needs of farmers, environmental managers and stakeholders and the solutions offered by information and communication technology. This book will be of interest to all those working in the field.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Industrial IoT Technologies and Applications, IoT 2017, held in Wuhu, China, in March 2017. The volume contains 25 papers carefully reviewed and selected from 41 submissions focusing on topics such as big data, cloud computing, Internet of things, areas of control, mobile computing, and security.

In recent years, the need for smart equipment has increased exponentially with the upsurge in technological advances. To work to their fullest capacity, these devices need to be able to communicate with other devices in their network to exchange information and receive instructions. Computational Intelligence in the Internet of Things is an essential reference source that provides relevant theoretical frameworks and the latest empirical research findings in the area of computational intelligence and the Internet of Things. Featuring research on topics such as data analytics, machine learning, and neural networks, this book is ideally designed for IT specialists, managers, professionals, researchers, and academicians.

This book gathers a selection of peer-reviewed papers presented at the second Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020) conference, held in Shanghai, China, on 28–29 December 2020. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.

In recent years, intelligent cities, also known as smart cities or cognitive cities, have become a perceived solution for improving the quality of life of citizens while boosting the efficiency of city services and processes. This new vision involves the integration of various sectors of society through the use of the internet of things. By continuing to enhance research for the better development of the smart environments needed to sustain intelligent cities, citizens will be empowered to provision the e-services provided by the city, city officials will have the ability to interact directly with the community as well as monitor digital environments, and smart communities will be developed where citizens can enjoy improved quality of life. Developing and Monitoring Smart Environments for Intelligent Cities compiles the latest research on the development, management, and monitoring of digital cities and intelligent environments into one complete reference source. The book contains chapters that examine current technologies and the future use of internet of things frameworks as well as device connectivity approaches, communication protocols, security challenges, and their inherent issues and limitations. Including unique coverage on topics such as connected vehicles for smart transportation, security issues for smart homes, and building smart cities for the blind, this reference is ideal for practitioners, urban developers, urban planners, academicians, researchers, and students.

As the number of digital devices used in daily life grows, it comes as no surprise that the next step in technological evolution is to conveniently interconnect these devices. This is where the Internet of Things fits in. The Internet of Things refers to all devices that are connected to the internet and share data on it, but there are numerous applications for this technology, ranging from smartphones to driverless cars. Despite the convenience smart devices offer, they also raise significant concerns about data privacy and security. Readers will encounter contrasting viewpoints on this timely and evolving issue.

The environment, as modified and created by people, is largely about the use of information, its generation and exchange. How do recent innovations in the technologies of information management and communication affect our use of space and place, and the way we perceive and think about our surroundings? This volume provides an international, exploratory forum for the complex phenomenon of new information and communication technology as it permeates and transforms our physical world, and our relation to it: the architectural definition of our surrounding, geographical space, urban form and immediate habitats. This book is a reader, an attempt at registering disciplinary changes in context, at tracing subtexts for which most mainstream disciplines have no established language. The project is to give voice to an emerging meta-discipline that has its logic across the specializations. A wide range of professionals and academics report findings, views and ideas. Together, they describe the architecture of a postmodern paradigm: how swiftly mutating the proliferating technology applications have begun to interact with the construction and reading of physical space in architecture, economics, geography, history, planning, social sciences, transport, visual art - but also in the newer domains that have joined this spectrum through the very nature of their impacts: information technology and telecommunications. The space navigated in this volume is vast, both in physical terms and in its virtual and analogous form. It ranges from the space that immediately encompasses, or is simulated to encompass, the human body - as in buildings and virtual tectonics - to that of towns and regions. We stay clear of molecular-scale space, and of dimensions that are larger than earth.

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers