Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include: Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol.

Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include: Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol.

The book focuses on the different aspects of sensing technology, i.e. high reliability, adaptability, recalibration, information processing, data fusion, validation and integration of novel and high performance sensors specifically aims to monitor agricultural and environmental parameters. This book is dedicated to Sensing systems for Agricultural and Environmental Monitoring offers to variety of users, namely, Master and PhD degree students, researchers, practitioners, especially Agriculture and Environmental engineers. The book will provide an opportunity of a dedicated and a deep approach in order to improve their knowledge in this specific field.

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

Provides an introduction to the topic of smart chemical sensors, along with an overview of the state of the art based on potential applications This book presents a comprehensive overview of chemical sensors, ranging from the choice of material to sensor validation, modeling, simulation, and manufacturing. It discusses the process of data collection by intelligent techniques such as deep learning, multivariate analysis, and others. It also incorporates different types of smart chemical sensors and discusses each under a common set of sub-sections so that readers can fully understand the advantages and disadvantages of the relevant transducers—depending on the design, transduction mode, and final applications. Smart Sensors for Environmental and Medical Applications covers all major aspects of the field of smart chemical sensors, including working principle and related theory, sensor materials, classification of respective transducer type, relevant fabrication processes, methods for data analysis, and suitable applications. Chapters address field effect transistors technologies for biological and chemical sensors, mammalian cell–based electrochemical sensors for label-free monitoring of analytes, electronic tongues, chemical sensors based on metal oxides, metal oxide (MOX) gas sensor electronic interfaces, and more. Addressing the limitations and challenges in obtaining state-of-the-art smart biochemical sensors, this book: Balances the fundamentals of sensor design, fabrication, characterization, and analysis with advanced methods Categorizes sensors into sub-types and describes their working, focusing on prominent applications Describes instrumentation and IoT networking methods of chemical transducers that can be used for inexpensive, accurate detection in commercialized smart chemical sensors Covers monitoring of food spoilage using polydiacetylene- and liposome-based sensors; smart and intelligent E-nose for sensitive and selective chemical sensing applications; odor sensing system; and microwave chemical sensors Smart Sensors for Environmental and Medical Applications is an important book for senior-level undergraduate and graduate students learning about this high-performance technology and its many applications. It will also inform practitioners and researchers involved in the creation and use of smart sensors.

The digital divide, caused by several factors such as poverty and slow communication technologies, has offset the progression of many developing countries. However, with rapid changes in technology, a better collaboration among communities and governance based on the latest research in ICT and technology has begun to emerge. Employing Recent Technologies for Improved Digital Governance is an essential reference source that provides research on recent advances in the development, application, and impact of technologies for the initiative of digital governance. The book has a dual objective with the first objective being to encourage more research in deploying recent trends in the internet for deploying a collaborative digital governance. The second objective is to explore new possibilities using internet of things (IoT) and cloud/fog-based solutions for creating a collaboration between the governance and IT infrastructure. Featuring research on topics such as intelligent systems, social engineering, and cybersecurity, this book is ideally designed for policymakers, government officials, ICT specialists, researchers, academicians, industry professionals, and students.

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 constitutes the refereed proceedings of the 9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015, held in Puerto Varas, Chile, in December 2015. The 36 full papers presented together with 11 short papers were carefully reviewed and selected from 62 submissions. The papers are grouped in topical sections on adding intelligence for environment adaption; ambient intelligence for transport; human interaction and ambient intelligence; and ambient intelligence for urban areas.

This book is a printed edition of the Special Issue "UAV Sensors for Environmental Monitoring" that was published in Sensors

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.