Green Internet of Things and Machine Learning

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenance A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management (PHM), this important work covers all areas of electronics and explains how to: assess methods for damage estimation of components and systems due to field loading conditions assess the cost and benefits of prognostic implementations develop novel methods for in situ monitoring of products and systems in actual life-cycle conditions enable condition-based (predictive) maintenance increase system availability through an extension of maintenance cycles and/or timely repair actions; obtain knowledge of load history for future design, qualification, and root cause analysis reduce the occurrence of no fault found (NFF) subtract life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.

DIGITAL FORENSICS AND INTERNET OF THINGS It pays to be ahead of the criminal, and this book helps organizations and people to create a path to achieve this goal. The book discusses applications and challenges professionals encounter in the burgeoning field of IoT forensics. IoT forensics attempts to align its workflow to that of any forensics practice—investigators identify, interpret, preserve, analyze and present any relevant data. As with any investigation, a timeline is constructed, and, with the aid of smart devices providing data, investigators might be able to capture much more specific data points than in a traditional crime. However, collecting this data can often be a challenge, as it frequently doesn’t live on the device itself, but rather in the provider’s cloud platform. If you can get the data off the device, you’ll have to employ one of a variety of methods given the diverse nature of IoT devices hardware, software, and firmware. So, while robust and insightful data is available, acquiring it is no small undertaking. Digital Forensics and Internet of Things encompasses: State-of-the-art research and standards concerning IoT forensics and traditional digital forensics Compares and contrasts IoT forensic techniques with those of traditional digital forensics standards Identifies the driving factors of the slow maturation of IoT forensic standards and possible solutions Applies recommended standards gathered from IoT forensic literature in hands-on experiments to test their effectiveness across multiple IoT devices Provides educated recommendations on developing and establishing IoT forensic standards, research, and areas that merit further study. Audience Researchers and scientists in forensic sciences, computer sciences, electronics engineering, embedded systems, information technology.

The development of connected, communicating objects is showing no signs of slowing down. With an increasing number of objects available on the market, the evolution of the Internet of Things is leading to more and more fields being explored via information and communication sciences. This book analyzes the ecosystem of the Internet of Things by retracing the historical and technological context of the Internet's evolution from traditional to dynamic, social and semantic, and then towards this ecosystem of connected objects. The evolution of concepts surrounding the Internet of Things is explored via real-life examples of connected objects; both those used for specific functions and for more general everyday objects. Numerous issues associated with these new technological and digital transformations in a «hyperconnected» world, as well as the impact of the massive influx of connected objects, are discussed. The crucial questions of potential intrusion into the private lives of users as well that of security are then studied.

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

RFID (Radio Frequency Identification) technology allows for automatic identification of information contained in a tag by scanning and interrogation using radio frequency (RF) waves. An RFID tag contains an antenna and a microchip that allows it to transmit and receive. This technology is a possible alternative to the use of barcodes, which are frequently inadequate in the face of rapid growth in the scale and complexity of just-in-time inventory requirements, regional and international trade, and emerging new methods of trade based on it. Use of RFID tags will likely eventually become as widespread as barcodes today. This book describes the technologies used for implementation of RFID: from hardware, communication protocols, cryptography, to applications (including electronic product codes, or EPC) and middleware. The five parts of this book will provide the reader with a detailed description of all the elements that make up a RFID system today, including hot topics such as the privacy concerns, and the Internet of Things.

This book addresses researchers and graduate students at the forefront of study/research on the Internet of Things (IoT) by presenting state-of-the-art research together with the current and future challenges in building new smart applications (e.g., Smart Cities, Smart Buildings, and Industrial IoT) in an efficient, scalable, and sustainable way. It covers the main pillars of the IoT world (Connectivity, Interoperability, Discoverability, and Security/Privacy), providing a comprehensive look at the current technologies, procedures, and architectures.

This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book: Examines cloud computing, data analytics, and sustainability and how they relate to IoT overs the scope of consumer, government, and enterprise applications Includes best practices, business model, and real-world case studies Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences. Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).