Energy efficiency issues for green internet of things (IoT) are investigated in this book, from the perspectives of device-to-device (D2D) communications, machine-to-machine (M2M) communications, and air-ground networks. Specifically, critical green IoT techniques from D2D communications in the cellular network to M2M communications in industrial IoT (IIoT), (from single physical-layer optimization to cross-layer optimization, and from single-layer ground networks to stereoscopic air-ground networks) are discussed in both theoretical problem formulation and simulation result analysis in this book. Internet of Things (IoT) offers a platform that enables sensors and devices to connect seamlessly in an intelligent environment, thus providing intelligence services including monitoring systems, industrial automation, and ultimately smart cities. However, the huge potentials of IoT are constrained by high energy consumption, limited battery capacity, and the slow progress of battery technology. The high energy consumption of IoT device causes communication interruption, information loss, and short network lifetime. Moreover, once deployed, the batteries inside IoT devices cannot be replaced in time. Therefore, energy efficient resource allocation is urgent to be investigated to improve the energy efficiency of IoT, facilitate green IoT, and extend the network lifetime. This book provides readers with a comprehensive overview of the state-of-the-art key technologies, frameworks, related optimization algorithms, and corresponding integrated designs on green IoT. It also presents an easy-to-understand style in a professional manner, making the book suitable for a wider range of readers from students to professionals interested in the green IoT.

Health Economics and Financing Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment. The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier. Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare. Audience The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

Energy efficiency issues for green internet of things (IoT) are investigated in this book, from the perspectives of device-to-device (D2D) communications, machine-to-machine (M2M) communications, and air-ground networks. Specifically, critical green IoT techniques from D2D communications in the cellular network to M2M communications in industrial IoT (IIoT), (from single physical-layer optimization to cross-layer optimization, and from single-layer ground networks to stereoscopic air-ground networks) are discussed in both theoretical problem formulation and simulation result analysis in this book. Internet of Things (IoT) offers a platform that enables sensors and devices to connect seamlessly in an intelligent environment, thus providing intelligence services including monitoring systems, industrial automation, and ultimately smart cities. However, the huge potentials of IoT are constrained by high energy consumption, limited battery capacity, and the slow progress of battery technology. The high energy consumption of IoT device causes communication interruption, information loss, and short network lifetime. Moreover, once deployed, the batteries inside IoT devices cannot be replaced in time. Therefore, energy efficient resource allocation is urgent to be investigated to improve the energy efficiency of IoT, facilitate green IoT, and extend the network lifetime. This book provides readers with a comprehensive overview of the state-of-the-art key technologies, frameworks, related optimization algorithms, and corresponding integrated designs on green IoT. It also presents an easy-to-understand style in a professional manner, making the book suitable for a wider range of readers from students to professionals interested in the green IoT.

In the era of Industry 4.0, the world is increasingly becoming smarter as everything from mobile phones to cars to TVs connects with unique addresses and communication mechanisms. However, in order to enable the smart world to be sustainable, ICT must embark into energy efficient paradigms. Green ICT is a moving factor contributing towards energy efficiency by reducing energy utilization through software or hardware procedures. Role of IoT in Green Energy Systems presents updated research trends in green technology and the latest product and application developments towards green energy. Covering topics that include energy conservation and harvesting, renewable energy, and green and underwater internet of things, this essential reference book creates further awareness of smart energy and critically examines the contributions of ICT towards green technologies. IT specialists, researchers, academicians, and students in the area of energy harvesting and energy management, and/or those working towards green energy technologies, wireless sensor networks, and smart applications will find this monograph beneficial in their studies.

Green Internet of Things (IoT) envisions the concept of reducing the energy consumption of IoT devices and making the environment safe. Considering this factor, this book focuses on both the theoretical and implementation aspects in green computing, next-generation networks or networks that can be utilized in providing green systems through IoT-enabling technologies, that is, the technology behind its architecture and building components. It also encompasses design concepts and related advanced computing in detail. • Highlights the elements and communication technologies in Green IoT • Discusses technologies, architecture and components surrounding Green IoT • Describes advanced computing technologies in terms of smart world, data centres and other related hardware for Green IoT • Elaborates energy-efficient Green IoT Design for real-time implementations • Covers pertinent applications in building smart cities, healthcare devices, efficient energy harvesting and so forth This short-form book is aimed at students, researchers in IoT, clean technologies, computer science and engineering cum Industry R&D researchers.

This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems.

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.