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This book outlines the background and overall vision for the Internet of Things (IoT) and Machine-to-Machine (M2M) communications and services, including major standards. Key technologies are described, and include everything from physical instrumentation of devices to the cloud infrastructures used to collect data. Also included is how to derive information and knowledge, and how to integrate it into enterprise processes, as well as system architectures and regulatory requirements. Real-world service use case studies provide the hands-on knowledge needed to successfully develop and implement M2M and IoT technologies sustainably and profitably. Finally, the future vision for M2M technologies is described, including prospective changes in relevant standards. This book is written by experts in the technology and business aspects of Machine-to-Machine and Internet of Things, and who have experience in implementing solutions. Standards included: ETSI M2M, IEEE 802.15.4, 3GPP (GPRS, 3G, 4G),  Bluetooth Low Energy/Smart, IETF 6LoWPAN, IETF CoAP, IETF RPL, Power Line Communication, Open Geospatial Consortium (OGC)  Sensor Web Enablement (SWE), ZigBee, 802.11, Broadband Forum TR-069, Open Mobile Alliance (OMA) Device Management (DM), ISA100.11a, WirelessHART, M-BUS, Wireless M-BUS, KNX, RFID, Object Management Group (OMG) Business Process Modelling Notation (BPMN)Key technologies for M2M and IoT covered: Embedded systems hardware and software, devices and gateways, capillary and M2M area networks, local and wide area networking, M2M Service Enablement, IoT data management and data warehousing, data analytics and big data, complex event processing and stream analytics, knowledge discovery and management, business process and enterprise integration, Software as a Service and cloud computingCombines both technical explanations together with design features of M2M/IoT and use cases.  Together, these descriptions will assist you to develop solutions that will work in the real worldDetailed description of the network architectures and technologies that form the basis of M2M and IoTClear guidelines and examples of M2M and IoT use cases from real-world implementations such as Smart Grid, Smart Buildings, Smart Cities, Participatory Sensing, and Industrial AutomationA description of the vision for M2M and its evolution towards IoT

Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.

This book is crafted to provide a comprehensive exploration of the integration of AI and ML techniques in the context of Smart IoT systems. The editors embark on a journey through the fundamental principles, methodologies, and applications that define this dynamic field. From the basics of AI and ML to their tailored applications in the IoT domain, the chapters unfold to reveal the intricacies of this symbiotic relationship. Key features of this book include the following. Foundations of AI and ML: The book begins with a thorough examination of the foundational concepts of AI and ML, providing readers with a solid understanding of the principles that underpin these technologies; Smart IoT Systems: Delving into the world of Smart IoT systems, the book explores the architecture, components, and challenges associated with building intelligent and interconnected ecosystems; Integration Strategies: Various strategies for seamlessly integrating AI and ML into IoT systems are discussed, offering insights into how these technologies can complement each other to enhance overall system efficiency; Applications Across Industries: The practical applications of AI and ML in diverse industries are explored, showcasing real-world examples of how these technologies are reshaping sectors such as healthcare, transportation, manufacturing, and more; Challenges and Future Directions: Recognizing that every technological advancement comes with its set of challenges, the book addresses the ethical, security, and privacy concerns associated with the integration of AI and ML in Smart IoT systems. Additionally, it provides a glimpse into the future, outlining potential trends and advancements.