This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality? Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality? Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

Climate Change in the Himalayas: Vulnerability and Resilience of Biodiversity and Forest Ecosystems explores and assesses issues affecting species survival in the rich forests of the Himalayan region. This book characterizes current biodiversity statuses, related ecosystem services, and provides new evidence and solutions for climate change effects on Himalayan animals and plants. Written by regional and international experts on climate change, ecosystems and the Himalayas, this book analyzes current species threats, loss of habitats, and carbon effects. It identifies critical areas requiring special attention and provides workable solutions for protection and ecosystem services. As many plant and animal species continue to be classified as extinct due to climate change, urbanization, and failing ecosystems, analyses and techniques in this book offer resolutions for sustaining current risks and curbing future risks. These can also be applied to other biodiverse, at-risk regions of the world.

Distributed Computing to Blockchain: Architecture, Technology, and Applications provides researchers, computer scientists and data scientists with a comprehensive and applied reference covering the evolution of distributed systems computing into blockchain and associated systems such as consensus algorithms, distributed ledgers, DApps, byzantine fault tolerance, distributed databases and operating systems. Sections cover key concepts and technologies such as distributed systems and their architecture, distributed ledger and decentralized web, application and properties of crypto economics, blockchain crypto-analysis for distributed systems followed by DApps architecture. Other sections cover blockchain architecture and security, including smart contracts, tokens, and more. The authors then review byzantine fault tolerance (BFT), distributed ledgers vs. blockchains, and blockchain protocols. The security issues of blockchain and how it aims to resolve trust problems is also covered, along with consensus algorithms used in blockchain. Throughout the book, the presentation of key concepts is supported by real-world tools, algorithms, programming languages and technology to support the implementation of distributed ledger and blockchain in a variety of fields, including healthcare, finance, legal and business applications.

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering.

The internet of medical things provides significant advantages for the well-being of society by increasing the quality of life and reducing medical expenses. An important step towards a smart healthcare system is to utilize the potential of existing technologies in order to deliver the best services to users and improve their circumstances. With the help of internet of medical things technologies, self-care and early diagnosis are influential services in strengthening the healthcare ecosystem, especially those which utilize remote monitoring systems. The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications focuses on the role of artificial intelligence, the internet of medical things, and telemedicine as well as the advantages and challenges that can occur from the integration of these technologies. The book also evolves methodologies to develop frameworks for the integration of the internet of medical things and telemedicine. Covering topics such as remote healthcare, medical imaging, and data science, this reference work is ideal for researchers, academicians, scholars, practitioners, instructors, and students.