Arockiam Daniel (95 results)

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condition: New. In an increasingly interconnected digital realm, Ubiquitous and Transparent Security: Challenges and Applications emerges as a guiding beacon through the intricate web of modern cybersecurity. This comprehensive tome meticulously dissects the multifaceted challenges faced in safeguarding our digital infrastructure. From the omnipresence of threats to the evolving landscape of vulnerabilities, this book navigates the complexities with a keen eye, offering a panoramic view of the security terrain.Drawing on a rich tapestry of insights from leading experts, this book transcends the traditional boundaries of security discourse. It unveils innovative strategies and technologies, illuminating the path toward a future where security seamlessly integrates with the fabric of our digital existence. With a keen focus on transparency, it delves deep into the mechanisms that enable a clear, holistic view of security, empowering stakeholders to navigate this dynamic landscape with confidence.From cutting-edge applications to the ethical considerations of ubiquitous security, each chapter acts as a guiding compass, providing actionable insights and fostering a deeper understanding of the intricate balance between accessibility and protection. Ubiquitous and Transparent Security is not merely a book; it's a roadmap for practitioners, policymakers, and enthusiasts alike, navigating the ever-evolving world of cybersecurity.Each chapter within this compendium illuminates the diverse challenges that confront security practitioners, policymakers, and technologists today. It goes beyond the conventional paradigms, exploring the nuanced intersections between accessibility, transparency, and robust protection. Through a rich amalgamation of research-backed insights, real-world case studies, and visionary forecasts, this book offers a holistic understanding of the evolving threat landscape, empowering stakeholders to fortify their defenses proactively.…

Paperback. Condition: Brand New. 280 pages. 9.18x6.12x9.21 inches. In Stock.

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Hardback. Condition: New. Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable. Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems. In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure. Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, RandD professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.…

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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

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Hardback. Condition: New. Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable. Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems. In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure. Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, RandD professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.…

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Condition: New. In English.