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    1. Data och IT
    2. Systemvetenskap och AI
    3. Artificiell intelligens

    Pervasive Intelligence – From Architectures to Sustainable Edge AI Systems-of-Systems

    AvOvidiu Vermesan,Luca Valcarenghi

    Inbunden, Engelska, 2026

    Del i serien River Publishers Series in Communications and Networking

    1 496 kr

    Slutsåld

    Beskrivning

    Artificial intelligence is rapidly moving beyond centralized cloud computing into distributed edge environments, creating a new generation of intelligent systems that are autonomous, adaptive, efficient, and sustainable. Pervasive Intelligence: From Architectures to Sustainable Edge AI Systems-of-Systems explores the technologies, architectures, and engineering methodologies driving this transformation.Written by leading researchers and industry experts, the book provides a comprehensive examination of edge AI, covering topics such as embedded AI acceleration, active inference agents, hardware-software co-design, distributed orchestration, privacy-preserving intelligence, and sustainable computing. It bridges theory and practice by addressing key deployment challenges, including real-time speech enhancement, neural network optimization for embedded devices, AI benchmarking on ARM processors, FPGA-based acceleration, and trustworthy AI systems operating at the edge.A recurring theme is the emergence of edge AI systems-of-systems, in which intelligent agents, sensors, devices, and computing resources collaborate seamlessly across the edge-to-cloud continuum. The book highlights the importance of interoperability, resilience, trustworthiness, adaptive autonomy, and energy efficiency in building next-generation intelligent infrastructures.Drawing on practical applications in robotics, autonomous systems, surveillance, smart agriculture, environmental forecasting, and cyber-physical systems, the contributors demonstrate how pervasive intelligence is transforming industries and enabling more responsive, data-driven decision-making. By combining advances in artificial intelligence with systems engineering, control theory, and physics-informed modeling, this volume offers both a strategic vision and a technical framework for the future of sustainable edge intelligence.An essential resource for researchers, engineers, system architects, and advanced students, this book provides the knowledge and tools needed to design, deploy, and manage intelligent systems operating at the edge.

    Produktinformation

    • Utgivningsdatum:2026-09-25
    • Mått:156 x 234 x undefined mm
    • Format:Inbunden
    • Språk:Engelska
    • Serie:River Publishers Series in Communications and Networking
    • Antal sidor:202
    • Förlag:River Publishers
    • ISBN:9788743815198

    Utforska kategorier

    • Artificiell intelligens inom Data och IT

    Mer om författaren

    Dr. Ovidiu Vermesan holds a Ph.D. degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are intelligent systems integration, mixed-signal embedded electronics, analogue neural networks, edge artificial intelligence and cognitive communication systems. Dr. Vermesan received SINTEF’s 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinated and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, ArchitectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, Horizon Europe and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for AI, IoT and Edge Continuum Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for the Optimised Performance Embedded Processing (EdgeAI) project.Dr. Luca Valcarenghi has been a full professor at the Scuola Superiore Sant'Anna of Pisa, Italy, since 2024. He received the Laurea in Electrical Engineering in 1997 from Politecnico di Torino and the M.S.E.E. and Ph.D. in electrical engineering major telecommunications from UTD in 1999 and 2001, respectively. He has published more than 300 papers in International Journals and Conference Proceedings. Dr. Valcarenghi received a Fulbright Research Scholar Fellowship in 2009 and a JSPS ""Invitation Fellowship Program for Research in Japan (Long Term)"" in 2013. He has coordinated, as a PI or local PI, several national and international projects, among which Collaborative edge–cLoud continuum and Embedded AI for a Visionary industry of thE future (CLEVER) project. His main research interests are optical networks design, analysis, and optimization, communication networks reliability, energy efficiency in communications networks, optical access networks, zero touch network and service management, 5G technologies and beyond, and networking for the edge–cloud continuum.

    Innehållsförteckning

    • 1. Edge AI System-of-systems Reference Architecture Engineering Foundations and Multi-dimensional Views 2. Towards Smart and Adaptive Agents for Active Sensing on Edge Devices 3. Prediction of Neural Network Latency on Embedded GPU Accelerators 4. Closing the Gap Between AI Models and Silicon: Application Deployment for Next-generation Edge Accelerators 5. Multichannel Speech Enhancement under Low-latency Constraints: Balancing Quality and Computational Cost 6. Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems 7. Improving Classifier Latency at the Edge through ARM Helium 8. Structural Sensitive-attribute Leakage in Face Recognition Embeddings for Edge AI Deployments 9. TinyHLS, a Python-based Hardware Compiler for 1D and 2D Convolutional Neural Networks 10. Fair AI Experimentation on Edge Device Clusters via Distributed Orchestration in dAIEdge-VLab 11. Physics-informed Kalman Filtering for Multi-step Bias Correction in Indoor Temperature Forecasting