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Titlebook: Device-Edge-Cloud Continuum; Paradigms, Architect Claudio Savaglio,Giancarlo Fortino,Jianhua Ma Book 2024 The Editor(s) (if applicable) and

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Toward Secure TinyML on a Standardized AI Architecture,vide a thorough review of related literature to help delineate the state of the art and classify existing approaches based on their scope, goals, and employed technical solutions. A second contribution is to delineate a research program to advance such state of the art, with a special focus on secur
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Deep Learning Meets Smart Agriculture: Using LSTM Networks to Handle Anomalous and Missing Sensor D at the compute continuum can ensure higher scalability in terms of bandwidth and latency, compared to a conventional cloud-based solution. Our findings show how the joint use of the compute continuum and deep learning can enable the development of a green-aware solution that fosters sustainable and
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Evaluating the Performance of a Multimodal Speaker Tracking System at the Edge-to-Cloud Continuum, through a simulation-based experimental evaluation performed with the iFogSim toolkit. Our findings reveal that edge-cloud integration improves application performance in terms of network usage and latency, compared to a centralized solution that solely relies on cloud computing.
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Occupancy Prediction in Buildings: State of the Art and Future Directions,ecasting in building environments. Initially, we will examine the key monitoring methods, based on Internet of Things technologies, employed for assessing presence in buildings. Subsequently, we will delve into some machine learning and deep learning algorithms utilized for predicting occupancy. Fin
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Steven Bond-Smith,Philip McCannenerated knowledge will be sent back to the infrastructure and will be available to other users of the system keeping private patients’ data locally in hospitals. In this chapter, we will briefly present the structure of an open AI/ML infrastructure and how federated learning (FL) is employed in it.
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Handbook of Rehabilitation in Older Adultsboard processing capabilities. We show that the mission time of each drone can be significantly reduced, compared to the default case where all computations are performed onboard, while producing fair schedules that respect the heterogeneity of the drones and their missions.
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