Titlebook: Learning Techniques for the Internet of Things; Praveen Kumar Donta,Abhishek Hazra,Lauri Lovén Book 2024 The Editor(s) (if applicable) and

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Intelligence Inference on IoT Devices,lutionizing modern lifestyles and enhancing production efficiency. However, the substantial computational and data requirements generated by Internet of Things (IoT) devices present a significant bottleneck, rendering traditional cloud-based computing models inadequate for real-time processing tasks

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Applications of Deep Learning Models in Diverse Streams of IoT,ined considerable attention and applicability. But there are certain challenges also pertaining to apt use of IoT applications. The challenges range from generation of huge amount of data by sensors to security and privacy threats to IoT models. Malwares, energy consumption, and decision-making in h

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Quantum Key Distribution in Internet of Things,d practical considerations of incorporating QKD into IoT networks. The goal is to understand how QKD can enhance the security of IoT communication and enable a new generation of secure and trusted IoT applications.

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Enhancing Security in Intelligent Transport Systems: A Blockchain-Based Approach for IoT Data Managt human intervention. While the ITS offers numerous benefits, it also introduces various cybersecurity risks. These risks include the potential for undetected malicious IoT sensors, vulnerabilities in authorization and authentication protocols, and insecure data management practices. Therefore, it i

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Edge Computing for IoT,is applied in IoT-based intelligent systems, including areas like healthcare, manufacturing, agriculture, and transportation. Finally, we discuss current research obstacles and outline potential future directions for further investigation in this domain.

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Federated Learning Systems: Mathematical Modeling and Internet of Things,algorithms and models that can learn from data and make decisions or make predictions without being programmed. It is a booming technology that could revolutionize many areas of today’s society. The continuous development of machine learning is crucial to address the complex challenges facing our so

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