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The heterogeneous connectivity of the massive Internet of Things (IoT) in 6G networks offers significant advantages by generating a vast amount of real-time data for various intelligent IoT services. However, this capability is vulnerable to sophisticated cyber-attacks and malicious network activities. This research provides a trusted and verifiable collaborative threat detection (CTD) framework by utilizing blockchain, federated learning, and efficient digital signature for securing massive IoT services in industrial applications.

The integration of the Internet of Things (IoT) into vehicular networks has given rise to a promising new concept known as the Internet of Vehicles (IoV), which is reshaping transportation by enhancing safety and improving mobility efficiency. The Electronic Control Unit (ECU) in IoV most commonly utilizes the Controller Area Network (CAN) bus protocol to exchange data within vehicles. However, without proper authentication and encryption mechanisms, existing CAN bus communications are susceptible to cyber threats, including intra-vehicle vulnerabilities. This reserach proposes a decentralized cyber attack detection (DCAT) framework by leveraging the Federated Learning (FL) technique to ensure privacy-preserving connected vehicle data during the distributed learning process.

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Automatic modulation classification (AMC) is essential to dynamic spectrum access in B5G and 6G networks for refarming the spectrum resources. However, the recent deep learning (DL)-based AMC framework has communication overhead, requires more computing resources, and poses security issues. Next-generation wireless networks allow distributed collaborative scenarios to provide ultra-reliable and low-latency communications (URLLC) services. This study proposes a blockchain-assisted decentralized collaborative AMC framework with a lightweight model to provide a trusted and decentralized AMC framework for next-generation wireless networks.

Avatars are essential in the metaverse platform development to visually represent the players. Edge learning-assisted interactive avatar creation framework can address the limited movement capability in the existing metaverse environments. This research provides a human-like avatar creation framework by employing IoT-based human activity recognition (HAR) with distributed and collaborative learning mechanisms. A reliable communication module is developed to integrate the HAR framework and metaverse platform.

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