Yazar "Craciunescu, Razvan" seçeneğine göre listele

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    A Comprehensive Survey on Resource Management in 6G Network Based on Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sefati, Seyed Salar; Haq, Asim Ul; Nidhi; Craciunescu, Razvan; Halunga, Simona; Mihovska, Albena; Fratu, Octavian
    The transition to 6th Generation (6G) cellular networks offers significant improvements over 5th Generation (5G), enhancing data transfer, reducing latency, and improving network reliability. Advanced Multiple-Input Multiple-Output (MIMO) technology in 6G boosts network efficiency, particularly benefiting Ultra-Reliable Low-Latency Communications (URLLC). This paper reviews literature on resource management in the Internet of Things (IoT) within the 6G context. We categorize the study into four segments: network-aware resource management, dynamic resource allocation, predictive resource distribution based on traffic and architecture, and energy-centric resource allocation considering IoT device mobility and location. We provide a detailed perspective on current research and highlight future research avenues. Key contributions include a comparative study of IoT resource management techniques, an overview of resource management across LTE, 5G, and 6G networks, insights into applications like Intelligent Transportation Systems (ITS), Industrial IoT (IIoT), and Mobile CrowdSensing (MCS), and an emphasis on upcoming challenges. We emphasize the crucial role of efficient resource management in IoT, particularly in the 6G landscape. © 2013 IEEE.
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    Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Sefati, Seyed Salar; Craciunescu, Razvan; Arasteh, Bahman; Halunga, Simona; Fratu, Octavian; Tal, Irina
    Highlights: What are the main findings? Implementation of blockchain enhances the security and scalability of smart city frameworks. Federated Learning enables efficient and privacy-preserving data sharing among IoT devices. What are the implications of the main finding? The proposed framework significantly reduces the risk of data breaches in smart city infrastructures. Improved data privacy and security can foster greater adoption of IoT technologies in urban environments. Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. © 2024 by the authors.