Browsing by Author "Jafari Navimipour, N."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Erratum
    Citation - WoS: 1
    Citation - Scopus: 1
    Correction: Securing and Optimizing IoT Offloading with Blockchain and Deep Reinforcement Learning in Multi-User Environments (Wireless Networks, (2025), 31, 4, (3255-3276), 10.1007/S11276-025-03932-4)
    (Springer, 2025) Heidari, A.; Jafari Navimipour, N.; Jabraeil Jamali, M.A.; Akbarpour, S.; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari
    In this article the affiliation details for Mohammad Ali Jabraeil Jamali and Dr. Shahin Akbarpour were incorrectly given as ‘Department of Computer Science and Engineering, Qatar University, Doha, Qatar’ but should have been ‘Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran’. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
  • Thumbnail Image
    Article
    Citation - Scopus: 10
    A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm
    (IEEE Computer Society, 2023) Zanbouri, K.; Al-Khafaji, H.M.R.; Jafari Navimipour, N.; Yalcin, S.
    The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT. IEEE