Jafari Navimipour, NimaZanbouri, K.Al-Khafaji, H.M.R.Jafari Navimipour, N.Yalcin, S.2023-10-192023-10-19202341070-986Xhttps://doi.org/10.1109/MMUL.2023.3247522https://hdl.handle.net/20.500.12469/4907The 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. IEEEeninfo:eu-repo/semantics/closedAccessCloud computingGenetic algorithmsInternet of ThingsMultimedia systemsOptimizationQuantum computingTask analysisCodes (symbols)Fuzzy inferenceGenetic algorithmsInternet of thingsMultimedia servicesMultimedia systemsParticle swarm optimization (PSO)Quantum entanglementCloud-computingMassive dataMultimedia transmissionsOptimisationsQuality challengesQuantum ComputingQuantum genetic algorithmService QualityTask analysisTransmission schemesAnt colony optimizationArticle11210.1109/MMUL.2023.32475222-s2.0-85149369843Q1Q1