Journal of Advances in Engineering and Technology [JAET] Volume 04 Issue ii

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/5057

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    Highly Efficient 3D Object Transmission System for HTC Services in 6G Networks
    (Faculty of Engineering, 2026-03) Svechnikov, D; Volkov, A; Marochkina,A; Muthanna, A; Kouhceryavy, A
    In recent years, advancements in technology have brought forth a new frontier in visual communication. Holography is a technique that captures and reproduces three-dimensional (3D) images with an unprecedented level of realism and depth, has emerged as a groundbreaking method for conveying visual information. Unlike traditional images and videos, holography recreates scenes with full parallax, enabling viewers to perceive objects from various angles. The transmission of holographic images presents both exciting possibilities and unique challenges. To this end, this article conducts a comparative analysis of a previously developed application system for transmitting dynamic 3D human movements with a ready-made solution for transmitting 2D video streams in order to provide conference calling services. The network characteristics of the systems were collected and compared. The opportunities that programs currently provide and will provide in the future are examined.
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    ItemOpen Access
    Task Scheduling Problem in Fog Computing Environment with improved Memetic algorithm
    (Faculty of Engineering, 2026-03) Thang, D.V; Artem, V; Muthanna, A; Vorozheikina, O; Koucheryavy, A
    The task scheduling problem in fog computing is one of the key challenges in the development of fog computing within next-generation communication networks. Addressing this challenge requires balancing processing performance with resource constraints while meeting network conditions. Given the distributed and heterogeneous nature, as well as the dynamic topology, optimally allocating tasks to fog nodes is a complex issue. To contribute to solving this problem, we propose a task scheduling method based on an improved Memetic algorithm. The proposed method leverages the strengths of evolutionary algorithms and local search, while incorporating a task restructuring mechanism, to enhance allocation efficiency and task processing in the fog computing environment. Simulation experiments demonstrate that the proposed method outperforms genetic algorithms, Round-Robin, Greedy methods, and the Ant Colony Optimization algorithm in terms of efficiency. This study provides a fresh, simpler approach that aligns with network conditions while still achieving the desired performance.