ScholarWorks@성균관대학교

초록

Since the requirement of effective resource management and task scheduling in Mobile Edge Computing (MEC) systems grows tremendously in ultra-dense IoT applications enabled by the 6G networks, innovative solutions are required to overcome dependency of tasks, resource competition, and latency issues. The paper develops a state-of-the-art framework that amalgamates Variant Sweep Clustering (VSC) and the Traveling Salesman Problem (TSP). To care for the fundamental issues, VSC utilizes intelligent orders activity in terms of proximity and resource demand, facilitating equal workloads and reducing communication delays. TSP in each cluster minimizes task execution shortens travel distances and responds more quickly times. These strategies collectively attain a smooth balance between scalability and efficiency, providing the basis for real-time edge computing processing under dynamic scenarios. Simulation results verify the efficiency of the approach and reveal a reduction of 37.3% in the entire task completion time, from 510 units to 320 units. By using clustering and path optimization, this system not only enhances resource management but also the low latency demands of next generation IoT systems. Its adaptability to the fast-converting the new evolving 6G landscape into a transformative tech for edge computing, with theoretical and applied relevance value.

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