Triyanto Dedi, Mustika I Wayan
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
Department of Computer Engineering, Universitas Tanjungpura, Pontianak 78124, Indonesia.
Sensors (Basel). 2025 Mar 10;25(6):1722. doi: 10.3390/s25061722.
Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov-MILP-based short-distance complete local execution algorithm and the full offloading-computation method.
移动边缘计算（MEC）是一项在无线网络中取得了重大进展的现代技术。为应对资源受限环境中高效任务执行的挑战，这项工作通过基于公平性和能源效率的资源分配来增强系统性能。将能量收集（EH）技术与MEC相结合，通过优化移动设备的功耗来提高可持续性，这对任务执行效率至关重要。MEC与超密集网络（UDN）的结合在第五代网络中对于满足超低延迟应用的计算需求至关重要。在本研究中，使用基于李雅普诺夫混合整数线性规划（MILP）的最优成本（LYMOC）技术解决了与计算卸载和资源分配相关的问题。使用李雅普诺夫漂移加惩罚方法解决优化问题。随后，采用MILP方法选择最优卸载选项，同时确保用户之间以公平为导向的资源分配，以提高整体系统性能和用户满意度。与传统方法不同，传统方法在密集网络中往往忽视公平性，而所提出的方法将以公平为导向的资源分配作为优先事项，防止服务降级并提高网络效率。总体而言，仿真研究结果表明，与基于李雅普诺夫 - MILP的短距离完全本地执行算法和完全卸载计算方法相比，LYMOC算法可能会大幅降低系统执行的总成本。
