In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

中文翻译：

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智能工厂私有5G多接入边缘计算系统的优化部署：使用混合乌鸦搜索算法


在智能工厂中，越来越多的移动智能设备被部署，以满足日益增长的柔性制造需求。这些设备配备了各种传感器，将大量数据与云服务器同步，以进行实时监控和控制。第五代移动网络（5G）结合多接入边缘计算（MEC）提供多连接、大规模数据传输和高效响应时间的能力，成为满足需求的主流网络架构。此外，随着节能趋势的兴起，最近的工作已将能源消耗视为一个重要的成本因素。然而，以往的研究大多只关注云服务器，忽视了边缘计算的能耗，并低估了异构移动设备的影响。随着智能工厂网络范围的不断扩大，挑战变得越来越严峻。本研究提出了专为智能面板工厂量身定制的私有 5G MEC 系统架构。该问题被表述为整数规划模型，确定MEC服务器和5G小型基站（包括微微基站和毫微微基站）的部署位置和数量，从而在满足连接性、容量、延迟、覆盖范围的约束的同时，最小化总体部署成本、适用性和能源消耗。由于边缘服务器部署问题是NP难题，本研究进一步提出了一种混合元启发式算法CSAVNS，该算法结合了乌鸦搜索算法（CSA）和可变邻域搜索（VNS）的优点。在CSA的全局搜索阶段，引入了VNS局部搜索来增强算法的能力。 实验分析表明，所提出的 CSAVNS 在解决方案的能力方面优于其他算法。