使用分支和绑定方法部署次优物联网设备

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
IET Networks Pub Date : 2024-02-29 DOI:10.1049/ntw2.12119
Haesik Kim
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引用次数: 0

摘要

物联网(IoT)网络部署已在 4G 和 5G 系统中得到广泛研究,并将继续成为推动 6G 系统大规模连接的关键技术系统。在 6G 中,物联网系统将利用 6G 新技术(如综合传感与通信)运行。6G 物联网系统将成为收集现实世界信息的平台,并创造新的使用案例和商业模式。随着物联网设备和蜂窝网络变得越来越智能,物联网生态系统将成为人类生活和数字生活之间的桥梁,并加速向超级互联世界的过渡。许多研究小组都对最佳和可扩展的物联网网络设计进行了研究,但这一课题的关键挑战依然存在。我们研究了一个物联网设备部署问题,以最大限度地降低网络节点之间的传输和计算成本。物联网设备部署问题被表述为混合整数非线性编程问题。在放宽约束条件并将问题转化为混合整数线性规划(MILP)问题后,作者提出了一种带有机器学习功能的新分支与约束(BB)方法,并将 MILP 问题作为次优解求解。在数值分析中,作者评估了传统的分支与边界法和带有加权因子的拟议分支与边界法,并比较了目标函数值、探索节点数和计算时间。在给定的仿真配置下,建议的 BB 方法的性能得到了显著改善。作者找到了物联网设备与融合节点的最佳映射。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sub‐optimal Internet of Thing devices deployment using branch and bound method
The Internet of Thing (IoT) network deployments are widely investigated in 4G and 5G systems and will still be key technical systems to drive massive connectivity of 6G systems. In 6G, IoT systems will operate with 6G new technologies such as Integrated Sensing and Communications. The IoT systems of 6G will be a platform to collect information in real world and create new use cases and business models. As the IoT devices and cellular networks are getting smarter, the IoT ecosystem allows us to bridge between human life and digital life and accelerate the transition towards a hyper‐connected world. Optimal and scalable IoT network design has been investigated in many research groups but key challenges in this topic still remain. An IoT devices deployment problem is investigated to minimise the transmission and computation cost among network nodes. The IoT devices deployment problem is formulated as Mixed‐Integer Nonlinear Programming problem. After relaxing the constraints and transforming the problem to a mixed integer linear programming (MILP) problem, the authors propose a new branch and bound (BB) method with a machine learning function and solve the MILP problem as a sub‐optimal solution. In the numerical analysis, the authors evaluate both conventional BB method and the proposed BB method with weighting factors and compare the objective function values, the number of explored nodes, and computational time. The performances of the proposed BB method are significantly improved under the given simulation configuration. The author finds the optimal mapping of IoT devices to fusion nodes.
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来源期刊
IET Networks
IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍: IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.
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