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考虑固定和随机无线信道的智能电表资源机会分配

Opportunistic Allocation of Resources for Smart Metering Considering Fixed and Random Wireless Channels.

作者信息

Jara Christian, Inga Juan, Inga Esteban

机构信息

Master of Electricity Program (MEL), Department of Master's Degree in Electricity, Universidad Politécnica Salesiana, Cuenca EC010102, Ecuador.

Telecommunications and Telematic Research Group (GITEL), Universidad Politécnica Salesiana, Cuenca EC010102, Ecuador.

出版信息

Sensors (Basel). 2025 Apr 18;25(8):2570. doi: 10.3390/s25082570.

DOI:10.3390/s25082570
PMID:40285258
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12031111/
Abstract

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions.

摘要

本文提出了一种用于蜂窝网络中无线信道分配的优化模型，该模型专为通过移动虚拟网络运营商（MVNO）传输智能电表（SM）数据而设计。该模型有效地分配传输信道，将智能电网（SG）成本降至最低。MVNO通过共享接入方案管理固定和随机信道，优化电表连接性。信道分配基于马尔可夫方法，并通过匈牙利算法进行优化，该算法可最小化电表与信道之间二分网络中的权重。此外，引入了累积令牌，根据信道可用性和网络拥塞对传输进行加权。仿真表明，虚拟网络中的动态分配可提高传输性能，有助于蜂窝网络的可持续性和成本降低。本研究强调了认知移动虚拟网络和认知无线电虚拟网络运营商（C-MVNO）进行低效资源管理的重要性，为智能网络的未来应用奠定了坚实基础。这项工作的动机是智能计量系统中对高效且可扩展的数据传输需求不断增加。其新颖之处在于集成了累积令牌和基于马尔可夫的二分图匹配算法，在异构无线条件下共同优化信道分配和传输可靠性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/b6a338f73e3f/sensors-25-02570-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/1cc29e96513b/sensors-25-02570-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/00e059fafb47/sensors-25-02570-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/c488ee2cb2d7/sensors-25-02570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/eaad5d55c421/sensors-25-02570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/97d9819ac848/sensors-25-02570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/06101bf5e90c/sensors-25-02570-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/eeea650d0e79/sensors-25-02570-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/b6a338f73e3f/sensors-25-02570-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/1cc29e96513b/sensors-25-02570-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/00e059fafb47/sensors-25-02570-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/c488ee2cb2d7/sensors-25-02570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/eaad5d55c421/sensors-25-02570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/97d9819ac848/sensors-25-02570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/06101bf5e90c/sensors-25-02570-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/eeea650d0e79/sensors-25-02570-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab9/12031111/b6a338f73e3f/sensors-25-02570-g006.jpg

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本文引用的文献

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2
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Sensors (Basel). 2022 Aug 2;22(15):5778. doi: 10.3390/s22155778.
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Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks.
多智能体强化学习在认知无人机网络中的联合协作频谱感知和信道接入。
Sensors (Basel). 2022 Feb 20;22(4):1651. doi: 10.3390/s22041651.
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Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods.使用多目标优化方法的频谱接入系统中的资源分配
Sensors (Basel). 2022 Feb 9;22(4):1318. doi: 10.3390/s22041318.
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Spectrum-Efficient Resource Allocation in Multi-Radio Multi-Hop Cognitive Radio Networks.多射频多跳认知无线电网络中的频谱高效资源分配。
Sensors (Basel). 2019 Oct 16;19(20):4493. doi: 10.3390/s19204493.