物联网网络中的无线链路质量预测

Miguel Landry Foko Sindjoung, P. Minet
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引用次数: 5

摘要

物联网网络中链路质量的知识允许更准确地选择无线链路,以构建用于数据收集的路由。减少了重传的次数,从而缩短了端到端延迟、提高了端到端可靠性和延长了网络生命周期。我们建议通过应用于两个指标的机器学习技术来预测链接质量:RSSI和PDR。通过逻辑回归、线性支持向量机、支持向量机和随机森林分类器得到的精度在格勒诺布尔部署的真实物联网网络的轨迹上进行计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wireless Link Quality Prediction in IoT Networks
The knowledge of link quality in IoT networks allows a more accurate selection of wireless links to build the routes used for data gathering. The number of retransmissions is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. We propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy got by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is computed on the traces of a real IoT network deployed in Grenoble.
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