Machine learning-based spectrum decision algorithms for Wireless Sensor Networks

2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2016-03-31 DOI:10.1109/CCNC.2016.7444931

Vinicius F. e Silva, D. Macedo, Jesse L. Leoni

引用次数: 3

Abstract

Wireless Sensor Networks (WSNs) employ Industrial, Scientific and Medical (ISM) spectrum bands for communication, which are overloaded due to various technologies such as WLANs and other WSNs. Therefore, such networks must employ intelligent methods such as Cognitive Radio (CR) to coexist with other networks. This study investigates the use of supervised Machine Learning (ML) for channel selection in WSNs. The proposed models were analyzed using ML tools and techniques, and the best algorithms were evaluated on real sensor nodes. The experiments show performance improvements on the delivery rate and delivery delay when the proposed cognitive solutions are employed.

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基于机器学习的无线传感器网络频谱决策算法

无线传感器网络（WSN）使用工业、科学和医疗（ISM）频段进行通信，由于 WLAN 和其他 WSN 等各种技术的影响，这些频段已经超负荷。因此，这类网络必须采用认知无线电（CR）等智能方法与其他网络共存。本研究探讨了在 WSN 中使用有监督的机器学习（ML）进行信道选择。使用 ML 工具和技术对所提出的模型进行了分析，并在真实传感器节点上对最佳算法进行了评估。实验结果表明，采用所提出的认知解决方案后，传输速率和传输延迟的性能都有所提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)

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