Machine Learning Algorithms in WSNs and its Applications

A. Raut, S. Khandait
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引用次数: 7

Abstract

Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements Machine Learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.
无线传感器网络中的机器学习算法及其应用
无线传感器网络(WSN)是监测实时应用的独特和最令人鼓舞的工具。它已被用于各个领域,特别是提供实时监测和控制应用,试图监测和记录环境参数,并在困难情况下及时作出适当的决定。在最近的扩展中,机器学习技术被用于解决无线传感器网络中的各种问题,以确保在复杂的情况下及时做出良好的决策。应用机器学习将有助于提高wsn的效率,并限制人形干预或重新编程。我们研究了以前解决无线传感器网络中服务质量(QoS)供应问题的工作。此外,我们还对近年来用于解决wsn问题的基于ML的技术进行了调查。
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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