arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1405.4463cs.NIcs.LG
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无线传感器网络中的机器学习:算法、策略与应用

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Nanyang Technological University(南洋理工大学);Institute for Infocomm Research(资讯通信研究院)

Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan

摘要:

无线传感器网络监测随时间快速变化的动态环境。这种动态行为要么是由外部因素引起的,要么是由系统设计者自己发起的。为了适应这种情况,传感器网络通常采用机器学习技术来消除不必要的重新设计的需要。机器学习还激发了许多实用的解决方案,这些解决方案可以最大限度地提高资源利用率并延长网络的寿命。在本文中,我们提出了一个广泛的文献综述在2002-2013年期间的机器学习方法,用于解决无线传感器网络(WSNs)中的常见问题。每个算法的优点和缺点进行评估对相应的问题。我们还提供了一个比较指南,以帮助WSN设计人员开发合适的机器学习解决方案,为他们的特定应用挑战。

英文摘要:

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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