Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.

中文翻译：

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众包无线频谱异常检测

跨频率、时间和空间的自动化无线频谱监测对于许多未来应用至关重要。由于大量测量位置和频谱使用环境的复杂性，手动和细粒度频谱分析变得不可能。从收集到的数据中检测无线频谱中的意外行为是这种自动化监控的关键部分，控制检测到的异常是实现自动化系统和最终用户之间交互的关键功能。在本文中，我们研究了众包传感器的无线频谱异常检测问题。我们首先详细分析这些异常的性质，并设计有效的算法，将更高维的输入数据带到跨传感器的公共特征空间。然后可以将异常检测为该特征空间中的异常值。此外，我们调查了用户反馈在异常检测过程中的重要性，以提高无监督异常检测的性能。此外，还开发了跨传感器概括用户反馈的方案，以关闭异常检测循环。