Robust Spectrum Sensing with Crowd Sensors
Robust Spectrum Sensing with Crowd Sensors
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DOI：
10.1109/vtcfall.2014.6966165
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Guoru Ding;Fei Song;Qi-hui Wu;Yulong Zou;Linyuan Zhang;S. Feng;Jinlong Wang
中科院分区：
文献类型：
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作者：
Guoru Ding;Fei Song;Qi-hui Wu;Yulong Zou;Linyuan Zhang;S. Feng;Jinlong Wang
本文研究了合作频谱传感的问题，其中包括一群低端个人频谱传感器（例如智能手机，平板电脑和车载传感器），其中一个关键的挑战是从人群传感器中的感应数据质量的不确定性，这些数据是人群传感器的质量，该数据的质量是可能是不可靠的，不信任的，甚至是恶意的。此外，由于设备出乎意料的故障或恶意行为，每个人群传感器都可以偶尔和随机贡献异常数据，这使现有的防御计划无效。为了应对这些独特的挑战，我们通过开发数据清洁框架提出了一种强大的频谱感测方案，在这种框架中，有执照的光谱频段的利用不足和非零异常数据的稀疏性共同利用，以鲁棒性地清除了潜在的非零非零异常数据组件，从损坏的传感数据。仿真结果表明，在各种异常数据参数配置下，提出的强大感测方案优于最先进的方案。
This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.