Efficient Localization of Multiple Intruders in Shared Spectrum System
Efficient Localization of Multiple Intruders in Shared Spectrum System
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DOI：
10.1109/ipsn48710.2020.00025
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发表时间：
2020-04
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影响因子：
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通讯作者：
Caitao Zhan;Himanshu Gupta;A. Bhattacharya;Mohammad Ghaderibaneh
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作者：
Caitao Zhan;Himanshu Gupta;A. Bhattacharya;Mohammad Ghaderibaneh
我们解决的问题，本地化的多个入侵者（未经授权的发射机）使用一组分布式传感器在共享频谱系统的上下文中。与单发射机定位相比，多发射机定位（MTL）尚未得到深入研究。在共享频谱系统中，重要的是能够同时定位存在的多个入侵者，以有效地保护共享频谱免受基于恶意软件、干扰或其他多设备未经授权使用的攻击。解决MTL问题的关键挑战来自于需要将从多个入侵者接收到的聚合信号“分离”成来自各个入侵者的单独信号。此外，在共享频谱范例中，授权用户的演进集合（例如，在本文中，我们提出了一个有效的算法MTL问题的基础上，基于假设的贝叶斯方法称为MAP。直接应用MAP方法的MTL问题会导致令人望而却步的计算和训练成本。在这项工作中，我们开发了基于MAP的优化技术，大大提高了计算和训练成本。特别是，我们开发了一种新的插值方法，ILDW，这有助于最大限度地减少训练成本。我们通过在线学习将我们的技术推广到背景中可能存在一组动态变化的授权用户的设置。我们评估我们开发的大规模模拟技术，以及在小规模的室内和室外试验台。我们的实验表明，我们的技术优于现有的方法显着的利润率，即，在大规模模拟中，误差最多减少74%，在真实测试平台中减少30%。
We address the problem of localizing multiple intruders (unauthorized transmitters) using a distributed set of sensors in the context of a shared spectrum system. In contrast to single transmitter localization, multiple transmitter localization (MTL) has not been thoroughly studied. In shared spectrum systems, it is important to be able to localize simultaneously present multiple intruders to effectively protect a shared spectrum from malware-based, jamming, or other multi-device unauthorized-usage attacks. The key challenge in solving the MTL problem comes from the need to "separate" an aggregated signal received from multiple intruders into separate signals from individual intruders. Furthermore, in a shared spectrum paradigm, presence of an evolving set of authorized users (e.g., primary and secondary users) adds to the challenge.In this paper, we propose an efficient algorithm for the MTL problem based on the hypothesis-based Bayesian approach called MAP. Direct application of the MAP approach to the MTL problem incurs prohibitive computational and training cost. In this work, we develop optimized techniques based on MAP with significantly improved computational and training costs. In particular, we develop a novel interpolation method, ILDW, which helps minimize the training cost. We generalize our techniques via online-learning to the setting wherein there may be a set of dynamically-changing authorized users present in the background. We evaluate our developed techniques on large-scale simulations as well as on small-scale indoor and outdoor testbeds. Our experiments demonstrate that our technique outperforms the prior approaches by significant margins, i.e., error up to 74% less in large-scale simulations and 30% less in real-world testbeds.