Spectrum Patrolling With Crowdsourced Spectrum Sensors
Spectrum Patrolling With Crowdsourced Spectrum Sensors
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DOI：
10.1109/tccn.2019.2939793
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发表时间：
2020-03
影响因子：
8.6
通讯作者：
A. Bhattacharya;Ayon Chakraborty;Samir R Das;Himanshu Gupta;P. Djurić
中科院分区：
文献类型：
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作者：
A. Bhattacharya;Ayon Chakraborty;Samir R Das;Himanshu Gupta;P. Djurić
我们使用众包方法进行RF频谱巡逻，其中广泛部署异构，低成本的频谱传感器，其任务是检测未经授权的传输，同时只消耗有限的资源。我们提出这是一个信号检测问题，其中各个传感器的检测性能可能会有很大的不同，基于各自的硬件或软件配置，但很难使用传统的方法建模。仍然必须选择传感器及其配置的最佳子集，以最大限度地提高整体检测性能，但要满足给定的资源（成本）限制。我们提出了这个问题在众包设置的挑战，并提出了一套方法来解决这些问题。这些方法使用数据驱动的方法来对单个传感器进行建模，并利用传感器选择和融合的机制，同时考虑它们的相关性。我们目前的性能结果使用的商品为基础的频谱传感器的例子，并显示出显着的改善相对于基线方法。
We use a crowdsourcing approach for RF spectrum patrolling, where heterogeneous, low-cost spectrum sensors are deployed widely and are tasked with detecting unauthorized transmissions while consuming only a limited amount of resources. We pose this as a signal detection problem where the individual sensor’s detection performance may vary widely based on their respective hardware or software configurations, but are hard to model using traditional approaches. Still an optimal subset of sensors and their configurations must be chosen to maximize the overall detection performance subject to given resource (cost) limitations. We present the challenges of this problem in crowdsourced settings and propose a set of methods to address them. These methods use data-driven approaches to model individual sensors and exploit mechanisms for sensor selection and fusion while accounting for their correlated nature. We present performance results using examples of commodity-based spectrum sensors and show significant improvements relative to baseline approaches.