WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用...WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用固定的K值,其定位精度在实际使用时具有局限性。尽管动态K的方案被提出,但是由于引入了新的不确定性参数,因此,并未真正解决问题。针对这个问题,提出了一种自适应动态K的WKNN室内定位方法。提出的算法的K值自适应调整仅依赖于离线和在线数据,即可以不引入新的不确定参数。在这个前提下,提出的算法采用"多雷达搜索策略"的方式自适应选择近邻数K值进行在线位置估计。在真实环境中采样了大量数据进行了试验。试验结果表明,提出的算法可根据在线情况自适应调整K值,获得了较好的定位结果。展开更多
Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-d...Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-demand services. CRs are able to sense the spectral environment, to detect unoccupied bands, and to use them for signal transmissions. This opportunity encourages malicious Users to surpass CRs by Primary User Emulation (PUE) attack and use vacant spectrums. This paper proposes an unsupervised algorithm to distinguish CRs from PUs regardless of static and mobile user. Employing K-means and graph theory are coincident in our algorithm to improve detection outcomes. The edge of graph corresponding to the relation between signals is used and the result of comparison the signal properties is exposed to different clusters. The Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) of our proposed algorithm prove our claim.展开更多
文摘WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用固定的K值,其定位精度在实际使用时具有局限性。尽管动态K的方案被提出,但是由于引入了新的不确定性参数,因此,并未真正解决问题。针对这个问题,提出了一种自适应动态K的WKNN室内定位方法。提出的算法的K值自适应调整仅依赖于离线和在线数据,即可以不引入新的不确定参数。在这个前提下,提出的算法采用"多雷达搜索策略"的方式自适应选择近邻数K值进行在线位置估计。在真实环境中采样了大量数据进行了试验。试验结果表明,提出的算法可根据在线情况自适应调整K值,获得了较好的定位结果。
文摘Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-demand services. CRs are able to sense the spectral environment, to detect unoccupied bands, and to use them for signal transmissions. This opportunity encourages malicious Users to surpass CRs by Primary User Emulation (PUE) attack and use vacant spectrums. This paper proposes an unsupervised algorithm to distinguish CRs from PUs regardless of static and mobile user. Employing K-means and graph theory are coincident in our algorithm to improve detection outcomes. The edge of graph corresponding to the relation between signals is used and the result of comparison the signal properties is exposed to different clusters. The Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) of our proposed algorithm prove our claim.
