基于RSSI的机器学习及其前后处理在IWSNs中的小区定位
RSSI-Based Machine Learning with Pre- and Post-Processing for Cell-Localization in IWSNs
摘要：
工业无线传感器网络对现代制造业至关重要。如果这些网络中的传感器是移动的，则除了传感器数据本身之外，位置信息也可以具有高度相关性。例如，该位置信息可以通过确保传感器不被物理地移除、偏离轨道或以其他方式受损来增加无线传感器测量的可信度。在某些应用中，无论传感器是在房间或单元的内部还是外部，单元级的定位信息都是足够的。为此，使用接收信号强度指示符（RSSI）测量的定位是非常流行的，因为RSSI值在几乎所有现有技术中都是可用的，并且不需要与移动传感器节点及其在网络中的通信的直接交互。对于这种情况下，我们提出的方法来提高常见的机器学习分类器的鲁棒性和准确性，通过使用基于短期矩的特征和使用隐马尔可夫模型的第二分类阶段。从一个广泛的测量活动的数据，我们表明我们的方法的适用性，并实现了93.5%的细胞级定位精度。
英文摘要：
Industrial wireless sensor networks are becoming crucial for modern manufacturing. If the sensors in those networks are mobile, the position information, besides the sensor data itself, can be of high relevance. E.g. this position information can increase the trustability of a wireless sensor measurement by assuring that the sensor is not physically removed, off track, or otherwise compromised. In certain applications, localization information at cell-level, whether the sensor is inside or outside a room or cell, is sufficient. For this, localization using Received Signal Strength Indicator (RSSI) measurements is very popular since RSSI values are available in almost all existing technologies and no direct interaction with the mobile sensor node and its communication in the network is needed. For this scenario, we propose methods to improve the robustness and accuracy of common machine learning classifiers, by using features based on short-term moments and a second classification stage using Hidden Markov Models. With the data from an extensive measurement campaign, we show the applicability of our method and achieve a cell-level localization accuracy of 93.5\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
