Validation of input data is essential in any computer system, but perhaps particularly important in pervasive IoT systems such as smart homes, smart cars, wearable health monitors, etc. In such systems, actions taken based on invalid inputs could have severe consequences. In this paper, we present statistical techniques for identifying data anomalies at the gateway that connects an edge network to its associated cloud services. We address two kinds of anomalies in environmental sensor data: data bias anomalies and sensor cut-off anomalies. In simulation experiments, we evaluate the effectiveness of applying control charts, a statistical process monitoring technique, to both kinds of anomalies. Our results show that using control charts as statistical methods for anomaly detection in IoT systems not only provides high performance in terms of accuracy and power (probability of detecting the anomaly), but also offers a graphical tool to monitor the IoT sensor data.

输入数据的验证在任何计算机系统中都是必不可少的，但对于普及的物联网系统（例如智能家居，智能汽车，可穿戴式健康监护仪等）可能尤其重要。在此类系统中，基于无效输入采取的措施可能会带来严重后果。在本文中，我们提出了统计技术，用于在将边缘网络连接到其关联的云服务的网关处识别数据异常。我们处理环境传感器数据中的两种异常：数据偏差异常和传感器截止异常。在模拟实验中，我们评估了将控制图，一种统计过程监控技术应用于这两种异常的有效性。