论文标题
LE3D：用于资源约束设备的数据漂移检测器的轻量级合奏框架
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
论文作者
论文摘要
随着物联网（IoT）传感器部署的增加，数据完整性变得至关重要。传感器数据可以通过良性原因或恶意作用来改变。检测漂移和不规则性的机制可以防止在物联网应用状态下的破坏和数据偏差。本文介绍了LE3D，这是能够检测异常传感器行为的数据漂移估计器的集合框架。与周围的物联网设备合作，也可以将漂移类型（自然/异常）识别为最终用户。提出的框架是一个轻巧且无监督的实现，能够在资源约束的IoT设备上运行。我们的框架也是可以推广的，可以适应新的传感器流和环境，并以最少的在线重新配置。我们将方法与最新的集合数据漂移检测框架进行了比较，从而评估了现实世界检测准确性以及实现的资源利用率。在实验现实世界的数据并模拟漂移时，我们显示了方法的有效性，该方法可达到97％的检测准确性，同时需要最少的资源来运行。
Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding IoT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.
