Data-Driven Pathwise Sampling Approaches for Online Anomaly Detection

Moving vehicle-based sensors (MVSs) have been increasingly used for real-time sensing and anomaly detection in various applications such as the detection of wildfires and oil spills. In this article, we propose data-driven sampling strategies using MVSs to quickly identify abrupt changes in an area of interest in real time considering their pathwise movement constraints. To tackle challenges due to variability and partial observability of online observations, we integrate instruments of statistical process control and mathematical optimization to monitor the global status of the area of interest and adaptively adjust paths of MVSs to sample from suspicious locations based on real-time data. We provide theoretical investigations and conduct simulations to validate the superior performance of the proposed methods. In a numerical study based on real-world wildfire data, we illustrate that our proposed strategies are able to detect wildfires much earlier than benchmark methods and can significantly reduce wildfire-related costs.

移动车载传感器（Moving Vehicle-based Sensors，MVSs）已被越来越广泛地应用于各类场景下的实时感知与异常检测任务，例如野火与原油泄漏的检测。本文提出了一种采用移动车载传感器的数据驱动采样策略，在考虑传感器路径运动约束的前提下，实时快速识别关注区域内的突发变化。针对在线观测存在的变异性与部分可观测性带来的挑战，本文结合统计过程控制工具与数学优化方法，对关注区域的全局状态进行监测，并基于实时数据自适应调整移动车载传感器的行进路径，以对可疑区域开展采样。本文通过理论分析与仿真实验验证了所提方法的优越性能。在基于真实野火数据开展的数值研究中，结果表明所提策略能够比基准方法更早地检测到野火，并可显著降低野火相关的处置成本。