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.基于移动车辆的传感器（Moving vehicle-based sensors, MVSs）已愈发广泛地应用于野火、原油泄漏检测等各类场景的实时感知与异常检测任务。本文提出了一种基于MVSs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，实现关注区域内突发变化的实时快速识别。针对在线观测存在的变异性与部分可观测性带来的挑战，本文整合统计过程控制工具与数学优化方法，实现对关注区域全局状态的监测，并基于实时数据自适应调整MVSs的行驶路径，以对可疑区域进行采样。本文通过理论分析与仿真实验验证了所提方法的优异性能。在基于真实野火数据的数值实验中，结果表明所提策略能够比基准方法更早地探测到野火，并可显著降低野火相关的应急成本。