针对嵌入式设备的实时异常检测，提出轻量级算法选型指南。
Real-Time Machine Learning for Embedded Anomaly Detection
- 对比隔离森林、一类SVM等轻量算法在嵌入式场景下的表现。
- 揭示精度与计算效率间的权衡，明确硬件约束对算法选择的根本影响。
- 提供面向TinyML的新趋势和设备适配建议，适合边缘安全应用开发者。
物联网和嵌入式设备的普及对边缘端实时异常检测提出了严峻挑战。本文综述了专为资源受限设备设计的机器学习方法，重点考察其在延迟、内存和功耗上的严格限制。对比了隔离森林、一类SVM、递归结构及统计方法在实际嵌入式部署中的表现，揭示了准确率与计算效率之间的显著权衡，指出硬件约束从根本上重塑了算法选择。文章最后给出了基于设备配置的算法选型建议，并介绍TinyML新趋势，助力缩小检测能力与嵌入式现实之间的差距。该综述可作为带宽受限且可能涉及安全关键场景的边缘部署工程师的战略参考。
原文摘要 · Abstract (English)
The spread of a resource-constrained Internet of Things (IoT) environment and embedded devices has put pressure on the real-time detection of anomalies occurring at the edge. This survey presents an overview of machine-learning methods aimed specifically at on-device anomaly detection with extremely strict constraints for latency, memory, and power consumption. Lightweight algorithms such as Isolation Forest, One-Class SVM, recurrent architectures, and statistical techniques are compared here according to the realities of embedded implementation. Our survey brings out significant trade-offs of accuracy and computational efficiency of detection, as well as how hardware constraints end up fundamentally redefining algorithm choice. The survey is completed with a set of practical recommendations on the choice of the algorithm depending on the equipment profiles and new trends in TinyML, which can help close the gap between detection capabilities and embedded reality. The paper serves as a strategic roadmap for engineers deploying anomaly detection in edge environments that are constrained by bandwidth and may be safety-critical.
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