Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection

2025-03-26

AI Summary

To address the latency and poor adaptability of real-time anomaly detection in streaming sensor data under dynamic operating conditions, this paper proposes a lightweight state-space model based on an enhanced Mamba architecture. The method introduces scalable state-space modeling—first applied to this task—and incorporates an adaptive gating mechanism that dynamically fuses contextual semantics with statistical features to regulate hidden-state updates. By integrating linear-complexity sequence modeling with online inference optimization, the model ensures low-latency deployment. Evaluated on multiple real-world and synthetic sensor datasets, it achieves an average 8.2% improvement in F1-score over baselines including LSTM, TCN, and USAD, while maintaining a single-step inference latency of under 1.3 ms—demonstrating a significant balance between detection accuracy and real-time performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an emph{Adaptive State-Space Mamba} ( extbf{ASSM}) framework for real-time sensor data anomaly detection. While state-space models have been previously employed for image processing applications (e.g., style transfer cite{wang2024stylemamba}), our approach leverages the core idea of sequential hidden states to tackle a significantly different domain: detecting anomalies on streaming sensor data. In particular, we introduce an adaptive gating mechanism that dynamically modulates the hidden state update based on contextual and learned statistical cues. This design ensures that our model remains computationally efficient and scalable, even under rapid data arrival rates. Extensive experiments on real-world and synthetic sensor datasets demonstrate that our method achieves superior detection performance compared to existing baselines. Our approach is easily extensible to other time-series tasks that demand rapid and reliable detection capabilities.

Problem

• Detect anomalies in real-time streaming sensor data
• Adaptive gating for dynamic hidden state updates
• Improve computational efficiency in rapid data scenarios

Innovation

• Adaptive State-Space Mamba for anomaly detection
• Dynamic gating modulates hidden state updates
• Computationally efficient for rapid sensor data