Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices

• URL: http://arxiv.org/abs/2607.12599v1
• Date: Tue, 14 Jul 2026 10:19:07 GMT
• Title: Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
• Abstract summary: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications.<n>Recent deep-learning approaches have improved detection accuracy, but many remain computationally expensive and often fail to capture subtle anomalies.<n>We propose a Lightweight MultiScale AutoEncoder network for univariate time-series anomaly detection, designed to be compact and computationally efficient.
• Score: 0.12891210250935145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity. Autoencoders are widely used for anomaly detection because they reconstruct normal patterns well, leading to elevated reconstruction errors for anomalous inputs. Their simplicity and efficiency also make them suitable lightweight backbones for handling multi-scale inputs. To address these challenges, we propose a Lightweight MultiScale AutoEncoder (LMSAE) network for univariate time-series anomaly detection, designed to be compact and computationally efficient. LMSAE leverages the Discrete Wavelet Transform (DWT) to extract multi-scale features and employs a multi-scale loss function to improve sensitivity to subtle or hidden anomalies. Experiments on benchmark datasets demonstrate competitive or superior detection performance despite using significantly fewer parameters and a model size of less than 500 KB. LMSAE also achieves low-latency, low-power inference on the NVIDIA Jetson Nano, with 9x reduction in inference latency and 2x reduction in power consumption, making it ideal for edge deployment.

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