Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications

• URL: http://arxiv.org/abs/2510.14688v1
• Date: Thu, 16 Oct 2025 13:56:54 GMT
• Title: Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications
• Authors: Junya Shiraishi, Jiechen Chen, Osvaldo Simeone, Petar Popovski,
• Abstract summary: This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks.<n>In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame.<n>The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system.
• Score: 58.796149594878585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks, encompassing possible use cases such as brain-machine interfaces and remote environmental monitoring. In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame. The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system. The queried neuro-SNs respond to the reader with impulse radio (IR) transmissions that directly encode the sensed local events. The reader processes these event-driven signals to determine whether the monitored environment is in a normal or anomalous state, while rigorously controlling the false discovery rate (FDR) of detections below a predefined threshold. The proposed approach employs an online hypothesis testing method with e-values to maintain FDR control without requiring knowledge of the anomaly rate, and it dynamically optimizes the sensor querying strategy by casting it as a best-arm identification problem in a multi-armed bandit framework. Extensive performance evaluation demonstrates that the proposed method can reliably detect anomalies under stringent FDR requirements, while efficiently scheduling sensor communications and achieving low detection latency.

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