# Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications

arXiv: https://arxiv.org/abs/2510.14688 · HTML: https://mlsanity.com/paper/2510.14688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Optimal Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1602.04589/) (2016)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Hypothesis testing with e-values](https://mlsanity.com/paper/2410.23614/) (2024)
- [Online Rules for Control of False Discovery Rate and False Discovery Exceedance](https://mlsanity.com/paper/1603.09000/) (2016)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Online false discovery rate control for anomaly detection in time series](https://mlsanity.com/paper/2112.03196/) (2021)
- [Neuromorphic Integrated Sensing and Communications](https://mlsanity.com/paper/2209.11891/) (2022)
- [End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence](https://mlsanity.com/paper/2009.01527/) (2020)
- [Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management](https://mlsanity.com/paper/2301.06510/) (2023)
- [Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning](https://mlsanity.com/paper/2404.01815/) (2024)
- [An Asynchronous Wireless Network for Capturing Event-Driven Data from Large Populations of Autonomous Sensors](https://mlsanity.com/paper/2305.12293/) (2023)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Coexistence of Push Wireless Access with Pull Communication for Content-based Wake-up Radios](https://mlsanity.com/paper/2404.12816/) (2024)
- [Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck](https://mlsanity.com/paper/2404.01804/) (2024)

## Abstract (arXiv)

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.