In this paper, we present a lightweight, embedded algorithm for autonomous edge event triggering in IoT sensor nodes suitable for operating in mesh networks. The device acquires local sensor data, performs deterministic FFT spectral feature extraction in firmware, and maintains a temporal spectral noise-floor baseline that absorbs non-stationary environmental excitations such as rain, wind, and mechanical vibration. While adaptive thresholds in IoT sensor nodes are often applied to manage communication load or stabilize long-term metrics, this work focuses on maintaining a time-evolving spectral noise floor to preserve event trigger reliability in dynamic environments. Our method targets trigger integrity under environmental non-stationary conditions, enabling calibration-free deployment of autonomous nodes; without shared noise models or cloud-side inference. Local decision authority preserves node responsiveness when connectivity is intermittent and mitigates security risks inherent in centralized remote-analysis systems. We validate the algorithm in a single node mesh sensor deployed in a dynamic outdoor environment using a radar-class proximity sensor as one example sensor modality. Results demonstrate substantial suppression of nuisance-induced triggers, reduced false-event traffic amplification in the mesh, bounded embedded execution, and reliable detection sensitivity to true spectral signatures.

翻译：本文提出了一种轻量级嵌入式算法，适用于运行在网格网络中的物联网传感器节点的自主边缘事件触发。该设备获取本地传感器数据，在固件中执行确定性FFT频谱特征提取，并维护一个时间域频谱噪声基底基线，以吸收降雨、风力和机械振动等非平稳环境激励。尽管物联网传感器节点中的自适应阈值常被用于管理通信负载或稳定长期指标，但本工作聚焦于维持一个随时间演化的频谱噪声基底，以在动态环境中保持事件触发可靠性。我们的方法针对环境非平稳条件下的触发完整性，实现了自主节点的免标定部署，无需共享噪声模型或云端推理。本地决策权限在连接间歇性时保持节点响应能力，并缓解集中式远程分析系统固有的安全风险。我们以雷达类接近传感器作为传感模态示例，在动态室外环境中对单节点网格传感器进行了算法验证。结果表明，该方法能显著抑制噪声诱发触发，减少网格中虚假事件流量放大，实现嵌入式执行边界可控，并对真实频谱特征保持可靠的检测灵敏度。