Real-time event detection in Internet of Things (IoT) mesh sensor networks presents significant challenges due to time-varying noise conditions, limited computational resources at edge nodes, and the need for autonomous operation without centralised coordination. This paper presents a comprehensive Monte Carlo simulation study comparing the Temporal Spectral Noise-Floor Adaptation (TSNFA) method against six alternative detection algorithms, evaluated across a 200-node mesh network over 24 hours with realistic noise models including 60 Hz electromagnetic interference (EMI), sinusoidally drifting noise power (+/- 6 dB), and intermittent digital switching bursts. TSNFA achieves 100% detection rate with zero false positives, uniquely combining three interlocking defences: spectral band selection, temporal persistence filtering, and adaptive noise-floor tracking. Every competing algorithm omits at least one of these three defences and fails correspondingly, with false-positive rates ranging from 0 (Send-on-Delta, which also detects nothing) to 13,387,930 (broadband energy ratio). These results identify the three-defence combination as necessary and sufficient for autonomous edge triggering in resource-constrained IoT deployments.

翻译：物联网网状传感器网络中的实时事件检测面临着时变噪声条件、边缘节点有限的计算资源以及无需集中协调自主运行等重大挑战。本文通过全面的蒙特卡洛仿真研究，将时频谱噪声基底自适应（TSNFA）方法与六种替代检测算法进行了比较，在包含200个节点的网状网络上进行了为期24小时的评估，采用了包括60 Hz电磁干扰、正弦漂移噪声功率（+/-6 dB）以及间歇性数字开关突波在内的真实噪声模型。TSNFA实现了100%的检测率且零误报，其独特之处在于结合了三种相互关联的防御机制：频谱带选择、时间持久性滤波和自适应噪声基底跟踪。每种竞争算法至少缺少这三种防御中的一种，并相应地出现失败，误报率从0（Delta触发法，同时也不会检测到任何事件）到13,387,930（宽带能量比法）不等。这些结果表明，三种防御的组合是资源受限物联网部署中实现自主边缘触发的必要且充分条件。