Real-time event detection in IoT mesh sensor networks must balance sensitivity against false-positive load on a constrained mesh radio. We present a Monte Carlo comparison of the Temporal Spectral Noise-Floor Adaptation (TSNFA) detector against four classical comparators drawn from the radar Constant False Alarm Rate (CFAR) family and from sequential change detection: the Lipski FFT energy detector, Cell-Averaging CFAR (CA-CFAR), Ordered-Statistic CFAR (OS-CFAR), and state-machine Cumulative Sum (CUSUM). All five detectors are implemented to fit a Cortex-M0+ class envelope, process a 1-D 100 Hz time series in 128-sample frames, and use temporal reference windows in place of the spatial reference cells of conventional radar CFAR. Across a factorial set of four configurations (10 and 50 nodes; 12 dB and 18 dB SNR), each replicated five times over 24 hours, TSNFA achieves 99.97 to 100% event detection rate with 100% event precision and zero false-positive clusters per node. The classical comparators each succeed on one quality dimension and fail on another. Lipski FFT (k = 3), CA-CFAR, and OS-CFAR all maintain near-perfect detection rate but with event precision below 3% and per-node bandwidth between 145 kB/h and 1.2 MB/h. CA-CFAR and OS-CFAR are indistinguishable in false-alarm performance, both saturating the same broadband-statistic failure mode. CUSUM shows an SNR-dependent detection-rate drop from about 70% at 18 dB to 51% at 12 dB. TSNFA is the only algorithm tested that simultaneously achieves high detection rate, high precision, and low per-node bandwidth.

翻译：物联网网状传感器网络中的实时事件检测需在灵敏度与虚警负载之间取得平衡，以适配受限的网状无线电传输。本文提出一种时间频谱噪声基底自适应（TSNFA）检测器与四种经典对照方法的蒙特卡洛对比研究，这些对照方法分别来自雷达恒虚警率（CFAR）家族与序列变化检测领域：Lipski FFT能量检测器、单元平均恒虚警率（CA-CFAR）、有序统计恒虚警率（OS-CFAR）以及状态机累积和（CUSUM）。所有五种检测器均设计适配Cortex-M0+类硬件环境，处理128帧采样的1维100赫兹时间序列，并采用时间参考窗口替代传统雷达CFAR的空间参考单元。在四种实验配置（10节点与50节点；12分贝与18分贝信噪比）的全因子设计中，每种配置经过24小时内五次重复验证，TSNFA实现了99.97%至100%的事件检测率、100%的事件精确率以及每节点零虚警簇。经典对照方法各自在某单项质量维度表现优异，却在另一维度显著失效。Lipski FFT（k=3）、CA-CFAR与OS-CFAR均保持了近乎完美的检测率，但事件精确率低于3%，每节点带宽消耗介于145千字节/小时至1.2兆字节/小时之间。CA-CFAR与OS-CFAR在虚警性能上无显著差异，均陷入相同的宽带统计失效模式。CUSUM呈现出信噪比依赖的检测率衰减，从18分贝时的约70%降至12分贝时的51%。TSNFA是唯一在测试中同时实现高检测率、高精确率与低每节点带宽的算法。