Wireless sensing approaches promise to transform smart infrastructures into privacy-preserving motion detectors, yet commercial adoption remains limited. A common assumption may explain this gap: that denser sensor deployments yield better accuracy. We tested this assumption with a 12-day naturalistic study using a 9-node ESP32-C3 mesh (72 sensing links) in a residential environment. Our results show that a single well-placed link outperformed the full 72-link mesh (AUC 0.541 vs. 0.489, Cohen's $d$=0.86). Even a random link selection matched optimized selection ($p$=0.35). The benefit comes from avoiding multi-link fusion, not from choosing the right link. We attribute this to a "dilution effect": links whose Fresnel zones miss activity regions contribute noise that overwhelms signal from informative links. In our deployment, strategic link placement mattered 2.7$\times$ more than classifier choice. We release 312 hours of labeled CSI data, firmware, and analysis code to enable validation across diverse environments.

翻译：无线感知方法有望将智能基础设施转变为保护隐私的运动检测器，但商业应用仍然有限。一种常见假设或许能解释这一差距：即更密集的传感器部署会带来更高的准确性。我们通过一项为期12天的自然主义研究测试了这一假设，在住宅环境中使用了一个包含9个ESP32-C3节点的网状网络（72条感知链路）。我们的结果表明，一条位置恰当的单一链路性能优于完整的72链路网状网络（AUC 0.541 对比 0.489，Cohen's $d$=0.86）。即使是随机选择的链路也与优化选择的表现相当（$p$=0.35）。这种优势源于避免了多链路融合，而非选择了正确的链路。我们将此归因于“稀释效应”：其菲涅尔区未覆盖活动区域的链路会引入噪声，这些噪声会淹没来自信息丰富链路的信号。在我们的部署中，策略性链路布局的重要性是分类器选择的2.7$\times$倍。我们发布了312小时的标记CSI数据、固件和分析代码，以支持在不同环境中进行验证。