这篇论文用推荐系统思路解决传感器选择，抗干扰还提精度20%，做跟踪或边缘计算的朋友可以看看。
该论文提出一种基于推荐系统的传感器子集选择方法，用于目标跟踪。此前研究利用低成本声学RSSI测量推荐传感器节点子集，使昂贵传感模式（如摄像头）实现高跟踪精度，但RSSI易受声学干扰。新方法采用频带声学特征和双塔多层感知机（MLP）架构，高效评分候选传感器子集。在户外车辆跟踪部署实验中，该方法相比RSSI基线将跟踪精度提升约20%，同时保持实时选择性传感所需的低计算开销。
A Recommendation System Approach for Interference-Robust Sensor Subset Selection
This paper develops a method for sensor-subset selection for tracking. Prior work showed that low-cost acoustic Received Signal Strength Indicator (RSSI) measurements can be used to recommend subsets of sensor nodes whose expensive sensing modalities, such as cameras, can achieve high tracking accuracy. While efficient, RSSI-based approaches are challenged by acoustic interference. We propose a recommendation-system-inspired framework that instead leverages frequency-band acoustic features and a Two-Tower Multi-Layer Perceptron (MLP) architecture to efficiently score candidate sensor subsets. Experimental results on outdoor vehicle-tracking deployments show that the proposed method can improve accuracy by around 20\% over the RSSI baseline while maintaining the low computational overhead required for real-time selective sensing.