使用低成本Wi-Fi嗅探器的占用估计
Occupancy Estimation Using Low-Cost Wi-Fi Sniffers
摘要：
室内和室外空间占用状态的实时测量可用于许多场景（HVAC和照明系统控制、建筑物能源优化、空间分配和预留等）。用于占用估计的传统系统依赖于环境传感器（CO2、温度、湿度）或摄像机。在本文中，我们离开这样的传统方法，并提出了一种新的占用估计系统，它是基于从用户的设备的Wi-Fi管理数据包的捕获。该系统在低成本ESP8266微控制器上实现，利用监督学习模型来适应不同的空间，并通过MQTT协议将占用信息传输到基于Web的仪表板。实验结果表明，该解决方案在四个不同的室内大学空间的有效性。
英文摘要：
Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
