基于校园WiFi基础设施数据的教室占用建模
Modeling Classroom Occupancy using Data of WiFi Infrastructure in a University Campus
摘要：
世界各地的大学正在经历入学人数的激增，因此校园物业管理人员正在寻求有关出勤模式的连续数据，以优化教室空间的使用。因此，越来越多的趋势是通过采用各种传感技术来测量教室出勤率，其中普遍的WiFi基础设施被视为低成本方法。在密集的校园环境中，连接的WiFi用户的数量不能很好地估计房间占用率，因为连接计数受到相邻房间、室外走道和网络负载平衡的影响。在本文中，我们开发了基于机器学习的模型来从WiFi传感基础设施推断教室占用率。我们的贡献是三方面的：（1）我们分析了一个密集和动态的无线网络，包括数千个接入点（AP）的元数据，以了解AP的覆盖范围，WiFi连接的用户的行为，以及估计房间占用率的挑战;（2）我们提出了一种方法来自动映射AP到教室使用无监督聚类算法;（3）结合不同算法的分类和回归方法对教室占用率进行建模。我们实现了84.6%的准确性，在映射AP到教室，而我们的估计房间占用率的准确性是可比的对称平均绝对百分比误差（sMAPE）为13.10%的光束计数器传感器。
英文摘要：
Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space. As a result, there is an increasing trend to measure classrooms attendance by employing various sensing technologies, among which pervasive WiFi infrastructure is seen as a low cost method. In a dense campus environment, the number of connected WiFi users does not well estimate room occupancy since connection counts are polluted by adjoining rooms, outdoor walkways, and network load balancing. In this paper, we develop machine learning based models to infer classroom occupancy from WiFi sensing infrastructure. Our contributions are three-fold: (1) We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs) to draw insights into coverage of APs, behavior of WiFi connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms using unsupervised clustering algorithms; and (3) We model classroom occupancy using a combination of classification and regression methods of varying algorithms. We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of our estimation for room occupancy is comparable to beam counter sensors with a symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.
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。
