Due to the ubiquitous nature of smartphones, opportunistic phone-based crowdsensing has emerged as an important sensing modality. Since fine-grain ambient temperature measurements are a pre-requisite for energy-efficient operation of heating and cooling (HVAC) systems in buildings, in this paper, we use mobile phone sensing in conjunction with a web-based crowdsensing system to obtain detailed ambient temperature estimates inside buildings. We present a machine learning approach based on a random forest ensemble learning model that uses the phone battery temperature sensor to infer the ambient air temperature. We also present a few-shot transfer learning method to quickly learn and deploy our model onto new phones with modest training overheads. Our crowdsensing web service enables predictions made by multiple phones to be aggregated in an opportunistic fashion, extending our approach from an individual level to a community level. We evaluate our ML-based model for a range of devices, operating scenarios, and ambient temperatures, and see mean errors of less than $\pm$0.5$°$ F for our temperature predictions. More generally, our results show the feasibility of using an on-device ML model for ambient temperature predictions in mobile phones. This allows buildings - new and old, with and without sensing systems - to benefit from a new class of ubiquitous temperature sensors, enabling more sustainable operation.

由于智能手机无处不在，基于机会的基于电话的人群感知已经成为一种重要的感知方式。由于细粒度的环境温度测量是建筑物供热和制冷（HVAC）系统节能运行的前提，因此在本文中，我们将手机感应与基于Web的人群感应系统结合使用以获得详细的环境建筑物内部的温度估算。我们提出了一种基于随机森林集成学习模型的机器学习方法，该模型使用电话电池温度传感器来推断环境空气温度。我们还提出了一些简单的转移学习方法，可以以适度的培训费用快速学习模型并将其部署到新手机上。我们的人群感知网络服务使机会可以汇总多部电话做出的预测，从而将我们的方法从个人层面扩展到社区层面。我们评估了一系列设备，操作场景和环境温度的基于ML的模型，并发现平均误差小于$\pm$0.5$°$F为我们的温度预测。更一般而言，我们的结果表明使用设备上的ML模型进行手机环境温度预测的可行性。这使得无论有无传感系统的新旧建筑物，都可以从新型无处不在的温度传感器中受益，从而实现更可持续的运行。