论文标题
使用低成本的物联网传感器改善对颗粒物的时空理解
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
论文作者
论文摘要
当前的空气污染监测系统笨重且昂贵，导致部署非常稀疏。此外，这些监视站的数据可能不容易访问。本文着重研究使用IoT启用低成本传感器节点来研究基于密集的空气污染监测。为此，总共九个低成本的物联网节点监测颗粒物（PM），这是最主要的污染物之一，它被部署在印度城市海得拉巴的一个小型教育校园中。其中，在IIIT-H开发了八个物联网节点，而一个物联网节点是从架子上购买的。开发了基于Web的仪表板网站，以轻松监视实时PM值。数据是从这些节点收集的五个月以上。在数据上进行了不同的分析，例如相关性和空间插值，以了解密集部署的功效，以更好地理解当地污染指标的空间变异性和时间依赖性变化。
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
