AirIoT: IoT-Based Air Pollution Monitoring

AirIoT is a temporal dataset of air pollution concentration values measured for almost three years in Hyderabad, India. In AirIoT, a dense network of IoT-based PM monitoring devices equipped with low-cost sensors was deployed. The research focuses on two primary aspects: measurement and modelling. The team developed, calibrated, and deployed 50 IoT-based PM monitoring devices throughout Hyderabad, India, covering urban, semi-urban, and green areas. The team also developed a web-based spatial data dashboard and an Android app to dynamically visualize data from the IoT network, providing actionable insights for citizens and government bodies to effectively control pollution. Spatial interpolation models were designed to extrapolate measurements at both micro and macro levels. The effectiveness of this dense deployment was demonstrated through a case study during the Diwali festival, highlighting the need for localized data in areas with significant air pollution hotspots.Additionally, the project explored the health impacts of air pollution, correlating the data with respiratory, cardiovascular, and psycho-physiological effects. A pilot study leveraging data from AirIoT, health wearables, and a questionnaire was conducted to investigate the long-term health implications for security personnel exposed to air pollution. The team also developed computer vision-based methods to scale air pollution monitoring by analyzing features such as visibility, traffic type, and density, reducing the reliance on frequent sensor usage. These methods, trained on a large dataset using deep learning, predict air quality in real time, providing a viable solution for large-scale implementations.To ensure citizen engagement and capacity building, pilot studies were conducted in schools, involving students in the understanding and mitigation of air pollution. Public display systems showcasing real-time pollution levels fostered excitement and awareness, leading to community advocacy for reforms. Engineering students from various colleges were engaged through hackathons and internship programs to develop low-cost air pollution monitoring devices for local measurements at their institutions and neighbourhoods. The work focuses on air quality monitoring and integrates with broader smart city applications through the Smart City Research Center at IIITH. This collaborative initiative, blending IoT technology with social participation, offers a comprehensive, data-driven approach to tackle the complex challenges of air pollution in India.

AirIoT是一项历时近三年的印度海得拉巴市空气污染浓度值的时间序列数据集。在AirIoT中，部署了一个基于物联网的PM监测设备密集网络，这些设备配备了低成本传感器。研究主要聚焦于两个核心方面：测量与建模。研究团队在印度海得拉巴市的城区、半城区和绿地中开发了、校准并部署了50个基于物联网的PM监测设备。此外，研究团队还开发了基于网页的时空数据仪表板和Android应用程序，以动态可视化物联网网络中的数据，为公民和政府部门提供可操作的见解，以有效控制污染。为在微观和宏观层面进行测量外推，设计了空间插值模型。通过在迪瓦利节期间的案例研究，证明了这种密集部署的有效性，强调了在空气污染热点地区进行本地数据收集的必要性。此外，该项目还探讨了空气污染对健康的影响，将数据与呼吸系统、心血管系统及心理生理效应相关联。通过利用AirIoT、健康可穿戴设备和问卷调查数据进行的试点研究，旨在调查长期暴露于空气污染中的安保人员的健康影响。研究团队还开发了基于计算机视觉的方法，通过分析可见度、交通类型和密度等特征来扩大空气污染监测的规模，减少对频繁传感器使用的依赖。这些方法利用深度学习在大数据集上训练，实时预测空气质量，为大规模实施提供了一种可行的解决方案。为确保公民参与和能力建设，在学校开展了试点研究，让学生参与到空气污染的理解和缓解工作中。展示实时污染水平的公共显示系统激发了公众的兴奋感和意识，促进了社区对改革的倡导。来自不同学院的工程学学生通过黑客马拉松和实习项目参与了进来，以开发低成本空气污染监测设备，用于其机构和社区的本地测量。该研究聚焦于空气质量监测，并通过IIITH的智能城市研究中心与更广泛的智能城市应用相结合。这一融合物联网技术与社会参与的协作倡议，为应对印度空气污染这一复杂挑战提供了一个全面、数据驱动的解决途径。