PerAwareCity 2020: 5th IEEE International workshop on pervasive context-aware smart cities and intelligent transport system - Welcome and committees

Welcome to the 5th IEEE International Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transport Systems (PerAwareCity) workshop, held in conjunction with the IEEE Percom 2020 in Austin, Texas, USA

PerAwareCity 2020: 5th IEEE International workshop on pervasive context-aware smart cities and intelligent transport system - Welcome and committees

Welcome to the 5th IEEE International Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transport Systems (PerAwareCity) workshop, held in conjunction with the IEEE Percom 2020 in Austin, Texas, USA

A system of monitoring and analyzing human indoor mobility and air quality

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies

A system of monitoring and analyzing human indoor mobility and air quality

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies