Veena Chidurala, Xiaodong Wang, Xinrong Li, Jesse H. Hamner
{"title":"IoT based Sensor System Design for Real-Time Non-Intrusive Occupancy Monitoring","authors":"Veena Chidurala, Xiaodong Wang, Xinrong Li, Jesse H. Hamner","doi":"10.1109/IoTaIS56727.2022.9975861","DOIUrl":null,"url":null,"abstract":"The sensor systems are growing daily in terms of their complexity and getting more sophisticated from an application perspective. Smart cities and intelligent buildings are critical driving factors in designing and improving sensor systems. However, there is always a big concern about invading people’s privacy and finding the right balance between privacy and sensing accuracy. In our previous work, we demonstrated how thermal imaging sensors could estimate occupancy effectively in a non-intrusive way. This paper presents an efficient sensor system design of a non-intrusive occupancy monitoring system (OMS). It uses state-of-the-art open-source software elements such as the FastAPI web framework, Raspberry Pi, low-resolution IR thermal sensor, temperature, humidity, and motion sensors. We also present our data collection methods in detail and show valuable insights and experimental results to demonstrate that our OMS can accurately estimate the occupancy in a designated area or a room level to meet various demanding real-time occupancy monitoring applications.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IoTaIS56727.2022.9975861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The sensor systems are growing daily in terms of their complexity and getting more sophisticated from an application perspective. Smart cities and intelligent buildings are critical driving factors in designing and improving sensor systems. However, there is always a big concern about invading people’s privacy and finding the right balance between privacy and sensing accuracy. In our previous work, we demonstrated how thermal imaging sensors could estimate occupancy effectively in a non-intrusive way. This paper presents an efficient sensor system design of a non-intrusive occupancy monitoring system (OMS). It uses state-of-the-art open-source software elements such as the FastAPI web framework, Raspberry Pi, low-resolution IR thermal sensor, temperature, humidity, and motion sensors. We also present our data collection methods in detail and show valuable insights and experimental results to demonstrate that our OMS can accurately estimate the occupancy in a designated area or a room level to meet various demanding real-time occupancy monitoring applications.