基于物联网的实时非侵入式占用监测传感器系统设计

Veena Chidurala, Xiaodong Wang, Xinrong Li, Jesse H. Hamner
{"title":"基于物联网的实时非侵入式占用监测传感器系统设计","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":"{\"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}","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

摘要

传感器系统的复杂性与日俱增,从应用的角度来看也越来越复杂。智能城市和智能建筑是设计和改进传感器系统的关键驱动因素。然而,如何侵犯人们的隐私,如何在隐私和传感精度之间找到适当的平衡,一直是人们关注的问题。在我们之前的工作中,我们展示了热成像传感器如何以非侵入式的方式有效地估计占用率。本文提出了一种高效的非侵入式占用监测系统(OMS)传感器系统设计。它使用最先进的开源软件元素,如FastAPI web框架、树莓派、低分辨率红外热传感器、温度、湿度和运动传感器。我们还详细介绍了我们的数据收集方法,并展示了有价值的见解和实验结果,以证明我们的OMS可以准确地估计指定区域或房间级别的入住率,以满足各种苛刻的实时入住率监控应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
IoT based Sensor System Design for Real-Time Non-Intrusive Occupancy Monitoring
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices A Two-Step Machine Learning Model for Stage-Specific Disease Survivability Prediction Comparing Analog and Digital Processing for Ultra Low-Power Embedded Artificial Intelligence Channel Estimation in Cellular Massive MIMO: A Data-Driven Approach A proposal on the control mechanism among distributed MQTT brokers over wide area networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1