Smart Sensors Network for Air Quality Monitoring Applications

被引:136
作者
Postolache, Octavian A. [1 ,2 ]
Dias Pereira, J. M. [1 ,2 ]
Silva Girao, P. M. B. [1 ]
机构
[1] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal
[2] Escola Super Tecnol Setubal, Inst Politecn Setubal, P-2910761 Setubal, Portugal
关键词
Air quality (AirQ); embedded Web server; neural network; wireless networks; NEURAL-NETWORKS; SYSTEM;
D O I
10.1109/TIM.2009.2022372
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition and control system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor influence quantities, i.e., temperature and humidity, are also measured. Advanced processing based on multiple-input single-output neural networks is implemented at the network sensing nodes to obtain temperature and humidity compensated gas concentration values. Anomalous operation of the network sensing nodes and power consumption are also discussed.
引用
收藏
页码:3253 / 3262
页数:10
相关论文
共 50 条
[31]   Machine Learning Methods for Air Quality Monitoring [J].
Zaytar, Mohamed Akram ;
El Amrani, Chaker .
3RD INTERNATIONAL CONFERENCE ON NETWORKING, INFORMATION SYSTEM & SECURITY (NISS'20), 2020,
[32]   Immersive, Secure, and Collaborative Air Quality Monitoring [J].
Marinho, Jose ;
Martins, Nuno Cid .
COMPUTERS, 2025, 14 (06)
[33]   Advancements in Context Recognition for Edge Devices and Smart Eyewear: Sensors and Applications [J].
Palermo, Francesca ;
Casciano, Luca ;
Demagh, Lokmane ;
Teliti, Aurelio ;
Antonello, Niccolo ;
Gervasoni, Giacomo ;
Shalby, Hazem Hesham Yousef ;
Paracchini, Marco Brando ;
Mentasti, Simone ;
Quan, Hao ;
Santambrogio, Riccardo ;
Gilbert, Cedric ;
Roveri, Manuel ;
Matteucci, Matteo ;
Marcon, Marco ;
Trojaniello, Diana .
IEEE ACCESS, 2025, 13 :57062-57100
[34]   A procedure for the optimization of air quality monitoring networks [J].
Andó, B ;
Cammarata, G ;
Fichera, A ;
Graziani, S ;
Pitrone, N .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 1999, 29 (01) :157-163
[35]   Data Analytics for Home Air Quality Monitoring [J].
Mihaylova, Petya ;
Manolova, Agata ;
Georgieva, Petia .
FUTURE ACCESS ENABLERS FOR UBIQUITOUS AND INTELLIGENT INFRASTRUCTURES, FABULOUS 2019, 2019, 283 :79-88
[36]   Neural Network Based Modeling of Hysteresis in Smart Material Based Sensors [J].
Tan, Yonghong ;
Dong, Ruili ;
He, Hong .
ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II, 2019, 11555 :162-172
[37]   Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network [J].
Righi, Serena ;
Lucialli, Patrizia ;
Pollini, Elisa .
ATMOSPHERIC ENVIRONMENT, 2009, 43 (25) :3850-3857
[38]   Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy [J].
Haddadi, Ali ;
Nikoo, Mohammad Reza ;
Nematollahi, Banafsheh ;
Al-Rawas, Ghazi ;
Al-Wardy, Malik ;
Toloo, Mehdi ;
Gandomi, Amir H. .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (35) :84110-84125
[39]   An Overview of Applications of MAS in Smart Distribution Network with DG [J].
He, Yongjing ;
Wang, Wei ;
Wu, Xuezhi ;
Xu, Lijie ;
Li, Rui .
2015 IEEE 2ND INTERNATIONAL FUTURE ENERGY ELECTRONICS CONFERENCE (IFEEC), 2015,
[40]   Intravascular oxygen sensors with novel applications for bedside respiratory monitoring [J].
Formenti, F. ;
Farmery, A. D. .
ANAESTHESIA, 2017, 72 :95-104