scholarly journals Wind direction sensor based on thermal anemometer for olfactory mobile robot

2019 ◽  
Vol 13 (2) ◽  
pp. 475 ◽  
Author(s):  
Helmy Widyantara ◽  
Muhammad Rivai ◽  
Djoko Purwanto
Keyword(s):  
Mobile Robot ◽  
Success Rate ◽  
Wind Direction ◽  
Tracking System ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Sensor System ◽  
Positive Temperature ◽  
Air Pollution Monitoring ◽  
Thermal Anemometer

A wind direction sensor has been implemented for many applications, such as navigation, weather, and air pollution monitoring. In an odor tracking system, the wind plays the important role to carry gas from its source. Therefore, the precise, low-cost, and effective wind direction sensor is required to trace the gas source. In this study, a new design of wind direction sensor has been developed using thermal anemometer principle with the main component of the positive temperature coefficient thermistor. Three anemometers each of which has different directions are used as inputs for the neural network to determine the direction of the wind automatically.The experimental results show that the wind sensor system is able to detect twelve wind directions. A mobile robot equipped with this sensor system can navigate to a wind source in the open air with a success rate of 80%.This system is expected to increase the success rate of the mobile robot in searching for dangerous leaking gas in the open air.

scholarly journals Portable RF-Sensor System for the Monitoring of Air Pollution and Water Contamination

2012 ◽  
Vol 2012 ◽  
pp. 1-5
Author(s):  
Joonhee Kang ◽  
Jin Young Kim
Keyword(s):  
Air Pollution ◽  
Water Contamination ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Sensor System ◽  
Limited Area ◽  
Air Pollution Monitoring ◽  
The Public ◽  
Quality Of Living ◽  
Air Pollution Forecast

Monitoring air pollution including the contents of VOC, O3, NO2, and dusts has attracted a lot of interest in addition to the monitoring of water contamination because it affects directly to the quality of living conditions. Most of the current air pollution monitoring stations use the expensive and bulky instruments and are only installed in the very limited area. To bring the information of the air and water quality to the public in real time, it is important to construct portable monitoring systems and distribute them close to our everyday living places. In this work, we have constructed a low-cost portable RF sensor system by using 400 MHz transceiver to achieve this goal. Accuracy of the measurement was comparable to the ones used in the expensive and bulky commercial air pollution forecast systems.

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Modular sensor system (MSS) for urban air pollution monitoring

2016 ◽  
Author(s):  
Wei-Ying Yi ◽  
Kwong-Sak Leung ◽  
Yee Leung ◽  
Mei-Ling Meng ◽  
Terrence Mak
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Pollution Monitoring ◽  
Sensor System ◽  
Urban Air ◽  
Air Pollution Monitoring

scholarly journals An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

2020 ◽  
Vol 9 (3) ◽  
pp. 42
Author(s):  
Rahim Haiahem ◽  
Pascale Minet ◽  
Selma Boumerdassi ◽  
Leila Azouz Saidane
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Area Network ◽  
Wide Area Network ◽  
Huge Number ◽  
Monitoring Method ◽  
Air Pollution Monitoring ◽  
Battery Lifetime ◽  
Monitoring Application

High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM.

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