Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Yang, Chao; Chen, Ho Wen; Chang, En Jui; Kristiani, Endah; Nguyen, Kieu Lan Phuong; Chang, Jo Shu

doi:10.1016/j.jhazmat.2021.126442

Cited by 31 publications

(13 citation statements)

References 70 publications

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“…Researchers expanded their focus beyond measuring CO 2 by also measuring temperature and humidity ( 27 , 35 , 65 , 66 ). To simplify the monitoring process, wireless technologies such as ZigBee were additionally utilized ( 52 , 65 , 71 ).…”

Section: Resultsmentioning

confidence: 99%

“…The prevention of SARS-CoV-2 transmission inside such infrastructures and the safe reopening of some public spaces and educational facilities were implemented through mechanical or non-mechanical means. The mechanical strategy adopts intelligent air ventilation made up of sensors and the sensor data will be transmitted to the cloud computing system thus easing users in terms of monitoring and controlling ( 28 , 65 , 66 ). Nowadays, CO 2 monitors are available in the marketplace and moreover, the Federation of European Heating, Ventilation and Air Conditioning Associations (REHVA) recommends installing CO 2 sensors to alert against poor ventilation in indoor areas ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

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Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

Wiryasaputra

Huang

Kristiani

et al. 2023

Front. Public Health

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The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

Wiryasaputra

Huang

Kristiani

et al. 2023

Front. Public Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…Evidence on adverse effects of ambient air pollution has been mounting steadily, and public interest in the quality of the air we breathe has been on the rise especially since the outbreak of COVID-19 pandemic [2,3]. While until recently, monitoring of air quality has been done by professional agencies, today, the availability of portable, low cost microsensor devices and the exponential growth of IoT (Internet of Things) in everyday life has enabled widespread monitoring of air quality also by lay people [4,5]. This development raises a number of technical and scientific challenges, among them, comparability, accuracy and repeatability of measurements, but also provides a number of opportunities to obtain data from locations that are important for human exposure but not well assessed with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Low-processing data enrichment and calibration for PM2.5 low-cost sensors

et al. 2023

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Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PMs, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.

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“…Moving the decision maker from the device to the cloud site increases the cooperation between AGVs, but making decisions from the cloud would delay decisions. Therefore, we used a cloud service to realize centralized management, the Internet of Things [3][4][5] to monitor AGVs' status, and edge computation [6] to minimize the communication cost for decision making. Thus, the proposed approach should be the best of both worlds by minimizing decision making delay and increasing the centralization of management.…”

Section: Introductionmentioning

confidence: 99%

On the Construction of an Edge-Based Remote Sensing Framework: The Applications on Automated Guided Vehicles and Drones

2022

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To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required frequently, and an examination process using cloud storage cannot commence until the device’s raw data are received. To reduce communication costs, the proposed edge-based monitoring system (EMS) applies edge computation to move the data examination from the cloud to an end site. The data buffered in the end device could be pre-processed by some detectors. For example, checking the energy is adequate for returning to the base. Thus, buffering data on the end device helps to minimize the time required by the decision maker for abnormal events, e.g., shutdowns caused by exhausted energy. In addition to adopting the common methods of storing, processing, and analyzing data at the data center, the EMS moves some time-sensitive services to the end vehicle. Moreover, after obtaining real-time motion data, the edge computing architecture immediately targets abnormal actions and sends reaction commands to shorten the decision making delay caused by the communication cost between the end vehicles and cloud storage sites, thereby avoiding collisions or accidents. The EMS has been implemented to monitor AGV and unmanned aerial vehicles. The EMS primarily monitored the power and motion of the vehicles. It also combined task-oriented motion commands for monitoring unexpected vehicle motions during tasks. If an abnormal event occurred, immediate warnings were provided through a notification interface and were immediately processed by the EMS to ensure safety during task execution. After checking data consistency between the EMS and the real device, the EMS reveals the corrected status of the device with very little delay. Therefore, the EMS could help with minimizing the time taken to make decisions. Moreover, the EMS has been modified to be deployed on drones to confirm its cross-platform applicability. In the simulations of drones, the EMS also got similar results to the simulations of AGVs. Therefore, the EMS could reduce the time in examining abnormal events and has cross-platform functionality.

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Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Cited by 31 publications

References 70 publications

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

Low-processing data enrichment and calibration for PM2.5 low-cost sensors

On the Construction of an Edge-Based Remote Sensing Framework: The Applications on Automated Guided Vehicles and Drones

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