Towards a Smart Data Transmission Strategy for IoT Monitoring Systems: Application to Air Quality Monitoring

2019 ◽  
Author(s):  
Razika Lounas ◽  
Dhai Eddine Salhi ◽  
Hocine Mokrani ◽  
Rachid Djerbi ◽  
Mohamed T. Bennai
Keyword(s):  
Air Quality ◽  
Data Transmission ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Transmission Strategy ◽  
Smart Data

Indoor Air Quality Monitoring Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1-14
Author(s):  
Rasha AbdulWahhab ◽  
Karan Jetly Jetly ◽  
Shqran Shakir
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Ad Hoc ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Activity ◽  
Pollution Levels ◽  
Closed Area

Research activity in the field of monitoring indoor quality systems has increased dramatically in recent years. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, the population has observed that improving ventilation in the closed area can significantly reduce infection risk. However, the significance of air quality statistics makes highly accurate real-time monitoring systems vital. In this paper, several researchers' protocols and the methodologies for monitoring a good high indoor air quality system are presented. The majority of the reviewed works are aimed to reduce air pollution levels of the atmosphere. The vast majority of the identified works utilized IoT and WSN technology to fix the partial access to sensed data, high cost, and non-scalability of conventional air monitoring systems. Furthermore, ad-hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper presents a short but comprehensive review of several researchers works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, the findings highlight the need for developing systematic protocols for these systems and establishing smart air quality monitoring systems capable of measuring pollutant concentrations in the air.

scholarly journals Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
Author(s):  
Zeba Idrees ◽  
Zhuo Zou ◽  
Lirong Zheng
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Real Time ◽  
Low Cost ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Quality Data ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Computing Device

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

scholarly journals Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

2017 ◽  
Vol 7 (8) ◽  
pp. 823 ◽  
Author(s):  
Shaharil Mad Saad ◽  
Allan Andrew ◽  
Ali Md Shakaff ◽  
Mohd Mat Dzahir ◽  
Mohamed Hussein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Supervised Machine Learning ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring

scholarly journals Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

2020 ◽  
Vol 17 (14) ◽  
pp. 4942 ◽  
Author(s):  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Gonçalo Marques
Keyword(s):  
Systematic Review ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Well Being ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Knowledge

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

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