WSN based indoor air quality monitoring in classrooms

Wang, S. K.; Chew, Sue Ping; Jusoh, Muhammad Shahar; Khairunissa, A.; Leong, Kin Yuen; Azid, Azman

doi:10.1063/1.4975296

Cited by 18 publications

(15 citation statements)

References 4 publications

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“…On one hand, compared to other systems proposed by References [46,47,49,50] based on WSN, the iAQ+ provides several advantages in scalability and installation in indoor living environments as it is only necessary to configure the Wi-Fi Internet connection and it is not required to configure the sensor nodes and coordinators. On the other hand, compared to the other systems proposed by References [46,[50][51][52] which do not use mobile computing, the iAQ+ provides smartphone and Web compatibility for data access.…”

Section: Resultsmentioning

confidence: 99%

“…Taking into account the IAQ influence on health, the development of a low-cost, open-source supervision system is a trending idea. Therefore, several monitoring solutions have been created [45][46][47][48][49][50][51][52]. Regarding the quality and important contribution of the referred solutions, a review of these studies is presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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An Internet of Things-Based Environmental Quality Management System to Supervise the Indoor Laboratory Conditions

Marques

Pitarma

2019

Applied Sciences

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Indoor air quality (IAQ) is not only a determinant of occupational health but also influences all indoor human behaviours. In most university establishments, laboratories are also used as classrooms. On one hand, indoor environment quality (IEQ) conditions supervision in laboratories is relevant for experimental activities. On the other hand, it is also crucial to provide a healthy and productive workplace for learning activities. The proliferation of cost-effective sensors and microcontrollers along with the Internet of Things (IoT) architectures enhancements, enables the development of automatic solutions to supervise the Laboratory Environmental Conditions (LEC). This paper aims to present a real-time IEQ-laboratory data collection system-based IoT architecture named iAQ Plus (iAQ+). The iAQ+ incorporates an integrated Web management system along with a smartphone application to provide a historical analysis of the LEC. The iAQ+ collects IAQ index, temperature, relative humidity and barometric pressure. The results obtained are promising, representing a meaningful contribution for IEQ supervision solutions based on IoT. iAQ+ supports push notifications to alert people in a timely way for enhanced living environments and occupational health, as well as a work mode feature, so the user can configure setpoints for laboratory mode and schoolroom mode. Using the iAQ+, it is possible to provide an integrated management of data information of the spatio-temporal variations of LEC parameters which are particularly significant not only for enhanced living environments but also for laboratory experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Internet of Things-Based Environmental Quality Management System to Supervise the Indoor Laboratory Conditions

Marques

Pitarma

2019

Applied Sciences

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“…When compared to them, AirPlus provides enhanced visualization and assessment tools for numerical and graphical representation. Moreover, when compared to [61,62,64,65,67], which are based on WSN architecture, the proposed system provides an important advantage according to scalability and configuration. The AirPlus is based on IoT architecture, which eliminates the requirement of sensor nodes and coordinators configuration.…”

Section: Resultsmentioning

confidence: 99%

“…A WSN architecture for IEQ is proposed by the authors of [64]. This experiment investigates undergraduate students' concentration during a lecture due to IEQ in the classroom.…”

Section: Related Researchmentioning

confidence: 99%

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

Marques

Pitarma

2019

JSAN

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Mobile health research field aims to provide access to healthcare anytime and anywhere through mobile computing technologies while using a cost-effective approach. Mobile health is closely related to ambient assisted living as both research fields address independence in elderly adults. Aging has become a relevant challenge, as it is anticipated that 20% of world population will be aged 60 years and older in 2050. Most people spend more than 90% of their time indoors, therefore the indoor environmental quality has a relevant impact on occupant's health and well-being. We intended to provide real-time indoor quality monitoring for enhanced living environments and occupational health. This paper presents the AirPlus real-time indoor environmental quality monitoring system, which incorporates several advantages when compared to other systems, such as scalability, flexibility, modularity, easy installation, and configuration, as well as mobile computing software for data consulting and notifications. The results that were obtained are promising and present a significant contribution to the monitoring solutions available in the literature. AirPlus provides a rich dataset to plan interventions for enhanced indoor quality, but also to support clinical diagnostics and correlate occupant's health problems with their living environment conditions. Keywords: ambient assisted living; enhanced living environments; health monitoring; indoor air quality; indoor environmental quality; Internet of Things; mobile health; occupational health IntroductionMobile health (mHealth) is a multifaceted research field to deliver healthcare services anytime and anywhere through mobile computing technologies while using a cost-effective approach. The mHealth services and applications are enhanced personalized healthcare systems that improve the communication between patient and doctor [1]. The development of mobile computing solutions for health improvement and well-being has significantly increased in the recent past. There is relevant interest in the study and development of reliable solutions based on mobile computing for enhanced healthcare systems [2]. However, most people do not use mobile health applications, and the individuals that use them tend to stop using it in a short period of time. Therefore, the software developers need to study new pervasive and ubiquitous methods and architectures to better address the consumer needs in order to promote the adoption of these mobile applications for enhanced living environments [3]. The proliferation of computer processing power and the evolution of wireless communication technologies have promoted mHealth and lead to the development of solutions and technologies that substantially contribute to the evolution of clinical research and healthcare [4].It is anticipated that 20% of the world population will be aged 60 years and older in 2050 [5], which will cause a significant increase of health disorders, medical services costs, and caregivers demands. Furthermore, Ambient Assisted Living (AAL)...

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“…A correlation between the concentration of pollutants and health problems in schoolchildren has also been shown in some studies [7][8]. Researchers in this area have also been able to link sleepiness or an impact on health to CO 2 levels on campuses or in offices, thereby showing the physiological effects that a high concentration of CO 2 indoors has on workers or students [9][10].…”

Section: State Of the Artmentioning

confidence: 95%

An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly

Saralegui¹,

Antón²,

Ordieres‐Meré

2017

MATEC Web Conf.

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Abstract. The goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO 2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.

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WSN based indoor air quality monitoring in classrooms

Cited by 18 publications

References 4 publications

An Internet of Things-Based Environmental Quality Management System to Supervise the Indoor Laboratory Conditions

An Internet of Things-Based Environmental Quality Management System to Supervise the Indoor Laboratory Conditions

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly

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