A Cost-Effective Air Quality Supervision Solution for Enhanced Living Environments through the Internet of Things

Marques, Gonçalo; Pitarma, Rui

doi:10.3390/electronics8020170

Cited by 83 publications

(50 citation statements)

References 61 publications

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“…Numerous IoT architectures have been developed for monitoring, using not only specific IAQ parameters such as particulate matter (PM) [37][38][39] and CO 2 [40], but also multi-gas sensors [41,42]. A context-aware mobile sensing system for indoor environment monitoring is proposed by [43].…”

Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

et al. 2019

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This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.

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Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

et al. 2019

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“…Numerous monitoring solutions are available in the literature; here, we describe some of them. Several IoT architectures for IEQ monitoring are proposed by [55][56][57][58][59][60]. They incorporate open-source technologies for data processing and transmission, as well as microsensors for data acquisition.…”

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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“…Its main characteristics are the scalability and the flexibility, provided by its hierarchical structure and a distributed design based on fog computing. Other general-purpose approaches propose data management engines based on NoSQL databases [11] or pervasive monitoring IoT-based systems [12,13].…”

Section: General-purpose and Data-driven Solutionsmentioning

confidence: 99%

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

et al. 2019

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Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

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A Cost-Effective Air Quality Supervision Solution for Enhanced Living Environments through the Internet of Things

Cited by 83 publications

References 61 publications

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

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

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

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