AQCI: An IoT Based Air Quality and Thermal Comfort Model using Fuzzy Inference
01 Dec 2019-pp 1-6
TL;DR: An end to end IoT system has been developed to sense and analyze indoor environmental parameters: temperature, humidity and carbon dioxide, which are combined to develop an indicator of the current air quality and thermal comfort index, the Air Quality and Comfort Indicator (AQCI), using fuzzy inference.
Abstract: As humans spend around 90% of their time indoors, Indoor Air Quality (IAQ) is a subject of major concern for the physical and mental well-being of humans According to the United States Environment Protection Agency (US EPA), even in centrally air-conditioned buildings, indoor air is much more polluted than outdoor air, mainly due to changes in occupancy patterns, old or ill-maintained ventilation systems and dust Therefore, it becomes important to measure and analyze IAQ In this work, an end to end IoT system has been developed to sense and analyze indoor environmental parameters: temperature, humidity and carbon dioxide These sensed values are then used to compute Predicted Mean Vote (PMV) and Ventilation Rate (VR), which are further combined to develop an indicator of the current air quality and thermal comfort index, the Air Quality and Comfort Indicator (AQCI), using fuzzy inference
Citations
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TL;DR: In this paper , a taxonomy for IoT-enabled indoor air quality (IAQ) systems and a systematic review of works concerning that classification is provided to address the ambiguity in such trends.
Abstract: Recent studies have focused on how to develop air quality-monitoring systems through smart sensor networks and the Internet of Things (IoT) technology. These works span a wide range of health research advancements in hospitals to improve air quality architecture and technologies, such as IoT-enabled gas sensors, hardware components and cloud computing. The benefits for comfort and productivity of good indoor air quality (IAQ) conditions in hospitals are an essential need. However, minimal attention has been paid to the review of the development of IoT-based sensory technology for proper IAQ in hospital facilities. Therefore, this study provided classification taxonomy for IoT-enabled IAQ systems and a systematic review of works concerning that classification to address the ambiguity in such trends. To this end, this study checked five databases: ScienceDirect, IEEE Xplore, PubMed, Scopus and Web of Science. A total of 926 papers from 2016 to 2021 were collected, and the retrieved articles were filtered in accordance with the defined inclusion and exclusion criteria to obtain the final set of 27 articles. The collected articles were classified into two categories on the basis of the aim and objective evidence across studies that fit the prespecified inclusion and exclusion criteria. The first category, which contained (n = 10/27) articles (37.03%), was ‘Integrated Indoor–outdoor Monitoring of Air Quality.’ The second category was ‘IAQ Contexts.’ This category contained (n = 17/27) articles (62.97%) and had five subcategories. Consequently, this study revealed new research opportunities, such as motivations, challenges and limitations and recommendations, which require attention for the synergistic integration of interdisciplinary works. Moreover, this extensive study listed a set of open issues and provided innovative key solutions, along with a systematic review, for IAQ-based IoT sensor deployment focusing on hospital facilities. The investigation of the recommended IAQ pollutants and parameters for hospital facilities depended on systematic literature and reliable organisations (Environmental Protection Agency, ISO and World Health Organisation). The required sensors for the most common pollutants and parameters, IoT hardware and devices and the required pollution thresholds utilised in IAQ for hospital facilities were highlighted and discussed. A set of available dataset information related to IAQ and the available resources were presented. The lifecycle of the context of IAQ phases was mapped for the first time, including the procedure sequencing and definition for each context to enhance IAQ-based IoT systems in future. We believe that this study is a useful guide for researchers and practitioners in providing direction and valuable information for ensuring proper IAQ research in future, especially in hospital facilities.
10 citations
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TL;DR: In this article , the authors developed an Indoor Air Quality (IAQ) monitoring and detecting system based on a new Internet of Thing (IoT) sensory technology device that incorporated nine recommended indoor pollutants by the academic literature.
Abstract: To develop an Indoor Air Quality (IAQ) monitoring and detecting system based on a new Internet of Thing (IoT) sensory technology device that incorporated nine recommended indoor pollutants by the academic literature and reliable organizations, such as World Health Organization (WHO), Environmental Protection Agency (EPA), and International Organization for Standardization (ISO). The pollutants include Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Formaldehyde (HCHO), Volatile Organic Compounds (VOC), Particulate Matter 2.5 (PM2.5) as well as air humidity and temperature that are used to assess the variety of indoor pollutants and provide a new IAQ pollutants dataset. Besides, the newly developed system provides real-time air quality monitoring, reports the pollutants’ data to a cloud platform (i.e., ThingSpeak), and can trigger early warnings as a service when abnormalities occurred in the air quality index. The system was tested to ensure its conformance to the recommended pollutants by collaborating with surgeons and specializing in IAQ in a hospital surgical intensive care unit (SICU), emergency department (ED), and in the women’s ward, which accommodate patients who are either newly born mothers (in case they need that) or who have had an operation, as well as pregnant patients who need to stay in the hospital to be under the supervision of medical care. Nine pollutants were identified and collected the pollutants dataset and their thresholds that affect the air quality within the hospital facilities and services (SICU, ED) to be used for assessing the effectiveness of the amount, concentration, and diversity of the pollutants. In the SICU, the concentrations of some pollutants were high in the beginning due to the residues of the previous surgery and because of the frequent use of sterilizers to clean and prepare the surgery room. Then, the concentrations of pollutants were moderate, but minutes after the start of the surgical, an increase in CO2 and formaldehyde was observed, which exceeds the threshold limit because of the use of anesthetic gas and sterilization. In the women’s ward, was all concentrations generally moderate except for particles matter PM2.5, and the same context with the 3rd installed location in the pharmacy of ED, most concentrations were moderate, except formaldehyde which exceeded the threshold. “CO” was the highest positive correlated and strongly correlated to “NO2” and that was expected because CO influences the oxidation of NO to NO2. On the contrary, the “CO” had the highest negative correlation with “VOC”, and the “NO2” had the highest negative correlation with “VOC”, chemistry is part of the responsibility for the weak correlation observed between the pollutants.
7 citations
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TL;DR: In this article , an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) were used to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters.
Abstract: • An IoT-based framework for data collection and context modelling has been developed. • Extended Kalman Filter (EKF) approach is used to deal with inaccuracies, missing data and eliminate errors. • The EKF-derived indoor pollutant concentrations are employed in context reasoning for proposing a new index. • An adaptive neuro-fuzzy inference system (ANFIS) uses the percent of dissatisfied people (PPD), ventilation rate (VR), and AQI data to identify the present state of IAQ and the percentage of time when the air quality in the classroom is unhealthy. For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO 2 , PM 2.5 , PM 10 , and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average.
1 citations
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02 Feb 2023
TL;DR: In this paper , an IoT-based approach has been used to effectively increase the efficiency and overall air quality within indoor spaces, and fuzzy logic algorithms are used to check whether the levels of the gases will cause discomfort or breathing difficulties in the people who are currently within the confines of the area.
Abstract: IoT (Internet of Things) is a concept that has been used extensively due to its acceptance and benefits if offers while used across several domains. Good fresh air with the right mix of particulates is what is needed today to ward off respiratory disorders. An important concern that needs to be addressed is how to provide this fresh air in today’s living environment. In this work, an IoT-based approach has been used to effectively increase the efficiency and overall air quality within indoor spaces. Several parameters like using temperature sensor to sense temperature, humidity sensor to sense humidity, Smoke sensor to collect data from the different gases, and many more other sensors are used to collect data from dust and air particulates. Fuzzy logic algorithms are used to check whether the levels of the gases will cause discomfort or breathing difficulties in the people who are currently within the confines of the area. An automated air ventilation and filtration system is used to either increase or decrease the airflow of the system to mitigate the air quality degradation and bring the system back to an equilibrium state therefore maintaining fresh air circulation.
References
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TL;DR: Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance, Third Edition, the authors is the standard text for the design of environments for humans to live and work safely, comfortably, and effectively.
Abstract: In the ten years since the publication of the second edition of Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance, Third Edition, the world has embraced electronic communications, making international collaboration almost instantaneous and global. However, there is still a need for a compilation of up-to-date information and best practices. Reflecting current changes in theory and applications, this third edition of a bestseller continues to be the standard text for the design of environments for humans to live and work safely, comfortably, and effectively, and for the design of materials that help people cope with their environments. See Whats New in the Third Edition: All existing chapters significantly updated Five new chapters Testing and development of clothing Adaptive models Thermal comfort for special populations Thermal comfort for special environments Extreme environments Weather Outdoor environments and climate change Fun runs, cold snaps, and heat waves The book covers hot, moderate, and cold environments, and defines them in terms of six basic parameters: air temperature, radiant temperature, humidity, air velocity, clothing worn, and the persons activity. It focuses on the principles and practice of human response, which incorporates psychology, physiology, and environmental physics with applied ergonomics. The text then discusses water requirements, computer modeling, computer-aided design, and current standards. A systematic treatment of thermal environments and how they affect humans in real-world applications, the book links the health and engineering aspects of the built environment. It provides you with updated tools, techniques, and methods for the design of products and environments that achieve thermal comfort.
1,008 citations
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TL;DR: In this paper, the authors present a state-of-the-art study through extensive review of the literature, by establishing links between indoor environmental quality and occupant well-being and comfort.
Abstract: Indoor environmental quality (IEQ) and its effect on occupant well-being and comfort is an important area of study. This paper presents a state of the art study through extensive review of the literature, by establishing links between IEQs and occupant well-being and comfort. A range of issues such as sick building syndrome, indoor air quality thermal comfort, visual comfort and acoustic comfort are considered in this paper. The complexity of the relationship between occupant comfort and well-being parameters with IEQ are further exacerbated due to relationships that these parameters have with each other as well. Based on the review of literature in these areas it is established that design of buildings needs to consider occupant well-being parameters right at the beginning. Some good practices in all these different areas have also been highlighted and documented in this paper. The knowledge established as part of this paper would be helpful for researchers, designer, engineers and facilities maintenance engineers. This paper will also be of great benefit to researchers who endeavour to undertake research in this area and could act as a good starting point for them.
329 citations
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TL;DR: In this article, two typical thermal comfort models, the simple ISO 14505 standard method and the comprehensive UC Berkeley thermal comfort model (UCB model), were coupled to computational fluid dynamic (CFD) numerical simulation with different process to evaluate thermal environment of a small office.
Abstract: Thermal comfort may be achieved more energy-efficiently in non-uniform thermal environments than in uniform ones, and such environments are also frequently transient, so developing a thermal comfort model to evaluate thermal comfort asymmetrical environments or transient conditions has being an hotspot of recent study. This paper first reviews several thermal comfort models that address local thermal sensations and attempts to distinguish these models by their advantages, limitations and suitable ranges of applications. Then, two typical thermal comfort models, the simple ISO 14505 standard method and the comprehensive UC Berkeley thermal comfort model (UCB model), were coupled to computational fluid dynamic (CFD) numerical simulation with different process to evaluate thermal environment of a small office. The results indicated that compared with the UCB model, the ISO 14505 index could be applied with caution as a convenient method to evaluate thermal comfort in non-uniform, overall thermally neutral environments.
164 citations
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TL;DR: A new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely and a new classification algorithm called Fuzzy Rule based Neural Classifier is proposed for diagnosing the disease and the severity.
Abstract: Recently, the mobile health care (m-healthcare) applications with Internet of Things (IoT) are providing the various dimensionalities and the online services. These applications have provided a new platform to the millions of people for getting benefit over the health tips frequently for living a healthy life. After the introduction of IoT technology and the related devices which are used in medical field, strengthened the various features of these healthcare online applications. The huge volume of big data is generated by IoT devices in healthcare environment. Cloud computing technology is used to handle the large volume of data and also provide the ease of use. In this scenario, cloud based applications are playing major role in this fast world. These medical applications are also used the Cloud Computing technology for secured storage and accessibility. For availing better services to the people over the online healthcare applications, we propose a new Cloud and IoT based Mobile Health care application for monitoring and diagnosing the serious diseases. Here, a new framework is developed for the public. In this work, a new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely. In addition, we propose a new classification algorithm called Fuzzy Rule based Neural Classifier for diagnosing the disease and the severity. The experiments have been conducted by the standard UCI Repository dataset and the real health records which are collected from various hospitals. The experimental results show that the performance of the proposed work which outperforms the existing systems for disease prediction.
156 citations
"AQCI: An IoT Based Air Quality and ..." refers methods in this paper
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TL;DR: In this article, the authors examined the issues, infrastructure, information processing, and challenges of designing and implementing an integrated sensing system for real-time indoor air quality monitoring, which aims to detect the level of seven gases, ozone (O fixme 3), particulate matter, carbon monoxide (CO), nitrogen oxides (NO fixme 2), sulfur dioxide (SO petertodd 2), volatile organic compound, and carbon dioxide (CO�� 2), on a realtime basis and provides overall air quality alert timely.
Abstract: With growing transportation and population density, increasing global warming and sudden climate change, air quality is one of the critical measures that is needed to be monitored closely on a real-time basis in today's urban ecosystems. This paper examines the issues, infrastructure, information processing, and challenges of designing and implementing an integrated sensing system for real-time indoor air quality monitoring. The system aims to detect the level of seven gases, ozone (O
3
), particulate matter, carbon monoxide (CO), nitrogen oxides (NO
2
), sulfur dioxide (SO
2
), volatile organic compound, and carbon dioxide (CO
2
), on a real-time basis and provides overall air quality alert timely. Experiments are conducted to validate and support the development of the system for real-time monitoring and alerting.
126 citations
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