Indoor air quality monitoring using wireless sensor network

Bhattacharya, Sayantani; Sridevi, S.; Pitchiah, R.

doi:10.1109/icsenst.2012.6461713

Cited by 132 publications

(72 citation statements)

References 3 publications

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“…In controlled laboratory tests, low-cost particle sensors have shown high linearity and stability in comparison with commercial instruments with a known particle size and composition (Wang et al, 2015;Manikonda et al, 2016;Sousan et al, 2016Sousan et al, , 2017Zikova et al, 2017). Several studies in the literature have reported the combination of low-cost particle sensors with "smart" home devices (e.g., temperature, humidity, carbon monoxide sensors, cameras) to provide more comfortable and energy-efficient homes and workplaces (Ivanov et al, 2002;Chung and Oh, 2006;Kim et al, 2010;Bhattacharya et al, 2012;Kim et al, 2014). Moreover, a few studies also applied multiple sensors for outdoor or indoor air quality measurements (Rajasegarar et al, 2014;Patel et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Optical Characterization Studies of a Low-Cost Particle Sensor

Biswas

2017

Aerosol Air Qual. Res.

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Compact low-cost sensors for measuring particulate matter (PM) concentrations are receiving significant attention as they can be used in larger numbers and in a distributed manner. Most low-cost particle sensors work on optical scattering measurements from the aerosol. To ensure accurate and reliable determination of PM mass concentrations, a relationship of the scattering signal to mass concentration should be established. The scattering signal depends on the aerosol size distributions and particle refractive index. A systematic calibration of a low-cost particle sensor (Sharp GP2Y1010AU0F) was carried out by both experimental and computational studies. Sodium chloride, silica, and sucrose aerosols were used as test cases with size distributions measured using a scanning mobility particle sizer (SMPS). The mass concentration was estimated using the measured size distribution and density of the particles. Calculations of the scattered light intensity were done using these measured size distributions and known refractive index of the particles. The calculated scattered light intensity showed better linearity with the sensor signal compared to the mass concentration. To obtain a more accurate mass concentration estimation, a model was developed to determine a calibration factor (K). K is not universal for all aerosols, but depends on the size distribution and refractive index. To improve accuracy in estimation of mass concentration, an expression for K as a function of geometric mean diameter, geometric standard deviation, and refractive index is proposed. This approach not only provides a more accurate estimation of PM concentration, but also provides an estimate of the aerosol number concentration.

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Section: Introductionmentioning

confidence: 99%

“…By developing a real-time air quality monitoring system, the HVAC system can operate more efficiently. Therefore, distributed and real-time particle concentration measurements are necessary to identify hotspots indoors and provide information for the HVAC system (Kim et al, 2010;Bhattacharya et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Optical Characterization Studies of a Low-Cost Particle Sensor

Biswas

2017

Aerosol Air Qual. Res.

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“…Since humans spend significant amount of time (nearly 90%) indoors [13] human occupant comfort is also an important requirement of building HVAC. Furthermore, a comfortable work environment can improve productivity [14].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Wijayasekara

Manic

Rieger

2013

2013 6th International Symposium on Resilient Control Systems (ISRCS)

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Abstract-Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount of information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.

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“…In response to increased demands for sustainability, and as buildings become more complex, integrated systems, sensor networks are increasingly being used to observe, monitor and control heating, ventilation and air-conditioning systems [18]. In new commercial buildings, monitoring and control systems are, as a rule, included as a necessary part of the construction.…”

Section: Environmental Sensor Networkmentioning

confidence: 99%

Enabling commodity environmental sensor networks using multi-attribute combinatorial marketplaces

Lee

Alrawahi

Toohey

2013

2013 19th Asia-Pacific Conference on Communications (APCC)

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Abstract-Large scale distributed e-infrastructures are emerging as commodity resource platforms. The next generation of commodity e-infrastructures will encapsulate the physical or tangible world by integrating ubiquitous sensors. Cheap environmental and physiological sensors are being increasingly deployed by many commercial organisations. The process of discovering and accessing commercially available resources requires a market for providers and consumers to trade these resources. This paper argues that developing a market will encourage the commoditisation of environmental sensor networks. It presents an overall architecture and adopts algorithms to support the trading of commodity environmental sensor networks.

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Indoor air quality monitoring using wireless sensor network

Cited by 132 publications

References 3 publications

Optical Characterization Studies of a Low-Cost Particle Sensor

Optical Characterization Studies of a Low-Cost Particle Sensor

Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Enabling commodity environmental sensor networks using multi-attribute combinatorial marketplaces

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