Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring

Jin, Ming; Liu, Shichao; Schiavon, Stefano; Spanos, Costas J.

doi:10.1016/j.buildenv.2017.11.003

Cited by 115 publications

(50 citation statements)

References 40 publications

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“…IAQ monitoring is a trending topic for which some other low-cost and open-source monitoring systems have been developed [43][44][45][46][47][48][49]. Due to the quality and relevant contribution of these solutions, a brief summary of these studies is presented in The RSSI test at floor level showed that the maximum distance between nodes was 8 m. The RSSI test at 1 m height reported 3 to 5 m distance.…”

Section: Discussion and Resultsmentioning

confidence: 99%

“…The system is composed of several sensor nodes that incorporate CO 2 sensors and upload the IAQ information to a remote server for data visualization. An indoor autonomous mobile robot system for environmental quality supervision enabled by a sensor-rich navigation-capable robot to actively survey the indoor space is proposed by [46]. The proposed automated mobile system incorporates CO 2 , light, volatile organic compounds (VOC), and temperature sensors; it uses Wi-Fi communication technology.…”

Section: Related Workmentioning

confidence: 99%

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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: Discussion and Resultsmentioning

confidence: 99%

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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“…Humans spend nearly 90% of their time indoors or in other enclosed environments [1,2], creating significant health concerns associated with the inhalation of indoor airborne particles. Infectious respiratory droplets or droplet nuclei exhaled from inflected occupants can serve as pathogen carriers [3].…”

Section: Introductionmentioning

confidence: 99%

An improved particle swarm optimization method for locating time-varying indoor particle sources

Feng

Cai

et al. 2019

Building and Environment

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A B S T R A C TThe indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

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“…In modern society, people spend as much as 90% of their time in indoor environments [1] . With the improvement of living standards, people are increasingly aware of the importance and urgency of indoor air pollution control.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation

Feng

Cai

Chen

et al. 2019

Energy and Buildings

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a b s t r a c tSource localization is critical to ensuring indoor air quality and environmental safety. Although considerable research has been conducted on source localization in steady-state indoor environments, very few studies have dealt with the more challenging source localization problems in dynamic indoor environments. This paper presents a comprehensive particle swarm optimization (CPSO) method to locate a contaminant source in dynamic indoor environments with mechanical ventilation and develops a multi-robot source localization system to experimentally validate the method. Three robots were used to test the presented method in a typical dynamic indoor environment with periodic swinging of the air supply louvers of a cabinet air conditioner. The presented method was validated with two typical source locations, DS (in the downwind zone) and RS (in the recirculation zone). For DS and RS, 15 and 14 experiments out of 15 experiments were successful, with success rates of 100% and 93.3%, and each robot moved an average of 24.4 and 23.6 steps, respectively. The presented method was also compared with the standard particle swarm optimization (SPSO) and wind utilization II (WUII) methods for locating the source at DS. For the SPSO and WUII methods, only 3 and 6 experiments out of 15 experiments were successful, with success rates of 20% and 40% and averages of 33.0 and 38.0 steps, respectively. The experimental results show that the presented method not only has a much higher success rate than the SPSO and WUII methods but also has higher source localization efficiency.

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Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring

Cited by 115 publications

References 40 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

An improved particle swarm optimization method for locating time-varying indoor particle sources

Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation

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