Real-Time Machine Learning for Air Quality and Environmental Noise Detection

Shah, Sayed Khushal; Tariq, Zeenat; Lee, Jeehwan; Lee, Yugyung

doi:10.1109/bigdata50022.2020.9377939

Cited by 30 publications

(14 citation statements)

References 24 publications

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“…We proposed two unique models for lightweight deep learning for the prediction of air quality in multiple cities on the edge. The proposed deep-learning model is more accurate than traditional machine-learning models, as shown in our previous study [ 1 ]. Furthermore, the lightweight design and event-based sensor fusion approach are suitable for edge intelligence.…”

Section: Discussionmentioning

confidence: 57%

“…The comparative evaluation with state-of-the-art research in air-quality prediction was also conducted (see Table 7 ). In our previous work [ 1 ], the air-quality level was predicted using three machine-learning algorithms, support vector machine (SVM), random forest, and decision tree with the Seoul AQI dataset (2014–2020) [ 44 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The Internet of Things (IoT) and artificial intelligence play a crucial part in enhancing the quality of care and sustaining urban growth. Social identity is one of the critical determinants for user satisfaction and ecological convenience when taking into consideration various environmental criteria such as greenhouse gases, air temperature, relative humidity, wind speed, and noise levels [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Shah

Tariq

Lee

et al. 2021

Sensors

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Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.

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Section: Discussionmentioning

confidence: 57%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Shah

Tariq

Lee

et al. 2021

Sensors

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“…Combined with simplicity and timesaving, the model can be applied to some simple detection, search, and rescue devices. For example, the model is introduced into an intelligent machine that can monitor air quality and noise levels [11] . Taking air pollution and noise as stimulus sources, the intelligent machine can find neighborhoods that exceed the preset standards.…”

Section: Discussionmentioning

confidence: 99%

Phototropic bionics: realization of intelligent machine detection and obstacle avoidance

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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The research on the detection function and obstacle avoidance function of automatic intelligent machine has become the development trend of contemporary scientific research. To realize the detection and efficient obstacle avoidance of intelligent machines in unknown environments, it is necessary to find a suitable and simple algorithm, which can be applied to most cases. Inspired by the principle of plant phototropism and Braitenberg Vehicle. Obstacles are likened to light sources. Through detecting the distance between obstacles and intelligent machines, then connect the detector directly to the motor. The motor is regulated by transmitting Positive and negative signal from detector that are similarly used as auxin, thus realizing the detection and obstacle avoidance. The experimental results show that the algorithm based on the above design can effectively complete the unknown environment detection work and achieve efficient obstacle avoidance.

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“…Regarding the processors used in edge-IoT nodes for air quality, the concept of single-board computer (SBC) has been widely spread, and they are usually implemented with Raspberry devices (Pi 3 and Pi 4 models, typically) based on ARM processors (Kiruthika and Umamakeswari, 2018;Kumar and Jasuja, 2017;Zhang et al, 2021), Arduino devices (Uno model, typically) based on Atmel microcontrollers (Abraham and Li, 2014;Firdhous et al, 2017;Karami et al, 2018), system-on-a-chip (SoC) devices such as ESP32 (Nasution et al, 2020;Tas ¸tan and Gökozan, 2019) and ESP8266/NodeMCU (Siva Nagendra Reddy et al, 2018), and integrated wireless sensor modules (IWSM) that embed in the same board a low-cost microcontroller, a RF circuit and some sensors (Kim et al, 2017). In other IoT implementations, more powerful processing devices are added to the edge to reduce computing times in the ML model implementation, such as GPUs, in which the Nvidia manufacturer has a clear dominance with models such as Jetson Nano (Shah et al, 2020) and GeForce RTX (Cosoli et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

Gamazo-Real,

Torres Fernández,

Murillo Armas

2023

Engineering Applications of Artificial Intelligence

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Real-Time Machine Learning for Air Quality and Environmental Noise Detection

Cited by 30 publications

References 24 publications

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Phototropic bionics: realization of intelligent machine detection and obstacle avoidance

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

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