Internet of Things Enabled Framework for Sustainable Mobility and Clean Environment in Smart Cities

Kaur, Surleen; Sharma, Sandeep

doi:10.1007/978-3-031-37303-9_21

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Rapid population growth, the inevitability of urbanization, and high mobility have resulted in increased emissions of dangerous air pollutants such as PM2.5 particles, NO2, SO2, and O3 [1]- [3]. This air pollution has a negative influence on human health, the environment, and the economy of the city [4]. A previous study in this area attempted to analyze Jakarta's air pollution using several methodologies [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

AL AZIES

2023

icdsos

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The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like PM10, SO2, NO2, and others, even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article

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Section: Introductionmentioning

confidence: 99%

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

AL AZIES

2023

icdsos

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Shaping Highly Intelligent Internet of Things (IoT) and Wireless Sensors for Smart Cities

Singh,

Jain,

Kaunert

et al. 2024

Advances in Computational Intelligence and Robotics

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The highly intelligent internet of things (IoT) and wireless sensor technologies are set to reshape the directions of safe and intelligent parking and transportation in the dynamic field of urban development. Sustainable and effective transportation solutions are critical as cities struggle with the effects of growing populations and rising urbanization. Sustainability is the key to the transportation of the future as the development of electric mobility solutions and environmentally friendly transportation choices is facilitated by highly intelligent IoT. By reducing the negative effects of transportation systems on the environment, this path hopes to promote a greener and more sustainable urban environment. So, tackling issues with privacy and cybersecurity, this trajectory seeks to increase confidence in the accuracy and privacy of data. This chapter explores how these technologies are developing and how they will affect urban transportation in the future to solve the difficulties of contemporary urban life, a focus is put on security measures, real-time data analytics and sustainable practices.

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Internet of Things Enabled Framework for Sustainable Mobility and Clean Environment in Smart Cities

Cited by 2 publications

References 34 publications

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

Shaping Highly Intelligent Internet of Things (IoT) and Wireless Sensors for Smart Cities

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