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Deep Neural Networks (DNNs) for Sustainable Development in Smart City
Book chapter   Open access

Deep Neural Networks (DNNs) for Sustainable Development in Smart City

Karthikeyan Velayuthapandian, Kirubakaran Ganesan and Ezhil Kalaimannan
Intelligent Computing and Optimization for Sustainable Development, pp.31-54
CRC Press, 1
12/18/2024
DOI: https://doi.org/10.1201/9781032625829-3

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Abstract

To make smart cities more sustainable, deep neural networks (DNNs) assess data from sensors, the Internet of Things (IoT), and social media in an effective manner. Swiftly assessing text, images, and time-series data, they aid administrators in understanding complex urban trends and allocating resources. DNNs employ climate predictions and energy usage patterns to enhance energy distribution, minimize trash, and boost green energy resources. This improves grid reliability and reduces greenhouse gas emissions. DNNs trained on real-time traffic data enhance congestion, transportation efficacy, and traffic flow. Intelligent scheduling techniques enhance transportation, shipment, and fuel economy. DNNs enhance smart city garbage collection routes, recycling opportunities, and on-demand services by assessing past and present data. This strategy encourages reducing trash, recycling, and maintaining a clean atmosphere. Nevertheless, there is an urgent need to address data privacy, computational injustice, and the accessibility of computer resources. Responsible and collaborative decision-making is crucial to address these issues and avoid unfairness. By prioritizing ethics and fair distribution of benefits, DNNs can improve transportation, trash management, and energy efficiency, paving the way for more accessible and inclusive digital cities.
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