Integration of deep learning with edge computing on progression of societal innovation in smart city infrastructure: A sustainability perspective

Abstract

According to statistical data from international organizations, the rising global population is intensifying the several critical challenges particularly in smart cities including air pollution, carbon emissions, traffic congestion, and healthcare infrastructure. Researchers have concluded that these challenges present significant barriers to meeting the United Nations’ sustainable development goals (SDGs) by 2030, hindering efforts toward a sustainable future and enhancing overall quality of life. Recent studies have concluded that the enabling technologies of Industry 4.0 have the potential to overcome the aforementioned challenges and establish sustainable infrastructure in smart cities. Edge computing and deep learning are two transformative technologies that have shown substantial outcomes in meeting the targets of SDG 3: Good health and well-being, SDG 9: Industry, Innovation, Infrastructure and SDG 11: Sustainable cities and communities. This study highlight the importance of sustainability, with specific focus on SDG goals 3.8, 3.9, 9.1, 9.4, 11.3, and 11.6, providing a technical comparative analysis of the integration of deep learning and edge computing technologies in healthcare, unmanned aerial vehicles (UAVs,) and other smart city applications. The study also investigates the challenges associated with various parameters and provides recommendations for future advancement, with plans to explore additional SDGs in future research.

Keywords

• Edge computing
• Deep learning
• Digitalization
• Industry 4.0
• Sustainable infrastructure