Future of Smart Cities: The Role of Machine Learning and Artificial Intelligence

I. V. D. Srihith, I. Kumar, R. Varaprasad, Y. R. Mohan, T. Srinivas, Y. Sravanthi
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引用次数: 3

Abstract

The goals of "smart cities" include relieving the burden of growing urbanisation, cutting down on energy use, protecting the environment, boosting the local economy and people's standard of living, and facilitating wider access to and use of cutting-edge Information and Communication Technologies (ICT). In smart cities, ICT is essential for policymaking, decision making, plan execution, and the delivery of useful services. The primary goal of this analysis is to investigate the part that AI and machine learning play. Examples of ed tech include Deep Reinforcement Learning (DRL) and Machine Learning (ML). In a complicated smart city setting, the aforementioned methods can be used to develop the best possible rules. Smart transportation, cyber-security, energy-efficient usage of smart grids (SG), efficient use of Unmanned Aerial Vehicles (UAVs) to guarantee the best 5G and beyond 5G (B5G) communications, and a smart health monitoring are all discussed in length in this paper. Finally, we discuss the research challenges that have yet to be met and potential future research directions that could bring the concept of a "smart city" closer to fruition.
智慧城市的未来:机器学习和人工智能的作用
“智慧城市”的目标包括减轻日益增长的城市化负担,减少能源使用,保护环境,促进当地经济和人民生活水平,促进更广泛地获取和使用尖端信息和通信技术(ICT)。在智慧城市,信息通信技术对于政策制定、决策制定、计划执行和提供有用服务至关重要。本分析的主要目标是研究人工智能和机器学习所起的作用。教育技术的例子包括深度强化学习(DRL)和机器学习(ML)。在复杂的智慧城市环境中,可以使用上述方法来制定最佳规则。本文详细讨论了智能交通、网络安全、智能电网(SG)的节能使用、无人机(uav)的高效使用以保证最佳的5G和超5G (B5G)通信以及智能健康监测。最后,我们讨论了尚未解决的研究挑战以及可能使“智慧城市”概念更接近实现的潜在未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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