Abstract
The context of smart cities should really be anchored onto two key attributes. First is the ability for a city to learn adaptively with the aid of machine learning (ML) or artificial intelligence (AI) and second is the ability for a city to sustain operations autonomously without any human intervention. While Internet of Things (IoT) is seen as an enabler by making all devices connect to a network that communicates with one another with minimal human interference; however, critical problems such as sewer management, health, parking woes, traffic congestion, pollution, waste management, and noise are not fully being addressed. In this paper, we discussed the most recent literature for smart city initiatives across the globe including the comprehensive Alcatel–Lucent study in 2011 and proposed an overarching autonomous learning city baseline and target architecture with specific functionalities for each layer.
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Muthaiyah, S., Zaw, T.O.K. (2021). Autonomous and Adaptive Learning Architecture Framework for Smart Cities. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15-5566-4_41
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