# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 5 Discussion
### Opportunities for Further Exploration  
AI-based solutions offer promising advances in SSC development, but there are several limitations and challenges that need to be addressed. These include scalability, high initial costs and infrastructure requirements, integration challenges, controlled energy consumption, limited predictive accuracy and technology dependency. Scalability is a significant constraint in sustainable AI-supported smart cities due to the huge number of sensors, which can reach up to 50 million. This enormous scale creates challenges in managing and processing the huge amount of data generated, leading to potential bottlenecks in real-time decision-making. The complexity of coordinating thousands of segments and ensuring seamless communication between them can place a strain on infrastructure and computing resources. Additionally, the cost and energy consumption required to maintain such a large system can be prohibitive. Finally, as the system grows, it becomes increasingly difficult to ensure data security and privacy as more nodes create more points of vulnerability. High initial costs and infrastructure requirements are significant constraints for the development of sustainable AI-supported smart cities, especially in developing countries. These regions often lack the financial resources and existing infrastructure needed to implement advanced technologies, making it difficult to adopt sustainable solutions. The costs of installing sensors, upgrading networks and providing robust data management systems can be prohibitively high, therefore delaying or preventing smart city initiatives. Furthermore, the ongoing costs of maintenance, training and technology upgrades add to the financial burden. As a result, the digital divide between developed and developing regions may widen, exacerbating global inequalities in access to sustainable technology. Integration challenges are a major constraint in sustainable AI-supported smart cities, especially when integrating AI into existing urban infrastructure. Older systems, which are often outdated and incompatible with new technologies, require significant modification or replacement, making the integration process complex and costly. The lack of standardisation between different technologies further complicates matters, as different systems may not communicate effectively or share data seamlessly. These challenges can lead to inefficiencies, delays and increased costs in implementing smart city initiatives. Additionally, the need for expertise to manage these integrations can be a barrier, especially in regions with limited technical resources. Controlled energy consumption is an important constraint in sustainable AI-supported smart cities as ML technologies are often energy-intensive. The computing power required to process large data sets, run complex algorithms and support real-time decision-making can lead to significant energy consumption. This increased energy demand can counteract smart cities' sustainability goals, potentially contributing to an increased carbon footprint. As cities seek to balance the benefits of artificial intelligence with the need for energy efficiency, this paradox challenges overall environmental goals. Furthermore, reliance on energy-intensive technologies may require additional infrastructure to support clean energy sources, thus adding complexity and cost. Limited predictive accuracy is a significant limitation in SSCs supported by artificial intelligence (AI) as machine learning (ML) models can struggle to accurately predict complex urban dynamics. The challenge for these models is often the uncertainty of human behaviour, which can be unpredictable and highly variable in different contexts. Additionally, factors such as changing weather conditions, economic changes and unexpected events further complicate the accuracy of predictions. The inherent limitations of machine learning algorithms in capturing and responding to these dynamic variables can lead to suboptimal decision-making and planning.