# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 5 Discussion
### Opportunities for Further Exploration
Consequently, reliance on AI forecasts can sometimes result in ineffective or inefficient urban management strategies. Dependence on technology is a significant limitation in sustainable AI-enabled smart cities as over-reliance on AI systems can introduce vulnerabilities. In the event of technical failures, cyber-attacks or natural disasters, critical systems can be compromised, leading to disruptions in essential services such as transport, healthcare and utilities. The lack ofhuman oversight and backup mechanisms in highly automated systems can exacerbate this risk, potentially leaving cities unprepared for unexpected events. This dependency highlights the need for robust contingency plans to manage disruptions and ensure continuity of services during emergencies. Without these safeguards, the resilience and sustainability of smart cities could be compromised, undermining their intended benefits. The framework is considered to be at an early stage of development, with challenges primarily related to data origination, data aggregation, scalability and data security, especially in target production versions on SSCs of various sizes (number of inhabitants, data, devices, employees, etc.) and their use cases. The next stage of the research will be to create large data sets and evaluate them in difficult conditions. Processing based on AI integrated with the cloud of data from many personalized IoMT devices (bedside, wearable) must be characterised by high performance to provide the basis for building next-generation intelligent healthcare. The SSC also needs a holistic approach to early detection of pandemics such as COVID-19 based on molecular diagnostics and computer-aided detection (AI-based models) [55; 56; 57; 58; 59]. There is a certain challenge still in collecting and integrating data (e.g., on health) from a wide variety of devices, not only from smart watches, scales and smart shoes but also from thermographic sensors and sensors that examine people's movements as part of screening. Other data are easier to integrate if they are linked in terms of geolocation. Addressing these limitations requires a multidisciplinary approach involving urban planners, policymakers, technologists (including AI specialists) and the public to develop SSC solutions that are inclusive, secure and ethically sound [67; 68; 69; 70]. On the other hand, automated machine learning (AutoML) can lead to high energy consumption due to a large number of calculations and thus can significantly worsen its carbon footprint. For this reason, there is a need to apply energy efficiency metrics to advanced optimisation algorithms within AutoML so that the costs do not exceed the results. These metrics allow the evaluation and optimisation of the algorithm's energy consumption, taking into account accuracy, sustainability and reduced environmental impact with a minimal decrease in validation accuracy (e.g., by 0.5%). ML can be made more sustainable by carefully considering the energy efficiency of computational processes [71; 72]. As biotechnological approaches evolve within smart cities, smart factories and smart territories, these technologies can be integrated with IoT, especially in rain gardens, urban vertical farming systems and urban photobioreactors. Biofuel cells can also be used to power low-power sensor networks or self-powered biosensors (synthetic biology: cell-based biosensors, bioactuators with synthetic genetic circuits as a development direction for cyber-physical system--CPS) [73]. Technological advancement and innovation path reviews provide a methodology for technological improvements and directions for their search. This can be guided by building on R&D strengths and business value, and structured technology exploration can be extended to explore and stimulate other forms of development [74]. It is necessary to carefully consider whether and which industrial and service optimisation processes (including the optimisation of machine resources as part of preventive maintenance) are consistent with sustainable development [75; 76; 77].