# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 2 AI in SSC
### AI in Selected Areas of SSCs
Integrating the IoMT with AI ensures that healthcare is not only personalised but also scalable, meeting the needs of a growing urban population. This collaboration between the IoMT and AI contributes to the city's sustainability goals by improving health outcomes while reducing resource consumption. Additionally, robust cybersecurity measures have been implemented to protect sensitive medical data, and so ensure trust and privacy in the IoMT ecosystem. Ultimately, the combination of the IoMT and AI in a sustainable smart city creates a future where healthcare is efficient, environmentally friendly and highly responsive to the needs of its residents. More effective legacy data acquisition and centralised ML models have less data security and privacy, and mass sensing, data provisioning and SSC service delivery may be more difficult as the population and sensing population grow [23; 24; 25; 26; 27; 28]. Sensors can measure air quality, traffic volume and other features of the urban environment, and in the \"drive-by\" paradigm, sensors can be installed in the vehicles of various companies, thus increasing range and reducing costs. Until now, the main problems have lain in reconciling the massiveness and privacy of data with the required short learning time of AI. It provides a distributed training approach capable of solving the aforementioned problems. Figure 2: IoMT main concept (own version based on [23; 24; 25; 26; 27; 28]). Local data are used to train local models and, in turn, local models are used to update the global model. This aggregated global model is returned to the local models for further training and this procedure is repeated until the global model converges and works to further maintain this convergence, i.e., a common operational picture at all levels [30; 31; 32; 33; 34; 35; 36; 37; 38]. Large volumes of IoMT data can be harnessed without sharing, complex dynamics and complex data-sharing agreements. However, handling data from multiple locations presents another challenge [35; 36; 37; 38]. Deep reinforcement learning (DRL), digital twins and generative adversarial networks (GANs) may be particularly useful here [38; 39; 40; 41; 42]. There is not much research on AI-based data and energy management within energy cooperatives (this is how SSCs should be considered); this area is only just being transformed [43]. In doing so, it is clear that SSCs will face the need for lighting and heating(especially in the global south), e.g., warming as a result of climate change and as a side effect of urbanisation, which will increase the need for cooling. GIS-based urban heat island estimation, energy modelling and rooftop solar potential have been used for residential energy stress (REST) buildings (Amaravati, India). This allows for the application of decentralized optimisation solutions for energy control and peer-to-peer energy sharing on a neighbourhood scale. In this solution, decision tree algorithms based on energy justice variables classify energy grid data in a sustainability framework to alleviate energy stress at the SSC level. This allows an up to 80% reduction in energy consumption in SSCs, including based on the optimisation of planning variables (such as floor area ratio and building density) and optimisation of current energy consumption. Therefore, these indicators must be taken into account when planning the construction of an SSC, and the mere use of AI for SSC management may produce lesser results [44; 45; 46]. The rapid development of modern IT, power, communication and road information systems, etc., develops distributed and energy-saving (if possible, powered by renewable energy sources) elements of the smart grid safely connected with entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence.