Smart cities have transformed how citizens relate to their city and environment. Technology and the increasingly powerful connections offered by the Internet, as well as the generation and interpretation of data (big data), helps cities optimize their operations, which translates into taking better care of the people’s comfort, economy, and environment.

Sensorization has played a fundamental role in the collection of data, which, once analyzed in the Internet of Things (IoT) and smart city platforms, has allowed for the optimization of multiple decisions in terms of governance and optimal resources. FOG and EDGE computing applications are new alternatives for scalable and huge computing that are accepted worldwide. Increasing complex infrastructure, however, leads to various issues concerning the performance questions of local and centralized systems.

This Special Issue aims to find and invite new research from the field of nature-inspired and future generation machine learning algorithms to improve the performance of FOG and EDGE computing applications such as in healthcare, smart homes, vehicular networks, electricity grids, smart city infrastructure, and more. This Special Issue is looking for mathematical models and optimization approaches to study and improve the performance of smart city applications. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Prediction models using artificial intelligence (AI)/machine learning (ML) for smart cities
• Energy optimization for smart homes and buildings using ML
• Nature-inspired models for EDGE computing
• Optimization models for vehicular networks in smart cities
• ML models for prediction
• Smart health care optimization models
• Smart traffic optimization
• Optimization models for cloud services in smart cities
• Energy-aware optimization models for FOG and EDGE computing
• Smart water management using AI and ML