About this Research Topic
As a powerful tool for solving complex problems, intelligent computing algorithms are widely known in computer science disciplines and have also been applied to smart cities. Smart cities in the future will require these intelligent algorithms to adapt to network protocols and resource management of different services in different scenarios. These artificial intelligence methods can be widely applied to solve data collection problems in the upper layer of smart cities, such as: urban pollution monitoring, urban noise maps, urban traffic conditions, social networks, and health care, thus yielding significant performance improvements over communication systems designed with traditional methods.
Yet, the evolution towards learning-based smart cities is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. In addition, an in-depth understanding of the basic performance constraints is also essential for the Quality-of-Service assurance of smart cities. The application of ML and AI for smart cities design and optimization should be deeply investigated to make future cities more connected, efficient and intelligent. Thus, the goal of this Research Topic is to explore the optimization for smart cities through the application of ML/ AI. The optimization includes, for example, the data collection, energy efficiency, security, privacy, and energy saving in the smart city.
Topics of interest include (but are not limited to) the following:
• ML/AI based 5G smart cities
• ML/AI based optimization modeling for smart cities
• ML/AI based energy-efficient for smart cities
• ML/AI based IoT scheme for secrecy and privacy for smart cities
• ML/AI based energy-efficient network operations for smart cities
• Resource allocation for shared/virtualized smart cities using machine learning AI and edge computing for smart cities
• ML/AL algorithms and edge networking protocols for smart cities
• ML/AI based edge communications for smart health
• Energy harvesting and learning-based edge communications for smart cities
• Novel smart city applications with ML/AL algorithms
• ML/AI based hardware/software platforms for smart cities
Keywords: Smart Cities, Machine Learning, Deep Learning, Edge Network, Intelligent Networking
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.