Ubiquitous Computing and Distributed Machine Learning in Smart Cities

Abstract

The article is devoted to the analysis of the use of ubiquitous computing and distributed machine learning in smart cities. Smart city is characterized by the introduction of high-tech infrastructure, digital services, integrated information monitoring systems that allow to optimize the environment and processes of urban management. The most promising direction of smart cities development is the implementation of ubiquitous computing systems. Ubiquitous computing involves the introduction of a significant number of technologies, including sensors, artificial intelligence, Internet of Things, network robots. Since ubiquitous computing is based on the processing of data generated by different devices, the new solutions are needed to structure and ensure data compatibility. Such solutions are the distributed machine learning methods: stochastic gradient descent and K-means method. The work separately considers the use of federated training, which has advantages in data privacy and mobile computing. The article deals with the main provisions of the concept of smart city, technologies of ubiquitous computing, features of methods of distributed machine learning and their introduction into urban systems management.