Data verification methods in smart cities at the intersection of artificial intelligence distributed registries and network theory

Abstract

This article is devoted to the study of data verification methods in smart cities using the synergy of artificial intelligence, distributed registries and network theory. The urgency of the issue is determined by the increasing complexity of urban infrastructure and the need to ensure the security, transparency and reliability of information coming from multiple sensors and devices within the framework of the smart city concept. The introduction substantiates the need to develop new methods of data verification to improve the sustainability and effectiveness of urban management systems. It is noted that traditional solutions often cannot cope with the scale and dynamics of modern urban space, where data is generated in real time and requires rapid analysis and authentication. The Methods section describes the approaches used, including machine learning algorithms for information processing and analysis, distributed ledger protocols that ensure data immutability, as well as network analysis methods that identify key nodes and connections in urban infrastructure. A comparative analysis of the characteristics of the proposed methods and technologies was carried out, which made it possible to form a unified methodological verification model. The "Results" section demonstrates how the use of the integrated model improves the reliability of smart city systems. Based on experimental data, it is shown that using a combined approach can significantly reduce the number of information falsifications, as well as optimize distributed data processing processes. The main patterns and advantages associated with the synergy of artificial intelligence technologies, blockchain solutions and network theory have been identified. The discussion focuses on the prospects of integrated solutions for the sustainable development of the urban environment, as well as discusses challenges and possible areas for further research. The authors offer recommendations on how to implement the developed methods into real urban systems, which will ensure a more flexible, reliable and self-regulating smart city infrastructure. This work can become the basis for developing new standards for data verification and improving interaction between information components of the urban environment.