Реферат: Smart cities are the backbone of any digital economy. This concept entails the end-to-end integration of IT technologies within a smart city, including tracking passenger traffic, monitoring transport and crime rates, managing household expenses such as heating, water, and electricity, facilitating interaction between citizens and government, providing automatic payroll and taxes, paying for health insurance, interacting with assistants and chatbots, and much more. All these achievements are aimed at improving the quality of life and reliability for ordinary citizens. Successful implementation of these processes requires a robust IT infrastructure that includes computers, sensors, and controllers. This infrastructure generates huge amounts of data that require processing, storage, and analysis. Artificial intelligence algorithms play a pivotal role in addressing these challenges, encompassing pattern recognition, voice recognition, chatbots, and intelligent assistants. These sophisticated mechanisms enable the analysis and processing of the acquired information. In this context, we are introducing a novel blockchain-based Delta platform. This platform enables the creation of digital twins of smart cities, accurately simulating all processes and relationships within them. The Delta platform implements a hierarchical approach to data storage and processing in which multi-blockchains, in combination with other big data stores, serve as the underlying information stores. The work of intelligent systems inside smart cities is described using smart contracts executed within multi-blockchain structures, thereby ensuring efficient and secure functionality. To obtain new knowledge and predictions, we use a specially developed learning theory for intelligent systems based on the requirements of maximum specificity presented in the works of Carl Hempel.

Библиографическая ссылка: Goncharov S. , Nechesov A.
AI-Driven Digital Twins for Smart Cities
В сборнике Proceedings of the 10th International Electronic Conference on Sensors and Applications, 15–30 November 2023, MDPI: Basel, Switzerland. 2023. – C.1-7. DOI: 10.3390/ecsa-10-16223 Scopus OpenAlex

Даты:

Опубликована в печати:	15 нояб. 2023 г.
Опубликована online:	15 нояб. 2023 г.

Идентификаторы БД:

Scopus:	2-s2.0-85186396397
OpenAlex:	W4390758468

Цитирование в БД:

БД	Цитирований
OpenAlex	1
Scopus	2

Альметрики: