Project Manager: Didar Yedilkhan
Funding source: PAF KS MSHE OF RK
Funding volume: 1,297,826,018.46 Tenge
Years of implementation: 2024 – 2026
Project Description: This project aims to develop an intelligent digital Smart City ecosystem designed for the sustainable development of urban environments and to improve the quality of life for citizens amidst rapid urbanization. The fast-growing population in megacities places increasing pressure on infrastructure, resources, and security, necessitating the implementation of innovative solutions in city management.
The digital Smart City ecosystem created within this project will integrate advanced technologies in data analysis, machine learning, the Internet of Things (IoT), cloud, and telecommunications solutions. This will ensure the efficient collection, processing, analysis, and visualization of multi-domain urban data.
The project holds high relevance in the context of modern urban development, where ensuring the sustainable functioning of infrastructure and enhancing resident comfort have become essential. These issues demand innovative approaches to urban environment management. The research will focus on developing comprehensive solutions, including the analysis of scientific trends, the creation of new methods for big data collection and processing, ensuring the information security of wireless connections, and improving network infrastructure for high throughput and reliability of telecommunication networks. Special attention will be given to developing a video stream analysis system using deep learning to enhance citizen safety.
The primary goal of this research program is to develop a digital Smart City ecosystem for the sustainable development of the city and to improve the quality of life for its citizens. The program’s results will provide recommendations for the administration of major cities in the Republic of Kazakhstan on creating an urban environment that supports sustainable and prosperous development, while also increasing comfort and well-being for all residents.
For 2024:
For 2025:
For 2026:
For 2024:
1.1 Comparative analysis of urban data collection methods was conducted, with an emphasis on transport and multi-domain sources.
2.1 Planning and analysis for improving 5G reliability in dense urban areas were completed.
2.2 The MobTest simulation model for evaluating network quality was developed and tested.
2.3 AI models for mobility management and big data analysis were researched and implemented.
3.1 A review of machine learning algorithms for facial recognition was conducted.
3.2 A model for data collection and preparation for video surveillance systems was developed.
4.1 A digital vertical farming system with sensors and cloud analytics was implemented.
4.2 Cultivation and collection of beet and tarragon microgreens samples were performed.
5.1 Wireless network vulnerabilities were analyzed, and testing methods were proposed.
5.2 Attacks on IoT networks were simulated; vulnerabilities were identified, and protective measures were proposed.
6.1 A review of solutions for building a Smart City platform was completed.
6.2 The architecture of the Smart City platform was developed.
7.1 6 articles were published in journals recommended by the CQAEAS.
For 2025:
1.2 A database of multi-domain transport data in a unified format was created.
2.4 A communication quality monitoring environment was implemented, and an AI sample was prepared.
2.5 AI models for predicting and optimizing network transmission were implemented.
3.3 Neural network architectures for accurate facial recognition were optimized.
4.3 Physicochemical parameters and safety of microgreens were investigated.
4.4 Up to 10 cultivation cycles were conducted on the vertical farm, and samples were collected for analysis.
5.3 A methodology for assessing the security of IoT networks using graphs was developed.
5.4 A tool for wireless network penetration testing was created.
6.3 A prototype of the Smart City digital platform’s core was created.
7.2 11 articles were published in CQAEAS journals from January to June 2025.
7.3 5 articles were published in Scopus/Web of Science (Q1/Q2) publications.
7.4 4 software product certificates were registered in the Republic of Kazakhstan.
Successful defense of 1 PhD dissertation was confirmed.
Successful defense of 7 Master’s dissertations was confirmed.
Results achieved: By publications:
13 articles published in journals included in the list of KOKNVO
2 articles — in international journals with Scopus / WoS indexes (Q1/Q2)
10 publications presented at IEEE conferences
Patents:
3 of the 4 planned author’s certificates have been registered
Staff training:
1 PhD dissertation has been successfully defended
Objective of Work Program №1: Research and development of methods for efficient collection, storage and analytical processing of multi-domain smart city data.
Results of Work Program №1:
A comparative analysis of existing methods for collecting big city data was conducted with a focus on transport data and related Smart City data.
A conceptual and physical architecture of multi-domain smart city data was developed.
Scientific publications were submitted describing the application of various machine learning models to the problem of predicting bus arrival times..
Objective of Work Program №2: Research and develop efficient artificial intelligence (AI) solutions to ensure stable and high-speed access provided by 5G and 6G cellular networks in ultra-dense and smart cities.
Results of Work Program №2:
The goal of the work program №3: Development and optimization of deep learning algorithms for the implementation of a video surveillance system with a face recognition function, providing high accuracy and speed of identification in real time in order to improve safety in public places
Results of the work program №3:
A study and review of machine learning algorithms and methods for face recognition was conducted in order to identify their advantages and disadvantages;
A study and development of a structural model for collecting and preparing big data for machine and deep learning was conducted;
A structural model of a system for access control in a university dormitory and passenger recognition was developed.
The article “Comprehensive evaluation of real-time object detection algorithm based on extended criteria” was published in the KazATK Bulletin
Scientific articles were submitted to peer-reviewed scientific journals indexed in Scopus, WoS [Q1-Q2].
Goal of Work Program №4: Development and optimization of integrated vertical farming systems aimed at sustainable urban agricultural production
Results of Work Program №4:
The goal of Work Program №5: Research of penetration testing methods and vulnerability analysis of wireless networks in the context of digital urban ecosystems, in order to ensure a high level of data security and prevent unauthorized access to the information resources of the urban infrastructure.
Results of Work Program №5:
The goal of work program №6: to create a single digital platform for a smart city capable of integrating data from various city systems, ensuring their collection, analytical processing, predictions and visualization. This will improve the efficiency of city resource management, improve interaction with citizens and ensure sustainable development of urban infrastructure.
Results of work program №6: