Intelligent models and methods of Smart City digital ecosystem for sustainable development and the citizens’ quality of life improvement

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. 

Relevance of the project:

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. 

Project Goal:

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. 

Expected results:

  1. For 2024: 

    1. Comparative analysis of existing methods for collecting large urban data, focusing on transport data and associated Smart City data. 
    2. Initiation and planning of the task to increase the throughput and reliability of 5G networks in dense urban areas: defining project goals, scope, and results. Comparative literature review report. 
    3. Design and development of simulation models: data collection and analysis, pre-processing of datasets with required characteristics for training artificial intelligence models. 
    4. AI models for HOP reduction and minimization of handover failures in high-mobility scenarios in ultra-dense 5G and 6G cellular networks. 
    5. Research and review of existing machine learning algorithms and methods for facial recognition to identify their advantages and disadvantages: review of machine learning algorithms and methods for facial recognition. 
    6. Structural data collection model for collecting and preparing large volumes of data. 
    7. Experimental studies on optimizing integrated vertical farming systems based on growing beet and tarragon microgreens. 
    8. Material obtained through vertical farming and collection of beet and tarragon microgreens samples for further analysis and research. 
    9. Review and comparative analysis of penetration testing methods and vulnerability analysis of wireless networks in the context of digital city ecosystems, aiming to ensure a high level of data protection and prevent unauthorized access to urban infrastructure information resources. 
    10. Analysis of wireless network vulnerability databases for the Internet of Things (IoT), modeling of attacks in wireless networks based on analysis of Smart City information and communication infrastructure data and IoT device vulnerabilities. 
    11. Review and comparative analysis of technologies and solutions for developing a comprehensive Smart City ecosystem platform. 
    12. Architecture of a comprehensive digital Smart City platform for data collection and analytical processing. 
    13. At least 3 articles in journals recommended by the Committee for Quality Assurance in Education and Science (CQAEAS). 

     

    For 2025: 

    1. Collection of multi-domain data with a focus on transport data: Multi-domain data, database. 
    2. Unified data architecture that will integrate various sources, ensuring data integrity, security, and accessibility. 
    3. Optimal mobility management solutions that address handover management issues in ultra-dense networks. 
    4. Optimization models and algorithms that are best suited for various network deployment models and system settings of 5G/6G cellular networks. 
    5. Intelligent solutions to find a harmonious balance between minimizing network complexity and maximizing handover management efficiency in 5G/6G network environments. 
    6. Deep neural network architectures for facial recognition. 
    7. Deep neural network models, a system for fast image processing. 
    8. Physicochemical indicators, safety indicators of beet and tarragon microgreens. 
    9. Cultivation of material using vertical farming and collection of beet and tarragon microgreens samples for further analysis and research. 
    10. Experimental studies on the development of beverage technology from beet and tarragon microgreens. 
    11. Research on the physicochemical, microbiological, and technological properties of the developed products. 
    12. Research on developing a methodology for assessing the security of wireless IoT networks using graphs and vulnerability taxonomy. Completion form: methodology for assessing the security of wireless IoT networks. 
    13. Research on developing a method and tool for effective wireless network penetration testing. Completion form: method and tool for wireless IoT network penetration testing. 
    14. Comprehensive digital Smart City platform for data collection and analytical processing. 
    15. At least 5 articles in journals recommended by the CQAEAS. 
    16. At least 7 articles or reviews in peer-reviewed scientific publications in the program’s scientific field, indexed in Q1-Q2 by impact factor in the Web of Science database and/or having a CiteScore percentile in the Scopus database of at least 50. 
    17. At least 2 patents in foreign patent offices (European, American, Japanese) or at least 4 foreign or international patents included in the Derwent Innovations Index database (Web of Science, Clarivate Analytics), or at least 4 intellectual property objects (patent; for information technology applications – copyright certificate) registered at the National Institute of Intellectual Property of the Republic of Kazakhstan. 

     

    For 2026: 

    1. Methods for efficient collection, storage, and analysis of multi-domain data. 
    2. Integration of artificial intelligence algorithms to enhance performance in 5G and 6G network handover management processes, leveraging AI’s adaptation and learning capabilities to improve network performance, reduce outage probability, and enhance user experience. 
    3. Dynamic AI-based solutions for managing radio station mobility in high-speed scenarios within small cell coverage areas. 
    4. Validation and verification of developed models. 
    5. Algorithms and methods for fast searching in biometric databases. 
    6. Conducting experiments and testing developed models and algorithms on real video streams: results of experiments and tests of developed algorithms. 
    7. Experimental studies on the development of beverage technology from beet and tarragon microgreens. 
    8. Salt substitute technology based on Salicornia plant raw materials. 
    9. Draft regulatory and technical documentation for new products. 
    10. Pilot industrial approbation of the developed product’s production. 
    11. Testing and analysis of the effectiveness of the method and tool for wireless network penetration testing. Technical documentation and an application for copyright registration of the developed solutions will be prepared. Completion form: testing methodology, testing program, testing act, quantitative assessments of the method and tool’s effectiveness in laboratory studies and real-world testing, technical documentation. 
    12. Research on developing practical recommendations for wireless network and IoT device developers and administrators on using the penetration testing method and tool. 
    13. Integrated comprehensive digital Smart City platform for data collection and analytical processing. 
    14. At least 4 articles in journals recommended by the CQAEAS. 
    15. At least 4 articles or reviews in peer-reviewed scientific publications in the program’s scientific field, indexed in Q1-Q2 by impact factor in the Web of Science database and/or having a CiteScore percentile in the Scopus database of at least 50. 
    16. At least 1 monograph or textbook in foreign or Kazakhstani publishing houses recommended by the applicant organization’s academic council and/or scientific and technical council. 
    17. At least 2 patents in foreign patent offices (European, American, Japanese) or at least 3 foreign or international patents included in the Derwent Innovations Index database (Web of Science, Clarivate Analytics), or at least 3 intellectual property objects (patent; for information technology applications – copyright certificate) registered at the National Institute of Intellectual Property of the Republic of Kazakhstan. 
    18. Successful defense of at least 12 Master’s and 4 PhD theses on the research topic. 

Results achieved

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:

  • Comparative analysis of existing mobility resilience optimization methods, load balancing management and handover decision models based on AI technologies was conducted.
  • Developed a monitoring system for assessing 5G network performance
  • Developed a robust and adaptable comprehensive network assessment tool, the Mobtest mobile application, supporting advanced 5G deployment planning and optimization, ensuring high network performance for a variety of applications and user needs.
  • Submitted scientific publications describing real-time cellular network assessments: Mobtest for 5G and emerging 6G technologies​

 

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:

  • An IoT system for monitoring and managing the microclimate of a vertical farm was created, using sensors to collect data on temperature, humidity, light level, pH, and electrical conductivity.
  • A vertical farm based on hydroponic technologies was built, which minimized the use of water and soil, as well as provided optimal conditions for growing micro plants and root crops
  • A Streamlit dashboard was created to display data in real time. Data collected from sensors is transmitted via Azure IoT Hub and stored in the cloud, which ensures minimal latency in visualization
  • Scientific publications describing the use of artificial intelligence and IoT in vertical farming were submitted​

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:

  • A methodology for a comprehensive security audit of wireless networks, including LoRa and Wi-Fi standards for smart city digital ecosystems, was developed.
  • Experiments were conducted on active and passive testing of wireless networks, which allowed us to identify vulnerabilities and determine practical measures to eliminate them.
  • A utility was created to automate the penetration testing process, which speeds up and simplifies security auditing.
  • An approach was developed to detect intrusions using edge computing and machine learning algorithms, which increased the effectiveness of threat monitoring.
  • Scientific articles describing new methods of penetration testing and vulnerability analysis for urban digital infrastructure were prepared and published.
  • Practical recommendations are provided to improve the security of wireless networks, thereby contributing to the development of more secure and sustainable digital ecosystems of smart cities.

 

 

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:

  • An analysis of existing smart city platforms was conducted, the technologies used (IoT, Big Data, AI, ML) for collecting, transmitting, storing and analytically processing data coming from various city systems (infrastructure, transport, ecology, etc.) were studied.
  • The operating principles of the intelligent components of the platform were studied based on machine learning and artificial intelligence methods for forecasting and optimizing urban processes, such as transport management, energy saving and environmental monitoring.
  • A cloud infrastructure was created and configured to host the OSM mapping service, taking into account the requirements for computing power and scalability. This includes choosing a cloud provider (e.g. AWS, GCP, Azure), deploying virtual machines (VM), configuring network parameters and load balancing.
  • Collected information on key social facilities in Astana, including schools, kindergartens, hospitals, clinics, cultural and government institutions, to improve the accuracy of navigation and route planning.
  • Prepared for collecting data on bus stops and routes
  • Integrated with the state address register.
  • Developed a two-way geocoding service.
  • Developed a real-time bus tracking service.
  • Developed a dynamic route change service.

More about the project: https://www.qna.kz/  

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