Development of Intelligent Models and Methods for the Digital Smart City Ecosystem to Ensure Sustainable Urban Development and Improve Citizens’ Quality of Life

Project Manager: Didar Yedilkhan

Funding source: PAF KS MSHE OF RK

Funding volume: 208 million tenge

Years of implementation: 2024 – 2026

Brief description of the project: The project is aimed at creating an integrated digital platform for a smart city (Smart City), ensuring efficient collection, storage and analysis of city data to improve sustainable development and quality of life of citizens

In the context of growing urbanization and the burden on infrastructure, cities need digital transformation. Challenges include: resource management, security, overload of communication networks, environmental and social problems. The Smart City digital ecosystem provides a comprehensive solution to them.

Creation of an integrated digital platform for a smart city with the implementation of advanced technologies for data collection and analysis, 5G networks, video surveillance systems, vertical farming and wireless network security.

1. Development of a Smart City digital platform;
2. Increasing the capacity and reliability of 5G in urban conditions;
3. Video surveillance system with facial recognition;
4. Integration of vertical farming into the urban environment;
5. Methodology of IoT and network cybersecurity;
6. Data visualization and recommendations for the city administration;
7. Scientific publications (11+ WoS/Scopus, 12+ KOKNVO), 1 monograph, 4 patents;
8. Training of 12 masters and 4 PhD.

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.