The 3rd Workshop on Intelligent Mobile Systems based on Internet of Things
Paper submission
All papers accepted for workshops will be included in the EUSPN-2026 proceedings, which will be published by Elsevier.
The authors must follow Elsevier guidelines as given in EUSPN-2026 Website.
The number of pages for workshop papers is limited to 6 pages.
The number of accepted papers is limited to a maximum of 10–12, with an acceptance rate of about 60%.
October 28-30, 2026, Almaty, Kazakhstan
Intelligent Mobile Systems based on the Internet of Things refer to the integration of smart mobile devices, sensors, communication technologies, and intelligent data processing methods to support connected and data-driven services. This combination uses the capabilities of IoT to improve the efficiency, connectivity, automation, and intelligence of mobile systems and applications. Such systems can be applied in different areas, including smart cities, transportation, healthcare, logistics, agriculture, environmental monitoring, and urban mobility. Some of the key aspects include but are not limited to:
• Sensors and Data Collection: IoT-enabled mobile systems use different types of sensors to collect data from the surrounding environment. These sensors may include GPS, accelerometers, gyroscopes, cameras, temperature sensors, humidity sensors, air quality sensors, motion sensors, and other sensing devices. The collected data can be transmitted to a central system, cloud platform, or edge device for further processing and analysis.
• Connectivity: IoT systems rely on connectivity to enable communication between mobile devices, sensors, vehicles, infrastructure, and cloud-based services. Mobile devices play an important role in this ecosystem because they can use wireless technologies such as Wi-Fi, Bluetooth, 4G, 5G, and cellular networks to exchange data in real time.
• Cloud and Edge Computing: The data collected by mobile and IoT devices is often sent to cloud platforms for storage, processing, and analysis. Cloud computing provides scalable resources for handling large amounts of data. At the same time, edge computing allows data to be processed closer to the source, which is important for real-time applications such as traffic monitoring, road incident detection, and intelligent transport systems.
• Intelligent Processing: With the integration of artificial intelligence and machine learning algorithms, mobile systems can process collected data and support intelligent decision-making. This may include predictive analytics, anomaly detection, object recognition, traffic flow analysis, route optimization, and other smart functionalities.
• Computer Vision Applications: Camera-based mobile and IoT systems can be used for road monitoring, vehicle detection, pedestrian detection, traffic congestion analysis, and road safety assessment. Computer vision methods allow systems to analyze visual data and identify important events in urban environments, especially in smart transportation and public safety applications.
• Road and Mobility Optimization: Intelligent mobile IoT systems can support road network optimization by analyzing GPS data, camera data, traffic flow, and real-time mobility patterns. These systems can help identify congested areas, improve route planning, optimize public transport movement, and support smarter traffic signal control.
• Automation and Control: IoT-enabled mobile systems can be used to automate different tasks and control connected devices or infrastructure. For example, a mobile application can help users control smart home devices, monitor industrial processes, manage logistics, track vehicles, or supervise smart city infrastructure in real time.
• Industry Applications: Intelligent mobile systems based on IoT have applications in many industries, including healthcare, transportation, agriculture, logistics, smart cities, and environmental monitoring. For instance, in healthcare, IoT-enabled mobile devices can monitor patients’ health indicators and send real-time data to healthcare providers. In transportation, they can support smart traffic monitoring, road safety analysis, and urban mobility optimization.
• Security Considerations: As with any IoT application, security is a critical consideration. Since intelligent mobile systems collect and transmit large amounts of data, strong security measures are needed to protect sensitive information and prevent unauthorized access to connected devices, mobile applications, and cloud platforms.
Topics
- Evolving Technologies in IoT and Intelligent Mobile Systems
- Cloud and Edge Computing in IoT and Intelligent Mobile Systems
- Data Management in IoT and Intelligent Mobile Systems
- Storage Solutions for IoT and Intelligent Mobile Systems
- Real-time Data Analytics and Predictive Analytics for IoT and Intelligent Mobile Systems
- Machine Learning in IoT and Intelligent Mobile Systems
- Edge AI for IoT and Intelligent Mobile Systems
- Challenges in Scaling IoT and Intelligent Mobile Systems
- Energy Efficiency in IoT and Intelligent Mobile Systems
- IoT and Intelligent Mobile Systems in Smart Cities, Industry 4.0, AgroTech
- Big Data for IoT and Intelligent Mobile Systems
- Impact of 5G on IoT and Intelligent Mobile Systems
- Edge Computing Advancements in IoT and Intelligent Mobile Systems
- Integration of Blockchain in IoT and Intelligent Mobile Systems
- AI-Enhanced Smart Mobility Platform for Predicting and Reducing Urban Congestion
- Multi-Modal Urban Mobility Analytics Using Camera, GPS, and IoT Sensor Data
- Explainable AI for Intelligent Traffic Management and Decision Support
| № | Technical Program committee |
|---|---|
| 1 | Dr. Didar Yedilkhan, PhD, Astana IT University, Kazakhstan |
| 2 | Dr. Beibut Amirgaliyev, PhD, Astana IT University, Kazakhstan |
| 3 | Dr. Nurkhat Zhakiyev, PhD, Harvard University, USA |
| 4 | Dr. Aigul Adamova, PhD, Astana IT University, Kazakhstan |
| 5 | Dr. Andrii Biloshchytskyi, Vice-Rector for Science and Innovations, Astana IT University, Kazakhstan |
| 6 | Dr. Zholdas Buribayev, PhD, Al-Farabi Kazakh National University, Kazakhstan |
| 7 | Dr. Ibraheem Shayea, PhD, Professor of the Faculty of Electrical and Electronic Engineering, Department of Electronics and Communication Technology, Istanbul Technical University, Turkey |
| 8 | Dr. Khaled Rabie, PhD, Faculty of Engineering, Manchester Metropolitan University, UK |
| 9 | Dr. Ainur Zhumadillayeva, Eurasian National University, Kazakhstan |
| 10 | Dr. Aivar Sakhipov, Astana IT University, Kazakhstan |
| 11 | Dr. Akhmet Tussupov, Astana IT University, Kazakhstan |
| 12 | Dr. Akzhibek Amirova, Astana IT University, Kazakhstan |
| 13 | Dr. Ardashir Mohammadzadeh, Sakarya University, Türkiye |
| 14 | Dr. Aruzhan Shoman, Astana IT University, Kazakhstan |
| 15 | Dr. De Mi, Birmingham City University, UK |
| 16 | Dr. Dina Satybaldina, Eurasian National University, Kazakhstan |
| 17 | Dr. Gerald Feldman, Birmingham City University, UK |
| 18 | Dr. Laura Aldasheva, Astana IT University, Kazakhstan |
| 19 | Dr. Mohammad Shojafar, University of Surrey, UK |
| 20 | Dr. Murat Ozer, University of Cincinnati, USA |
| 21 | Dr. Praveen Kumar, Astana IT University, Kazakhstan |
| 22 | Dr. Talgat Islamgozhayev, Astana IT University, Kazakhstan |
| 23 | Dr. Tamara Zhukabayeva, Eurasian National University, Kazakhstan |
| 24 | Dr. Zhanat Karashbayeva, Astana IT University, Kazakhstan |
| 25 | Dr. Zharasbek Baishemirov, Astana IT University, Kazakhstan |
| 26 | Ms. Aidana Zhalgas, Astana IT University, Kazakhstan |
| 27 | Ms. Sabina Saleshova, Astana IT University, Kazakhstan |
| 28 | Mr. Miras Mussabek, Astana IT University, Kazakhstan |
| № | Workshop Organizer |
|---|---|
| 1 | Prof. Didar Yedilkhan, PhD, Head of the Smart City research center, Astana IT University, Kazakhstan |
| 2 | Prof. Beibut Amirgaliyev, PhD, Astana IT University, Kazakhstan |
| 3 | Prof. Nurkhat Zhakiyev, PhD, Harvard University, USA |
| 4 | Prof. Aigul Adamova, PhD, Astana IT University, Kazakhstan |