Internet of Things (IoT) promises the integration of the physical world into computer-based systems, infrastructures, and platforms. Straight applications of IoT leads to several Quality of Service (QoS) issues and pitfalls based on constraints such as intermittent connectivity of mobile devices, limited performance of inexpensive hardware, and availability of resources in emergency response scenarios. The study course addresses the studies on service systems performance modelling. Students gain the knowledge and elaborate skills by studying methodologies for analysis and performance assessment of general and IoT networks. Students learn by applying Queueing Theory techniques in practice, and by using mass service systems (MSS) models and Petri Nets in their work.
Course contents:
- Introductory lecture. Aim of the study course, goals, and methodology.
- Statements of the research problems, tasks, simulation methods and tools.
- Traffic and queueing tasks, methods, tools, and decision-making models.
- Queueing modeling, analysis, simulation, and prediction.
- Queueing algorithms (in-depth analysis), their assessment methods and rankings.
- Routing algorithms in distributed networks: methods and protocols.
- Resource management in IoT networks, methods, modelling principles and tools.
- The principles of secure networking in sensor networks.
- Introduction to machine learning in sensor networks: tasks, methods, and tools.
- Security aspects of the IoT systems. Attack schemes, tasks, methods, and tools.
- Closing lecture. Advanced tasks and topics.
