Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Telecommunications

Background:

• Radio access networks face evolving demands, shifting focus from capacity to data freshness.
• Exponential traffic growth necessitates advanced solutions like multiple radio access technology (multi-RAT).
• Internet of Things (IoT) networks require efficient energy use and timely data delivery.

Purpose of the Study:

• Investigate multi-RAT for reducing user transmission delay.
• Preserve Quality of Service (QoS) while maintaining information freshness using the Age of Information (AoI) metric.
• Develop a framework for forecasting network performance in multi-hop and cellular coordinated systems.

Main Methods:

• Modeled a queuing system encompassing network and MAC layers.
• Investigated coordination between multi-hop and cellular networks.
• Developed a framework with models and tools for performance forecasting (end-to-end delay and AoI).

Main Results:

• Comprehensive simulations were conducted to validate the proposed framework.
• The framework effectively forecasts network performance metrics.
• Numerical results highlight the benefits of the multi-RAT approach for delay and freshness.

Conclusions:

• Leveraging multi-RAT can significantly reduce transmission delays in IoT networks.
• The proposed framework provides valuable insights into network performance tuning and optimization.
• The study demonstrates a viable approach to balancing QoS, delay, and data freshness in modern networks.