Research and Development of Delay-Sensitive Routing Tensor Model in IoT Core Networks

Area of Science:

• Computer Science
• Network Engineering
• Telecommunications

Background:

• Internet of Things (IoT) networks require efficient routing protocols to manage increasing data traffic.
• Ensuring Quality of Service (QoS) parameters such as low latency and minimal packet loss is critical for IoT applications.
• Existing routing models may not adequately address the delay-sensitive nature of core IoT network traffic.

Purpose of the Study:

• To develop and present an improved delay-sensitive routing tensor model specifically for the core of IoT networks.
• To formulate the delay-sensitive routing problem as an optimization task with defined constraints.
• To investigate the model's effectiveness in enhancing QoS indicators, particularly average end-to-end delay.

Main Methods:

• Utilizing a flow-based tensor model within a coordinate system of interpolar paths and internal node pairs.
• Formulating the routing problem as an optimization task with specific constraints and conditions.
• Employing a system of optimality criteria to guide the routing solution and resource utilization.

Main Results:

• The improved tensor model demonstrates applicability to IoT architectures for ensuring QoS.
• The model effectively addresses bandwidth, average end-to-end delay, and packet loss probability.
• Numerical research validated the model's features and the adequacy of its multipath routing solutions.

Conclusions:

• The developed delay-sensitive routing tensor model offers enhanced performance for IoT networks.
• The model contributes to optimizing network resource usage and improving key QoS metrics.
• The findings support the model's suitability for multipath routing in complex IoT environments.