AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture

Area of Science:

• Computer Science
• Network Engineering
• Machine Learning

Background:

• Smart agricultural systems utilize sensor networks and IoT devices to gather environmental data for improved farming efficiency.
• Traditional data processing methods struggle with the complexity and dynamism of data in smart agricultural systems.
• Optimizing traffic routing in Software-Defined Networks (SDN) is crucial for efficient data management and resource allocation.

Purpose of the Study:

• To propose a novel framework integrating Machine Learning (ML) and Reinforcement Learning (RL) for optimizing traffic routing in SDN.
• To classify data traffic into emergency, normal, and on-demand categories using various ML models.
• To enhance network performance by reducing latency and improving resource allocation, especially for time-sensitive traffic.

Main Methods:

• Employed ML models including Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) for traffic classification.
• Utilized the Q-learning (QL) algorithm, a basic RL version, within the SDN paradigm for optimizing routing based on classified traffic.
• Evaluated the performance of ML models, noting superior accuracy from Random Forest (RF) and Decision Trees (DT).

Main Results:

• The proposed ML and RL integrated framework effectively optimizes traffic routing in SDN environments.
• Random Forest (RF) and Decision Trees (DT) demonstrated higher accuracy in classifying traffic data compared to other ML models.
• The integration significantly improves resource allocation, reduces network latency, and prioritizes the delivery of emergency traffic.

Conclusions:

• The combined ML-based data classification and QL algorithm offer a robust solution for dynamic traffic routing in SDN.
• SDN's adaptability allows routing algorithms to adjust to real-time network conditions and traffic characteristics.
• This approach is vital for enhancing the efficiency and responsiveness of smart agricultural systems and other data-intensive applications.