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

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networ...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 19; s. 8218
Hlavní autoři: Masood, Fahad, Khan, Wajid Ullah, Jan, Sana Ullah, Ahmad, Jawad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2023
MDPI
Témata:
Agricultural industry
Agricultural production
Agriculture
Algorithms
Communication
Communications networks
Data analysis
Data processing
Decision making
Distance learning
emergency/critical data
Internet of Things
IoT
Machine learning
Network security
Optimization
reinforcement learning
SDN
Sensors
smart agriculture system
Software
Soil moisture
Traffic control
Traffic regulations
Trends
United Kingdom
China
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23198218