EQARO-ECS: Efficient Quantum ARO-Based Edge Computing and SDN Routing Protocol for IoT Communication to Avoid...

Area of Science:

• Environmental Science
• Computer Science
• Artificial Intelligence

Background:

• Desertification, driven by factors like temperature and humidity, degrades fertile land.
• Internet of Things (IoT) architectures and AI-driven routing are crucial for desertification monitoring.
• Classical AI algorithms face challenges with local optima and high energy consumption.

Purpose of the Study:

• To propose an improved multi-objective routing protocol for IoT networks to monitor and prevent desertification.
• To enhance energy efficiency and data analysis speed in IoT networks for environmental monitoring.
• To address the limitations of traditional AI algorithms in desertification management.

Main Methods:

• Developed the efficient quantum (EQ) artificial rabbit optimisation (ARO) based on edge computing (EC) and software-defined networking (SDN) (EQARO-ECS).
• Integrated cloud, SDN, EC, and quantum-enhanced ARO for optimized IoT network routing.
• Utilized iterated quantum gates within ARO for faster convergence to global optimum and an objective function considering energy, communication cost, and desertification parameters.

Main Results:

• The EQARO-ECS protocol significantly reduces energy consumption in IoT networks.
• Data analysis speed is markedly improved due to the quantum-enhanced ARO.
• The protocol demonstrates increased accuracy and extended network lifetime compared to existing algorithms.

Conclusions:

• The EQARO-ECS protocol offers an effective solution for monitoring and combating desertification using advanced IoT and AI.
• This approach enhances the efficiency and longevity of IoT networks for environmental applications.
• The integration of edge computing, SDN, and quantum-inspired optimization provides a robust framework for sustainable land management.