Quantum adaptive clonal genetic algorithm for low-energy clustering in agricultural WSNs

Area of Science:

• Computer Science
• Agricultural Engineering
• Artificial Intelligence

Background:

• Agricultural Wireless Sensor Networks (AWSNs) are vital for precision farming but face limited node energy and battery replacement challenges.
• Extending the operational lifetime of AWSNs is critical for sustainable and efficient smart agriculture.

Purpose of the Study:

• To develop an energy-efficient clustering algorithm for AWSNs using a novel optimization approach.
• To enhance the stability and longevity of AWSNs through intelligent energy management.

Main Methods:

• Proposed a Quantum Adaptive Clonal Genetic Algorithm (QACGA) integrating quantum-inspired operators and dynamic adjustment of genetic parameters.
• Incorporated multi-objective constraints including node distribution, residual energy, and communication distance for cluster-head selection.

Main Results:

• QACGA demonstrated significant energy savings, up to 38.1% compared to PSO, SFLA, and WOA.
• Outperformed the MRCH algorithm in energy efficiency under equivalent conditions.
• Improved clustering stability and extended the overall network lifetime across various deployment scenarios.

Conclusions:

• QACGA offers a robust and practical optimization framework for energy management in AWSNs.
• The proposed algorithm sets new benchmarks for energy efficiency in smart agriculture applications.
• QACGA provides valuable insights for enhancing the performance and sustainability of precision farming technologies.