Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field...

Area of Science:

• Computer Science
• Network Engineering
• Game Theory

Background:

• The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a standard for energy management in Wireless Sensor Networks (WSNs).
• LEACH faces limitations in network longevity and performance due to energy consumption imbalances.
• Optimization of energy efficiency is crucial for extending the operational lifespan of WSNs.

Purpose of the Study:

• To enhance the energy efficiency and extend the network lifetime of Wireless Sensor Networks (WSNs).
• To address the energy consumption imbalances inherent in existing protocols like LEACH.
• To introduce and evaluate a novel protocol, MFG-LEACH, leveraging Mean Field Game (MFG) theory.

Main Methods:

• Developed the MFG-LEACH protocol, integrating Mean Field Game (MFG) theory into the LEACH framework.
• Modeled node interactions as a game, enabling individual nodes to optimize transmission energy based on network-wide conditions.
• Conducted extensive simulations to compare MFG-LEACH against established protocols: EZ-SEP, EAMR, and BRE.

Main Results:

• MFG-LEACH demonstrated significant reductions in overall energy consumption compared to benchmark protocols.
• The proposed protocol led to a notable increase in the number of successfully received packets.
• Performance improvements were consistent across various node density scenarios.

Conclusions:

• MFG-LEACH effectively optimizes energy consumption in WSNs, outperforming existing methods.
• The integration of Mean Field Game (MFG) theory provides a robust framework for enhancing WSN performance and longevity.
• MFG-LEACH offers a promising solution for energy-constrained wireless sensor network applications.