Enhanced meta-heuristic optimization of resource efficiency in multi-relay underground wireless sensor networks

Area of Science:

• Wireless Sensor Networks
• Communication Engineering
• Optimization Algorithms

Background:

• Underground wireless sensor networks (UWSNs) face challenges in energy and spectral resource utilization due to limited battery power and difficult soil environments.
• Extending the operational lifetime of UWSNs is critical for reliable data collection from buried sensors.

Purpose of the Study:

• To develop an improved meta-heuristic algorithm for optimizing energy and spectral resource utilization in multi-relay UWSNs.
• To enhance the Salp Swarm Algorithm (SSA) for efficient data relaying and network lifetime extension in UWSNs.

Main Methods:

• An improved Salp Swarm Algorithm (SSA) was applied to multi-relay UWSNs.
• The algorithm optimizes transmission powers for relay nodes to maximize network resource efficiency.
• Enhancements to the standard SSA include chaotic map initialization and uniform crossover for population updates.

Main Results:

• The proposed algorithm significantly outperforms the standard SSA in resource efficiency and network lifetime.
• Performance gains increase with a higher number of cooperative relay nodes.
• The algorithm demonstrates superior efficiency compared to other meta-heuristic approaches.

Conclusions:

• The enhanced SSA provides an effective solution for optimizing resource utilization and extending the lifetime of UWSNs.
• Cooperative relaying strategies combined with optimized power control are crucial for UWSN performance.
• The proposed method offers a robust and efficient approach for challenging underground communication environments.