Energy-Efficient Optimization in Wireless Sensor Networks Using a Hybrid Bat-Artificial Bee Colony Algorithm

Area of Science:

• Computer Science
• Electrical Engineering
• Network Optimization

Background:

• Wireless Sensor Networks (WSNs) face critical challenges with limited node energy and network lifetime degradation.
• Efficient resource utilization is crucial for the sustainability of WSNs in various applications.

Purpose of the Study:

• To introduce a novel hybrid Bat-Artificial Bee Colony (BA-ABC) algorithm for energy-efficient optimization in WSNs.
• To address the dual objectives of optimizing clustering and routing processes for enhanced network performance.

Main Methods:

• Integration of the Bat Algorithm (BA) for local convergence and Artificial Bee Colony (ABC) for global exploration.
• Development of an adaptive multi-objective fitness function to balance energy consumption, network lifetime, and communication efficiency.
• Simulation and statistical validation using MATLAB R2024a.

Main Results:

• The BA-ABC algorithm demonstrated significant improvements over conventional methods.
• Achieved reductions in total energy consumption (22-30%), improvements in network lifetime (18-25%), and latency reduction (approx. 24%).
• Validated robustness, stability, and consistency through statistical analysis.

Conclusions:

• The BA-ABC algorithm offers a computationally efficient and scalable solution for WSN optimization.
• It provides high performance without excessive overhead, suitable for resource-constrained environments.
• The framework is adaptable for real-world applications like smart cities and healthcare systems.