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Related Experiment Video

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

Hussein S Mohammed1, Poria Pirozmand2, Sheeraz Memon3

  • 1IT Department, King's Own Institute (KOI), 11 York Street, Sydney, NSW 2000, Australia.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

A new hybrid Bat-Artificial Bee Colony (BA-ABC) algorithm enhances energy efficiency in Wireless Sensor Networks (WSNs). This approach optimizes clustering and routing, significantly extending network lifetime and reducing energy consumption.

Keywords:
artificial bee colonybat algorithmenergy optimizationhybrid BA-ABC algorithmswireless sensor networks

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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.