Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoT).
• A key challenge in WSNs is achieving adequate coverage due to uneven sensor node distribution.
• Existing optimization algorithms struggle to efficiently address WSN coverage limitations.

Purpose of the Study:

• To propose a novel enhanced Whale Optimization Algorithm (WOA-LFGA) for optimizing WSN coverage.
• To improve the global and local search capabilities of the standard WOA.
• To validate the effectiveness of WOA-LFGA in WSN coverage optimization.

Main Methods:

• Integration of Lévy flight for enhanced global search and convergence speed.
• Incorporation of a genetic algorithm mechanism for improved local and random search.
• Testing WOA-LFGA on 29 mathematical optimization problems and a WSN coverage model.

Main Results:

• WOA-LFGA demonstrated highly competitive performance against mainstream optimization algorithms.
• The algorithm showed significant improvements in WSN coverage optimization.
• Simulation results confirmed the algorithm's practicality and effectiveness.

Conclusions:

• The enhanced WOA-LFGA algorithm offers a superior solution for WSN coverage optimization.
• The integration of Lévy flight and genetic algorithms effectively addresses WSN limitations.
• WOA-LFGA provides a robust and efficient method for improving IoT network performance.