A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional...

Area of Science:

• Computer Science
• Wireless Sensor Networks
• Optimization Algorithms

Background:

• Coverage control is a fundamental challenge in directional sensor networks.
• Existing methods struggle with geographically irregular events and heterogeneous sensor node properties (sensing radius, field of angle, communication radius).

Purpose of the Study:

• To formulate the coverage optimization problem in directional sensor networks as a multi-objective optimization problem.
• To address challenges posed by irregular event distribution and heterogeneous sensor characteristics.
• To introduce a novel algorithm for efficient and effective coverage control.

Main Methods:

• Formulation of coverage control as a multi-objective optimization problem considering coverage rate, working node count, and network connectivity.
• Introduction of a learning automata-based coral reef algorithm for adaptive parameter selection.
• Application of a Tchebycheff decomposition method to transform the multi-objective problem into a single-objective problem.

Main Results:

• The proposed algorithm effectively balances network coverage, node count, and connectivity.
• Simulation results consistently show the superiority of the developed algorithm compared to existing approaches.
• The method successfully handles geographical irregularity and sensor heterogeneity.

Conclusions:

• The learning automata-based coral reef algorithm provides a robust solution for multi-objective coverage optimization in directional sensor networks.
• The Tchebycheff decomposition method is effective in simplifying complex multi-objective problems.
• This approach offers significant improvements for directional sensor network coverage control.