Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Effective coverage and extended network lifetime are critical challenges in Wireless Sensor Networks (WSNs).
• Existing algorithms often struggle to balance multiple optimization objectives simultaneously.

Purpose of the Study:

• To propose a multi-objective coverage optimization algorithm for WSNs named MOCADMA.
• To enhance network coverage, node utilization, and residual energy while prolonging network lifetime.

Main Methods:

• MOCADMA models WSN coverage as a multi-objective optimization problem.
• It employs a memetic algorithm with a dynamic local search strategy.
• Solutions are represented in matrix form and optimized through evolutionary processes (selection, crossover, mutation, local enhancement, fitness evaluation).

Main Results:

• MOCADMA demonstrates strong capabilities in maintaining sensing coverage.
• The algorithm achieves higher network coverage and improved energy efficiency.
• It effectively prolongs the operational lifetime of WSNs.

Conclusions:

• MOCADMA offers a significant improvement over existing algorithms for WSN coverage optimization.
• The proposed method successfully balances multiple critical objectives for WSNs.
• It provides a robust solution for enhancing WSN performance and longevity.