Actor Placement Optimization in WSANs by the PSO-HC-DGA Hybrid System for Two-Zone Industrial Environments

Area of Science:

• Computer Science
• Engineering
• Automation

Background:

• Wireless Sensor and Actor Networks (WSANs) are vital for Industry 4.0 industrial automation.
• Optimal actor placement for connectivity and coverage in WSANs is an NP-hard problem.

Purpose of the Study:

• To address the Actor Placement Problem (APP) in constrained, two-zone industrial environments.
• To propose and evaluate a novel hybrid system for improved WSAN deployment.

Main Methods:

• A hybrid system integrating Particle Swarm Optimization (PSO), Hill Climbing (HC), and Distributed Genetic Algorithm (DGA) was developed.
• Four crossover methods (UNDX, SPX, BLX-α, psBLX) and two replacement methods (RIWM, FC-RDVM) were evaluated.
• Performance was assessed across small-, medium-, and large-scale industrial scenarios.

Main Results:

• The psBLX crossover method demonstrated superior performance across various scenarios.
• The hybrid system achieved 100% connectivity in large-scale tests.
• Statistically significant improvements in load balancing and sensor coverage were observed compared to a standard PSO system.

Conclusions:

• The proposed PSO-HC-DGA hybrid system, particularly with the psBLX method, offers a robust and high-performance solution for industrial WSANs.
• This approach effectively addresses connectivity and coverage challenges in two-zone industrial environments.