Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

Abstract

This research addresses the sensor placement optimization problem (SPOP) in the industrial sector, aiming to enhance operational efficiency and safety through the strategic deployment of sensors. The focus is on optimizing the locations of thermal cameras and motion sensors, with a dual objective of maximizing coverage and minimizing redundancy in the production hall. To solve this challenge, a method based on the application of the nature-inspired bat algorithm was employed. The study reveals noteworthy findings, emphasizing the proficiency of the bat algorithm (BA) in optimizing the placement of thermal cameras and motion sensors. Numeric outcomes demonstrate the algorithm’s effectiveness in maximizing machine coverage while minimizing sensor usage within a real-world industrial environment. These results underscore the versatility and reliability of the BA, establishing it as a valuable tool for addressing complex optimization tasks in industrial settings.

Keywords

• bat algorithm (BA)
• sensor placement optimization problem (SPOP)
• sensor network
• swarm intelligence
• nature-inspired algorithms
• thermal camera