Sailfish Optimizer Algorithm for Effective Toxic Gas Detection Sensor Placement in IIoT

Toxic gas detection in industrial contexts is a key safety problem, necessitating the strategic positioning of sensors for efficient monitoring. However, the ideal placement of these sensors is a difficult optimisation problem due to factors such as gas dispersion patterns, facility layout, and economic considerations. Traditional methodologies frequently fail to provide efficient solutions, resulting in suboptimal sensor combinations and possible safety risks. This study presents an innovative solution to this problem using the Sailfish Optimizer algorithm within the context of the Industrial Internet of Things (IIoT). The Sailfish Optimizer Algorithm (SOA), inspired by sailfish cooperative hunting behaviour, provides a novel solution to optimisation issues that involve exploring and exploiting search space. The programme quickly navigates across solution spaces by simulating the coordinated movement of sailfish, seeking optimal configurations that minimise the risk caused by poisonous gases while maximising coverage and lowering expenses. By comparing the performance of the Sailfish Optimizer algorithm to standard optimisation techniques, we demonstrate its superiority in achieving optimal sensor combinations. Furthermore, we assess the algorithm’s capacity to solve real issues including scalability, adaptability, and integration with current IIoT infrastructure.