Smart sensor architecture selection for coastal marine monitoring

Marine pollution in coastal areas poses increasing threats to aquatic ecosystems, necessitating the deployment of efficient, real-time monitoring systems. This study addresses the critical challenge of selecting the most effective sensor network architecture for such monitoring by applying the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Three alternative configurations, namely fixed buoy systems, hybrid mobile gliders with satellite relays, and IoT-enabled underwater sensor grids, are evaluated using five criteria: detection accuracy, coverage area, operational cost, energy consumption, and system resilience. Expert opinions from 30 professionals across multiple domains were collected via linguistic surveys, converted into triangular fuzzy numbers, and aggregated into a fuzzy decision matrix. Results indicate that the hybrid glider-satellite architecture achieved the highest closeness coefficient (0.68), balancing accuracy, adaptability, and sustainability. The findings underscore the value of integrating expert input and fuzzy logic into multi-criteria decision frameworks for designing intelligent marine pollution monitoring systems.