Cognitive radio and spectrum sensing techniques for sustainable marine ecosystem monitoring

Introduction Efficient monitoring of marine ecosystems requires reliable and energy-efficient wireless communication to support large-scale underwater sensor networks. Cognitive radio-based Internet of Underwater Things (IoUT), particularly when integrated with non-orthogonal multiple access (NOMA), enables dynamic spectrum access but faces challenges from Rayleigh and Rician fading conditions. Spectrum sensing (SS) is therefore critical for ensuring robust and efficient spectrum utilization. Methods This work evaluates two spectrum sensing techniques for underwater cognitive radio systems: Double Match Filter (DMF) with a fixed probability of false alarm (Pfa = 0.5) and Energy Spectrum Sensing (ESS) with Pfa < 0.5. Both methods were analyzed under Rayleigh and Rician channels to reflect dynamic marine environments. Key performance metrics—probability of detection (Pd), Pfa, and bit error rate (BER)—were simulated and compared against conventional SS and Match Filter methods. Results The proposed DMF–ESS approach achieves superior detection accuracy and communication reliability, offering higher Pd for equivalent Pfa levels and lower BER across a range of SNR conditions. These gains are consistent across both fading models. Discussion By enhancing detection performance and energy efficiency, the DMF–ESS framework improves the reliability and scalability of underwater IoUT networks. This supports real-time monitoring of water quality, biodiversity, and pollution, contributing to more effective marine ecosystem management.