Adaptive AI-Enhanced Signal Processing Framework for Optimized Communication in the Internet of Underwater Things (IOUT)

The Internet of Underwater Things (IoUT) is an emerging revolution in underwater monitoring and communication, offering real-time detection of objects and data collection in harsh aquatic conditions. Severe communication problems, including signal attenuation, high latency, limited bandwidth, and noise/interference vulnerability, reduce underwater network performance and impede IoUT deployment. The proposed work introduces three new techniques to tackle these problems. The first work introduces the Sequential Memory Fusion Network (SMFN) for predicting environmental changes using historical and real-time data and predicting factors such as signal degradation and noise levels. In the second work, the Adaptive Condition-Based Binary Phase-Shift Keying system (ACB-BPSK) dynamically switches between modulation schemes of high-noise and low-noise modes with the help of real-time feedback to achieve signal robustness and optimized data rates. ACB-EBPSK handles adaptation in modulation techniques according to environmental conditions for enhancing wireless communication’s reliability and efficiency. The third contribution is an Energy-Efficient Reinforcement Learning (EERL) framework for routing decisions, which learns from network feedback, packet loss, and congestion-allows inserting energy into the routing policy and prolonging the lifetime of battery-operated devices considering the dynamic natures of network conditions. The developed techniques’ advantages include increased communication reliability, higher data transmission rates, and prolonged network lifetime.