Designing Edge Architectures for Underwater Sensor Networks to Enable Realtime Data Processing in Extreme Environments

Underwater Sensor Networks USNs play a critical role in environmental monitoring marine exploration and defense applications. However traditional cloudbased data processing introduces significant latency and energy consumption making realtime decisionmaking challenging in extreme underwater environments. This paper proposes a novel edge computing architecture tailored for USNs enabling localized realtime data processing and anomaly detection. The architecture integrates a CNNLSTM deep learning model optimized for lowpower edge devices significantly reducing the need for cloudbased processing. Our experimental evaluation demonstrates a 39 reduction in latency and a 36 improvement in energy efficiency compared to cloudbased solutions. Additionally we present performance benchmarks showing a higher packet delivery ratio and improved data throughput. The proposed approach enhances the autonomy and efficiency of underwater sensor networks making it a viable solution for realtime applications in extreme environments.