GHL-SAR: Secure and Adaptive Routing Based on GMM-HMM-LSTM for UASNs

Underwater acoustic sensor networks (UASNs) have emerged as a promising technology for marine exploitation. However, due to the challenging characteristics of the underwater communication, including propagation delay and unstable link quality, UASNs face numerous issues. Furthermore, the open nature of the underwater environment poses significant security threats, making secure routing in UASNs a crucial requirement. In this paper, we propose a novel and secure routing approach, named GHL-SAR, to address these challenges. GHL-SAR models and measures the trust level of nodes by considering energy trust, communication trust, and node trust. Specifically, Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) is employed to predict the most likely hidden state sequence for a given observation sequence, and then Long Short-Term Memory (LSTM) is used to determine the relationship between the hidden state and the trust level. Moreover, GHL-SAR utilizes an adaptive routing method based on the Particle Swarm Optimization Algorithm (PSOA), which takes into account the accurate link quality to determine the routing strategy. The simulation results demonstrate that GHL-SAR significantly reduces packet loss and energy consumption while ensuring network security compared to other existing routing algorithms.