An Efficient Secure and Adaptive Routing Protocol Based on GMM-HMM-LSTM for Internet of Underwater Things
Rongxin Zhu, Azzedine Boukerche, Qiuling Yang
Abstract
The growing significance of marine information in the context of increased human oceanic activities has fostered interest in marine exploration. However, the specific nature of underwater acoustic communication, characterized by propagation delays and fluctuating link quality, presents multifaceted challenges to Internet of Underwater Things (IoUT). The vast openness of the underwater environment further amplifies security vulnerabilities, emphasizing the imperative for secure network routing. This paper introduces GHL-SAR, a fortified routing paradigm to address these challenges. Central to GHL-SAR’s design is its ability to evaluate node trustworthiness based on energy, communication, and node trust. Within this model, the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) serves as a predictor of potential hidden state sequences, while the Long Short-Term Memory (LSTM) elucidates the relationship between these states and trust levels. Moreover, GHL-SAR deploys an adaptive routing mechanism rooted in the Particle Swarm Optimization Algorithm (PSOA), judiciously weighing link quality for routing decisions. The protocol further advances a density-based spatial clustering method for effective trust evidence aggregation. The simulation results demonstrate that GHL-SAR significantly reduces packet loss and energy consumption, while ensuring detection accuracy and network security compared to other existing routing protocols.