Enhanced secure path selection model for underwater acoustic sensor networks using advanced machine learning and optimization techniques

Abstract

The underwater acoustic sensor network is a large network consisting of many operating sensor nodes that surround a transmitting node. The communication process faces substantial disturbances caused by the everchanging nature of the underwater acoustic channel, which is characterized by fluctuating properties in both time and location. Therefore, the underwater acoustic communication system has difficulties in reducing interference and improving communication efficiency and quality by using adaptive modulation. This work presents a model that aims to tackle these difficulties by suggesting an optimum route selection and safe data transmission
approach in UASN using sophisticated technology. The suggested approach for transferring safe data in UASN via optimum route selection consists of two main stages. Nodes are first chosen based on restrictions such as energy, distance, and connection quality, which are quantified in terms of throughput. Moreover, the process of forecasting energy is made easier by using sophisticated machine learning methods like transformer models. The ideal route is generated using a hybrid optimization technique called enhanced swarm optimization, which combines ideas from particle swarm optimization and genetic algorithms. Afterward, data is safely transported via the most efficient route by using fully homomorphic encryption. Finally, the ESO+ transformer model that was created is tested against established benchmark models, showcasing its strong and reliable performance. The proposed model demonstrates remarkable performance with an accuracy of 95.12%, precision of 94.83%, specificity of 93.65%, sensitivity of 95.28%, false positive rate of 4.72%, F1 score of 94.95%, Matthews correlation coefficient of 94.85%, false negative rate of 4.72%, negative predictive value of 95.15%, and false discovery rate of 5.15% when trained on a learning percentage of 70%.