School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
PLoS One. 2023 Sep 5;18(9):e0289306. doi: 10.1371/journal.pone.0289306. eCollection 2023.
The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.
水下声传感器网络（UASN）是一个大型网络，其中传输节点的附近由许多工作传感器节点组成。由于水下声信道的时变和空变特性，通信过程可能会受到很大干扰。因此，水下声通信系统需要通过自适应调制来降低干扰，提高通信效果和质量。为了解决这个问题，本文旨在通过基于长短期记忆（LSTM）的能量预测，提出一种在 UASN 中进行最优路径选择和安全数据传输的模型。通过最优路径在 UASN 中传输安全数据的模型涉及两个主要阶段。首先，根据吞吐量方面的能量、距离和链路质量等约束条件选择节点。此外，利用 LSTM 预测能量，并利用提出的混合优化算法——Pelican 更新狒狒优化算法（PUCOA）选择最优路径，该算法是 Pelican 优化算法（POA）和狒狒优化算法（COA）的组合。然后，通过使用所提出的改进的 Blowfish 算法（IBFA）对数据进行加密，通过最优路径安全地传输数据。最后，使用标准基准模型对所开发的 LSTM+PUCOA 模型进行验证，结果表明，所提出的 LSTM+PUCOA 模型的性能达到了 90.85%的准确率、92.78%的精度、91.78%的特异性、89.79%的灵敏度、7.21%的 FPR、89.76%的 F1 得分、89.77%的 MCC、10.20%的 FNR、92.45%的 NPV 和 10.22%的 FDR，学习百分比为 70。
