Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Abstract

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.

Enhancing underwater target localization through proximity-driven recurrent neural networks

“…At each time step, the input layer will accept data sequences, and the RSSI readings collected from anchor nodes will be of particular interest. To record hidden states throughout time, an equation incorporating hyperbolic tangent activations and weight matrices is utilised, along with the linkages that connect the input to hidden states and hidden states to other hidden states that are related to those states [ [40] , [41] , [42] , [43] ]. The output layer can create forecasts by using the hidden state as well as a softmax operation to standardize the probability distribution.…”

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

“…The finite-element method is commonly used for calculations involving a wide range of physics fields. When this switch is turned on, the software can do error control utilising a number of numerical solvers as well as a constrained component analysis on a userdefined lattice [38]. When the switch is in the "on" position, all of this is possible.…”

Analysis and Application of the Electromagnetic Wave Propagation, Fields, and Dispersion Relation in an Elliptical Chiro Plasma Waveguide Immersed in Magnetized Plasma

“…"Energy balanced reliable and effective clustering for underwater wireless sensor networks" have been studied (Kaveripakam & Chinthaginjala, 2023a). "Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction" have been investigated (Kaveripakam & Chinthaginjala, 2023b). Furthermore, "underwater wireless sensor network performance analysis using diverse routing protocols" has been studied (Sathish, Ravikumar, et al, 2022).…”