Title: Deep learning-based energy prediction in wireless sensor networks

Authors: Manikandan Selvaraj; Suganthi Santhanam

Addresses: Department of Computer Engineering, Kongunadu Polytechnic College, Tiruchirappalli, Tamilnadu, India ' Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Tiruchirappalli, Tamilnadu, India

Abstract: A wireless sensor network (WSN) plays a major role in network protection. In WSN, the data is routed towards the sink node and attack detection is the key process of WSN. In this research, the Taylor Walrus optimisation algorithm (Taylor WaOA) enabled LeNet model (Taylor WaOA-LeNet) is developed for attack detection. Here, the WSN is simulated and the deep Q network (DQN) is used for predicting the energy. The proposed Taylor WaOA is utilised in routing using fitness factors like energy, throughput, trust and distance. In base station (BS), the feature selection and data augmentation are carried out in attack detection; the proposed Taylor WaOA-LeNet model is employed. Furthermore, the energy, throughput, distance, trust, accuracy, sensitivity, and detection rate metrics are used to evaluate the model performance, which offered the finest values of 0.984 J, 0.073 Mbps, 67.08 m, 0.157, 0.918, 0.947, and 0.958.

Keywords: attack detection; Taylor series; wireless sensor networks; WSN; Walrus optimisation algorithm; WaOA; deep learning; DL; deep Q network; DQN.

DOI: 10.1504/IJBIC.2024.141691

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.3, pp.176 - 190

Received: 31 Jan 2024
Accepted: 22 May 2024
Published online: 30 Sep 2024 *

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