Article: Optimised route selection for multipath routing in MANET using latent encoder coupled generative adversarial network Journal: International Journal of Bio-Inspired Computation (IJBIC) 2025 Vol.25 No.4 pp.247 - 258 Abstract: Mobile ad hoc networks (MANETs) enable decentralised communication in environments without traditional infrastructure, such as disaster zones and military settings. However, they face challenges like high latency and energy inefficiency in route selection, affecting overall network performance. To address this, an optimised route selection for multipath routing in MANET using latent encoder coupled generative adversarial network (LECGAN-RS-MR-MANET) is proposed. Initially, the tyrannosaurus optimisation algorithm (TOA) is employed for efficient route exploration, followed by LECGAN for optimal node selection through latent feature learning. The improved sooty tern optimisation algorithm (ISTOA) ensures effective route maintenance, while the multitask multi-attention residual shrinkage convolutional neural network (MMRSCNN) handles route recovery. The proposed LECGAN-RS-MR-MANET is evaluated and demonstrates 23.92% lower energy consumption, 24.92% lower end-to-end delay, and 22.42% lower routing overhead compared to existing techniques. These findings confirm that the proposed model significantly improves routing performance and reliability in MANETs. Inderscience Publishers - linking academia, business and industry through research

Title: Optimised route selection for multipath routing in MANET using latent encoder coupled generative adversarial network

Authors: Surulivelu Muthumarilakshmi; Hemanand Duraivelu; Manikandan Subramanian

Addresses: Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai-600 069, India ' Department of Computer Science and Engineering, S.A. Engineering College, Thiruverkadu, Chennai-600 077, Tamil Nadu India ' Department of Information Technology, K.Ramakrishnan College of Engineering, Samayapuram, Trichy 621112 Tamil Nadu, India

Abstract: Mobile ad hoc networks (MANETs) enable decentralised communication in environments without traditional infrastructure, such as disaster zones and military settings. However, they face challenges like high latency and energy inefficiency in route selection, affecting overall network performance. To address this, an optimised route selection for multipath routing in MANET using latent encoder coupled generative adversarial network (LECGAN-RS-MR-MANET) is proposed. Initially, the tyrannosaurus optimisation algorithm (TOA) is employed for efficient route exploration, followed by LECGAN for optimal node selection through latent feature learning. The improved sooty tern optimisation algorithm (ISTOA) ensures effective route maintenance, while the multitask multi-attention residual shrinkage convolutional neural network (MMRSCNN) handles route recovery. The proposed LECGAN-RS-MR-MANET is evaluated and demonstrates 23.92% lower energy consumption, 24.92% lower end-to-end delay, and 22.42% lower routing overhead compared to existing techniques. These findings confirm that the proposed model significantly improves routing performance and reliability in MANETs.

Keywords: latent encoder coupled generative adversarial network; tyrannosaurus optimisation algorithm; TOA; mobile ad hoc networks; MANETs; improved sooty tern optimisation algorithm; ISTOA.

DOI: 10.1504/IJBIC.2025.146917

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.4, pp.247 - 258

Received: 23 Apr 2024
Accepted: 25 Jan 2025
Published online: 26 Jun 2025 *

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Title: Optimised route selection for multipath routing in MANET using latent encoder coupled generative adversarial network

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