Title: Routing and trust management in MANET using hybrid crayfish white shark optimisation

Authors: Moresh Madhukar Mukhedkar; Vaishali Satish Jadhav; Priyanka Dhondiraj Halle; Uttam Popat Waghmode; Nitin Ashok Dawande; Pallavi Vasant Sapkale

Addresses: Department of Electronics & Telecommunication Engineering, D. Y. Patil University, Ambi, Talegaon Dabhade, Pune 410507, Maharashtra, India ' Department of Electronics Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed University, Dr. D.Y. Patil Vidyanagar, Sector-7, Nerul Navi-Mumbai 400706, India ' Department of Information Technology, SKN Sinhgad Institute of Technology & Science, Sinhagad campus, Kusgaon, Lonavala, Kurvande, Maharashtra 410401, India ' Department of Computer Engineering, Ramrao Adik Institute of Technology, Sector 7, Phase I, Pad. Dr. D. Y. Patil Vidyapeeth, Nerul, Navi Mumbai 400706, Maharashtra, India ' Department of Computer Engineering, D. Y. Patil University, Ambi, Talegaon Dabhade, Pune 410507, Maharashtra, India ' Department of Electronics & Telecommunication Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed University, Dr. D.Y. Patil Vidyanagar, Sector-7, Nerul Navi-Mumbai 400706, India

Abstract: In the current communication system, Mobile Ad Hoc Networks (MANETs) are considered as the individual nodes in mobile networking and they easily communicate with each other. The performance of MANETs is impacted by security issues. Hence, effectual routing and trust updation are required for upgrading the data transmission security level in MANET. In this research, the Crayfish White Shark Optimisation (CFWSO)-based routing and trust updation is developed for MANET. The MANET is simulated by the energy, mobility and trust models. The routing is carried out through the CFWSO with fitness functions like energy, delay, throughput, distance and trust. Moreover, the Deep Neuro Fuzzy Network (DNFN) is used for trust updation. In addition, the performance computing measures like energy, delay, distance, packet loss, throughput and link lifetime are used to compute the efficacy of the model, and the finest outcomes of 0.140 J, 0.569 ms, 42.551 m, 1.489%, 85.196 Mbps and 86.680 ms, are achieved.

Keywords: COA; crayfish optimisation algorithm; DNFN; deep neuro-fuzzy network; MANETs; mobile ad hoc networks; WSO; white shark optimisation; routing.

DOI: 10.1504/IJWMC.2025.149192

International Journal of Wireless and Mobile Computing, 2025 Vol.29 No.4, pp.395 - 406

Received: 23 Apr 2024
Accepted: 17 Dec 2024
Published online: 17 Oct 2025 *

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