This approach utilizes mobility prediction and modified gold rush optimization to enhance routing reliability in MANETs, addressing challenges like energy efficiency and collision avoidance.
Abstract This research aims to develop a reliable routing model for MANETs, which are decentralized networks of mobile nodes lacking a central infrastructure. It faces challenges like energy efficiency, collision avoidance, and reliability. The proposed approach focuses on selecting predictor nodes using the Modified Gold Rush Optimization approach (MGRO), considering factors like distance, trust, link quality, path cost and energy. CH are then chosen via MGRO, prioritizing nodes with lower weights. Furthermore, mobility prediction is conducted by Convolutional Neural Network (CNN), incorporating distance and link quality data. This prediction aids in ensuring reliable data transfer by anticipating node movement. By integrating these approaches, the goal is to maximize the reliability of MANET routing. Finally, the study evaluates metrics like path cost, link quality, trust and distance to assess the network performance comprehensively.
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Vinutha et al. (2025) studied this question.
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