Reliable routing in MANET with mobility prediction via long short-term memory

Abstract

A MANET consists of a self-configured group of transportable mobile nodes that lacks a central infrastructure to manage network traffic. To facilitate communication, govern route discovery, and manage resources, all moving nodes in multi-hop wireless networks (MANETs) work together. These networks struggle with dependability, energy consumption, and collision avoidance. The goal of this research project is to establish a new, dependable MANET routing model, where the selection of predictor nodes comes first. For selecting predictor nodes based on factors like distance, security (risk), Receiver Signal Strength Indicator (RSSI), Packet Delivery Ratio (PDR), and energy, the adaptive weighted clustering algorithm (AWCA) is used in this case. Using the Interfused Slime and Battle Royale Optimization with Arithmetic Crossover (IS&BRO–AC) model, the node with the lower weight is selected as the Cluster Head (CH). Additionally, mobility prediction is carried out, in which the node mobility is forecast using Improved Long Short Term Memory (LSTM) while taking distance and Receiver Signal Strength Indicator (RSSI) into account. Based on the forecast, trustworthy data transfer is implemented, ensuring more accurate and dependable MANET routing. The examination of RSSI, PDR, and other metrics is completed at the end.