AI-based Mobility and Topology Management in Flying Ad Hoc Networks using Hybrid Bio-Inspired Optimization

Abstract

The adoption of a new communication paradigm is getting attention in the research world, where Flying Ad Hoc Networks (FANETs) have been deemed a viable approach for supporting coordinated operations of multiple Unmanned Aerial Vehicles (UAVs) in situations characterized by dynamic environments and the absence of infrastructure. Taking into consideration these drawbacks, in this paper, a novel and up-to-date AI-Based Mobility and Topology Management Framework for Flying Ad Hoc Networks via Hybrid Bio-Inspired Optimization is proposed. The proposed systems combine a Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) inspired model, introducing a novel hybrid model, with Artificial Intelligence techniques to provide a dynamic framework for optimizing UAV mobility patterns, topology formation, and communication paths within the proposed framework. Predictive mobility analysis using AI to make networks more adaptable and minimize topology changes. In addition, the hybrid optimization method will optimize the routing efficiency, reduce the communication overhead, and increase the packet delivery efficiency between nodes in the highly dynamic FANET environment. Results of experimental analysis prove that the proposed scheme has a better PDR of 96.4%, lower EED or end-to-end delay of 31%, and better topology stability that performs better than the traditional mobility management approaches with respect to reducing energy consumption.