Design and analysis of genetic fuzzy backoff algorithm for contention window optimization of flying ad-hoc network under channel error

Abstract

Flying ad-hoc networks (FANETs) assist in high-risk tasks and monitor locations where human reachability is always at risk. FANETs have become one of the most appealing solutions for the Internet of Things in such scenarios. However, the default access mechanism in FANETs, binary exponential back-off, leads to increased collisions and energy utilization due to its lack of network adaptability. The proposed solution is a genetic fuzzy logic backoff (GFLB) algorithm, which determines the contention window size (CWS) based on the collision and success rate of the FANET. The genetic function optimizes the collision-success ratio by minimizing the fitness, while this optimized fitness value and retransmission attempts are then used to predict the CWS for subsequent transmission. A discrete chain Markov model with channel error is developed to evaluate the performance of the GFLB algorithm in terms of success, collisions, delay, and energy utilization under varying densities of unmanned aerial vehicles. The extensive simulation results demonstrate that the proposed GFLB algorithm significantly improves the efficiency and energy usage of end devices in FANETs compared to existing algorithms. The GFLB algorithm effectively reduces collision rates and energy consumption while enhancing the success rate and reducing delay in transmissions.