Performance Improvement of Hybrid Pseudo-Bayesian Approach-Based Random Access in FANETs

Abstract

Operating in flying ad-hoc networks (FANETs) is challenging due to the highly dynamic nature of the network environment. Energy efficiency is crucial in these networks, particularly because unmanned aerial vehicles (UAVs) have limited battery capacity. In slotted-ALOHA (S-ALOHA)-based FANETs, frequent packet collisions, driven by changes in the network environment, can significantly degrade energy efficiency. Therefore, accurately estimating the number of active UAVs is essential for improving the performance and energy efficiency of S-ALOHA-based networks. Several estimation methods, such as low-bound, Schoute, max-probability, and Bayesian estimation, have been explored and perform well in static network environments. However, their estimation accuracy decreases significantly in dynamic environments. To address this issue, this study proposes a hybrid pseudo-Bayesian estimation method designed to improve estimation accuracy in highly dynamic environments. Specifically, this method combines the strengths of pure-Bayesian and pseudo-Bayesian estimation methods to overcome limitations such as the pure-Bayesian method's inadequacy in dynamic environments and the lower estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed method with that of benchmark methods in terms of estimation error and the number of successful packets, considering variation periods and step sizes. The results demonstrate that the proposed method is more adaptable to dynamic changes in network environments.