HyPE: Online Hybrid Pseudo-Bayesian Estimation Method for S-ALOHA-Based Tactical FANETs

Abstract

Significant challenges are involved in tactical flying ad-hoc network (FANET) missions because network environments are very dynamic. In addition, energy-efficient network operation is important in tactical FANETs owing to the limited capacity of the on-board battery in unmanned aerial vehicles (UAVs). In a slotted-ALOHA (S-ALOHA)-based tactical FANET, frequent packet collisions due to changes in the network environment deteriorate the energy efficiency. Therefore, accurately estimating the number of active UAVs is crucial for improving the performance of S-ALOHA-based networks. Several estimation methods such as low-bound, Schoute, max-probability, and Bayesian estimation have been studied, and these methods perform well in static network environments; however, the estimation error significantly increases in dynamic network environments. To accurately estimate the number of active UAVs in highly dynamic environments, this study proposes an online hybrid pseudo-Bayesian estimation (HyPE) method. Specifically, this method combines the pure-Bayesian and pseudo-Bayesian estimation methods to overcome their shortages such as the inability in a dynamic environment of the pure-Bayesian method and the low estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed HyPE method with that of benchmark methods in terms of the estimation error according to the variation period and variation step size. The results show that HyPE is more adaptable to dynamic changes in network environments.

Keywords

• Bayesian estimation
• unmanned aerial vehicle (UAV)
• active UAV
• slotted-ALOHA
• tactical flying ad-hoc network (FANET)