Resource Allocation in a Secure Internet of Battle Vehicles Through RF Fingerprint Recognition
Di Lin, Weiwei Wu
Abstract
In the internet of battle vehicles (IoBV), a few unauthorized users may attack and intrude on a network. The attackers who sneak into the network may maliciously occupy invaluable wireless resources that are supposed to serve authorized users. To guarantee the quality of service (QoS) of authorized users, we novelly propose an architecture of secure IoBV through the radio frequency (RF) fingerprint recognition technology and investigate the optimal resource allocation scheme under such an architecture. With few training samples, we propose a meta-transfer-based few-shot learning method for RF fingerprint recognition and an algorithm to optimize the data rate in an IoBV. Compared with a few benchmarks, the analysis results show that our proposed algorithm can improve the overall data rate and reduce the computation time.