RF Fingerprint-Identification-Based Reliable Resource Allocation in an Internet of Battle Things

Abstract

With the advances of Internet of Things (IoT) technology, the Internet of Battlefield Things (IoBT) has revolutionized the military battlefield. Unlike civilian IoT applications, a few adversarial attackers may sneak into an IoBT and destroy the network infrastructure by generating cyberattacks, e.g., Distributed Denial-of-Service (DDoS) attacks, making the Quality of Service (QoS) of network dramatically decrease. To improve the performance of a network, we propose two schemes of enhancing network QoS: one is user access control with the aid of RF fingerprinting because of small-scaled available signal samples, while the other is the optimization of network performance. Specifically, we address the optimization of network utility with the methods of power allocation and channel allocation. Mathematically, we propose a mixed-integer nonlinear (MIN) problem, which optimizes the allocation of wireless resources. Also, the original optimization problem is decomposed into a power allocation subproblem and a channel allocation subproblem, and both these subproblems are nondeterministic polynomial (NP) hard problems. We propose a few approximate algorithms, which can iteratively converge to the optimum of the original problem in polynomial time. The simulation results show that our proposed algorithms can improve network utility and various network QoS than the other algorithms in an IoBT environment.