Abstract
Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies.