Abstract
In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.
Highlights
A S A NOVEL network to realize the comprehensive information interaction between human and the objective world, the Internet of Things (IOT) is based on information perception, transmission and processing
If the channel in use is blocked in the current timeslot, the sensor node will randomly switch to an idle channel in the timeslot, otherwise leaving the channel unchanged
For the internal mutual interference caused by competition among sensor nodes and external intelligent multi-channel blocking jamming
Summary
A S A NOVEL network to realize the comprehensive information interaction between human and the objective world, the Internet of Things (IOT) is based on information perception, transmission and processing. A joint multiagent anti-jamming algorithm (JMAA) based on multi-agent Q-learning is proposed. In order to avoid external multi-channel intelligent blocking jamming and mutual interference among sensor nodes in WSN, a joint multi-agent anti-jamming algorithm (JMAA) based on multi-agent Q-learning is proposed.
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