Research on Wireless Communication Security Strategies for WSN Environment
In response to the shortcomings of low efficiency and susceptibility to successful at-tacks in existing WSN security protection strategies of operators,this paper proposes a wireless communication security strategy for the WSN environment based on an improved reinforcement learning algorithm.The algorithm is based on reinforcement learning models and utilizes deep neural networks to improve the shortcomings of low training efficiency and low output accura-cy caused by the increase of original model parameters.At the same time,direct trust and indirect trust indicators are proposed based on Gaussian distribution,and an improved reinforcement learning algorithm is used for adaptive updates.Finally,the fused trust is output,and the node property judgment result is output after comparing with the threshold.In experimental testing,the proposed algorithm has significant advantages in application capability and performance indi-cators compared to other algorithms,proving that it can effectively monitor node performance and improve network security.
NETL
NSTL
万方数据
