In order to enhance the ability of wireless sensor networks to resist various security threats and reduce the limitations caused by the characteristics of wireless sensor networks and sensor nodes, this paper proposes a secure routing protocol for wireless sensor networks based on trust management. Combined with the relevant parameters of wireless sensor network, the simulation experiment is carried out with MATLAB. Aiming at the trust management part of the wireless sensor network security protocol proposed in this paper, the malicious attack environment such as sensor node attributes is simulated to verify the resistance of this model to relevant malicious attacks. For the trust management-based wireless sensor network security routing protocol proposed in this paper, the model included in the protocol is compared to the existing security routing model, combining the characteristics of average simulation transmission, network life, and average routing update time. Experiments show that the model has better routing performance and has improved by an average of about 20%. We offer a new solution to solve the problem of wireless routing security.

The security prediction and control program of sensor network based on big data analysis is studied. First of all, in view of the shortcomings of existing security measures, this paper regards security standards as a challenge. Based on the changes of network security before and after the attack, the concept of “network security line” is proposed to select and simplify the security measures that can meet the needs and affect the real security and monitor the counting process of the network. Then, for counterattack, several models of topology security measurement based on stochastic process are developed to check the topology security. Finally, the experimental design of the risk assessment method is found by creating a risk receiving data model, deleting data features, and recreating distributed data. Experimental results show that the efficiency of this method is above 90% when the estimated distance is 10–40 m. The working power is 196∼461 Hz, lower than the normal standard. The time delay is less than 0.3 s, and the real-time performance is better than ordinary models. The relative error is less than 3%, and the accuracy is higher.

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