A machine learning-based trust evaluation model for wireless sensor networks

Wireless sensor networks have been widely used in many industries, where sensor nodes are used to perform specific tasks, such as information collection or data transmission. Due to the particularity of sensor nodes, the trust mechanism of their nodes has been difficult to establish. Different from the traditional trust evaluation model, this paper proposes a trust evaluation model based on machine learning principles. By combining the extracted trust features, the final trust value is obtained for decision-making. Experimental results show that the model is effective and has higher accuracy than other methods.