A study of trust mining algorithms for beacon nodes in large-scale network environments

Abstract

In a large-scale network environment, node positioning is prone to large deviations. Mining beacon node trust is the basis for precise node positioning in the network environment. Therefore, this article studies the trust degree mining algorithm of beacon nodes in a large-scale network environment. First, according to the distance error evaluation and probability function of beacon nodes in the large-scale network environment, the direct trust degree of beacon nodes is obtained. The trust degree is converted into influence, and the influence of beacon nodes is mined using the seepage theory to determine the beacon node with the highest impact in the large-scale network environment. Then, according to the influence of nodes, received signal strength indicator (RSSI) is used to optimize the conventional distance vector hop (DV-Hop) node location algorithm. The influence weights the average hop distance of beacon nodes. The weight of the influence of beacon nodes defines the average hop distance of unknown nodes. The average hop distance information of unknown nodes is taken from more high-influence beacon nodes, solving the problem of significant positioning errors caused by the uncertainty of location targets. Finally, the security status of nodes is reflected according to the degree of trust of different nodes to beacon nodes. The experimental results show that the algorithm can accurately locate other nodes in a wide network environment when the number of beacon nodes and communication distance change, and the trust degree of nodes mined can accurately reflect the security status of nodes.