A study of trust mining algorithms for beacon nodes in large-scale network environments
Yanyan Jiang
TL;DR
This paper proposes a trust mining algorithm for beacon nodes to improve node positioning accuracy in large-scale networks.
Contribution
A novel trust-based algorithm using seepage theory and RSSI to optimize node positioning in large-scale networks.
Findings
The algorithm improves node positioning accuracy by incorporating beacon node influence and trust.
Experimental results show the algorithm performs well under varying beacon node numbers and communication distances.
Trust mining effectively reflects the security status of nodes in the network.
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…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Security in Wireless Sensor Networks · Indoor and Outdoor Localization Technologies