Shuffled frog leaping algorithm based internet of things node reputation evaluation method

Abstract

Disclosed is a shuffled frog leaping algorithm based internet of things node reputation evaluation method. The shuffled frog leaping algorithm based internet of things node reputation evaluation method includes analyzing local features of nodes in the internet of things, calculating importance of the nodes in internet of things autonomous domains, using the calculated node importance as a basis for node screen, using a shuffled frog leaping algorithm for clustering the nodes, selecting a kind of nodes with higher importance as neighbor nodes of reputation evaluation, using the neighbor nodes to perform reputation evaluation on nodes to be evaluated according to a reputation evaluation algorithm, calculating a more accurate node reputation value according to current reputation and history reputation of the nodes, setting a threshold value, comparing the node reputation value with the threshold value to judge whether the nodes are credible, judging the nodes are incredible nodes when the reputation value is lower than the set threshold hold, and otherwise, judging the nodes are credible nodes. According to the shuffled frog leaping algorithm based internet of things node reputation evaluation method, the problem that incredible nodes interfere evaluation results in traditional reputation evaluation systems can be effectively avoided.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a reputation evaluation method for Internet of Things nodes based on a hybrid leapfrog algorithm. Background technique [0002] At present, research on the credibility of distributed systems mainly focuses on two aspects: trust management and trust evaluation. The essence of trust management is an access control model and method based on authentication and authorization. The trust evaluation method is based on the recommended trust relationship between entities, and evaluates the credibility of entities based on their own experience, and then makes decisions based on the credibility. Classic reputation management techniques include reputation averaging schemes, Bayesian networks, and cluster filtering. It is very important to establish trust between nodes and domains. Generally speaking, the trust level can be evaluated by the behavior of neighboring nodes and the informa...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problem that untrustworthy nodes may interfere with the evaluation results in the traditional reputation evaluation system, the present invention proposes a method for evaluating the reputation of Internet of Things nodes based on the hybrid leapfrog algorithm

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