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Media type:
E-Article
Title:
Trust Mechanism of Feedback Trust Weight in Multimedia Network
Contributor:
Lv, Zhihan;
Song, Houbing
imprint:
Association for Computing Machinery (ACM), 2021
Published in:ACM Transactions on Multimedia Computing, Communications, and Applications
Language:
English
DOI:
10.1145/3391296
ISSN:
1551-6857;
1551-6865
Origination:
Footnote:
Description:
<jats:p>
It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, a
<jats:bold>FTWDF (Feedback Trust Weighted for Data Fusion)</jats:bold>
is proposed. Second,
<jats:bold>EEFA (Efficiency unequal Fuzzy clustering Algorithm</jats:bold>
) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of the
<jats:bold>TMDF (Trust Multiple attributes Decision-making-based data Fusion)</jats:bold>
algorithm, and 8.3% higher than that of
<jats:bold>LDTS</jats:bold>
(
<jats:bold>Larger Data fusion based on node Trust evaluation in wireless Sensor networks)</jats:bold>
algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.
</jats:p>