The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.
Peng YANG
Chongqing University of Posts and Telecommunications,MIIT
Yu YANG
Chongqing University of Posts and Telecommunications
Puning ZHANG
Chongqing University of Posts and Telecommunications
Dapeng WU
Chongqing University of Posts and Telecommunications
Ruyan WANG
Chongqing University of Posts and Telecommunications
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Peng YANG, Yu YANG, Puning ZHANG, Dapeng WU, Ruyan WANG, "Sensitivity Enhanced Edge-Cloud Collaborative Trust Evaluation in Social Internet of Things" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 9, pp. 1053-1062, September 2022, doi: 10.1587/transcom.2021EBP3130.
Abstract: The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3130/_p
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@ARTICLE{e105-b_9_1053,
author={Peng YANG, Yu YANG, Puning ZHANG, Dapeng WU, Ruyan WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Sensitivity Enhanced Edge-Cloud Collaborative Trust Evaluation in Social Internet of Things},
year={2022},
volume={E105-B},
number={9},
pages={1053-1062},
abstract={The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.},
keywords={},
doi={10.1587/transcom.2021EBP3130},
ISSN={1745-1345},
month={September},}
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TY - JOUR
TI - Sensitivity Enhanced Edge-Cloud Collaborative Trust Evaluation in Social Internet of Things
T2 - IEICE TRANSACTIONS on Communications
SP - 1053
EP - 1062
AU - Peng YANG
AU - Yu YANG
AU - Puning ZHANG
AU - Dapeng WU
AU - Ruyan WANG
PY - 2022
DO - 10.1587/transcom.2021EBP3130
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2022
AB - The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.
ER -