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TrustGrey: A General Trust Evaluation Framework Based on Gray Buffer
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AI
DOI：10.1109/JIOT.2025.3645417.png)
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Abstract
En 中文
Mobile crowd-sensing (MCS) has emerged as a promising solution for large-scale data collection in Internet of Things (IoT) scenarios. However, the malicious behavior of smart terminals, such as providing corrupted and falsified data or deliberately spreading false data, poses a significant threat to the credibility of MCS services. At present, the mainstream trust evaluation schemes evaluate their trust value through the accumulation of terminal interaction experience, but the inherent defects of these schemes will make the MCS service suffer serious trust-decaying destruction. The current trust computing methods do not yet take into account the balance between the quality of service completion and the crowd-sensing collaboration experience of the terminal. To solve these problems, this article proposes a novel general gray buffer trust evaluation framework (TrustGrey), which is used to evaluate the trust relationship of smart terminals. Specifically, we construct the trust value calculation model of the gray trust state for smart terminals to make up for the inherent defects of the normal trust value calculation model. The gray buffer of sudden drop is designed in the trust value calculation model to avoid trust-decaying destruction. We design a quick recovery mechanism of a gray buffer to avoid detecting false alarms caused by the sudden drop of trust, as well as avoiding the loss of multiple damage of intelligent malicious terminals by an irreversible black value reduction mechanism. Then, we design a supply–demand equilibrium-based dynamic recruitment mechanism to dynamically coordinate the recruitment process of service requests by comprehensively considering the importance level of service requests and the credibility of terminals, to balance the experience of service originators and completion parties. Experiments on real-world datasets highlight the advantages of our proposed framework, TrustGrey. The experiments also show that TrustGrey has considerable versatility.
Keywords:
Internet of Things (IoT)
mobile crowd-sensing (MCS)
trust evaluation
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