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Trust-aware truth discovery with Long-term Vehicle Reputation for Internet of Vehicles Crowdsensing

机译：信任感知真相发现，用于车辆互联网的长期车辆声誉

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摘要

In Internet of Vehicles (IoV), vehicle-based crowd-sensing provides various significant data, such as weather condition and GPS data, etc. However, due to different source quality, the sensed data of vehicles may vary from the ground truth. Truth discovery is usually used to analyze conflicting data, and it traditionally estimates source quality only from the current task. Aiming to take the long-term reputation into consideration, in this paper, we propose a trust-aware model. Specifically, we first propose a new model to define vehicle reliability. Instead of using weight as reliability, we define source reliability by its contribution to the loss function. Then, we propose a novel trust-aware model to accumulate long-term reputation of vehicle from its history tasks. Finally, based on vehicle reputation, initial weights are assigned for the future task. Experiments conducted on weather conditions and GPS datasets demonstrate that our trust-aware model can save execution time and achieve higher accuracy.
机译：在车辆互联网上（IOV），基于车辆的人群传感提供各种重要数据，例如天气状况和GPS数据等。然而，由于源质量不同，所感测的车辆数据可能因地面真理而变化。真理发现通常用于分析冲突数据，并且它传统上只能从当前任务估计源质量。旨在考虑长期声誉，在本文中，我们提出了一个信任感知模型。具体而言，我们首先提出了一种新模型来定义车辆可靠性。我们通过对损耗功能的贡献来定义源可靠性而不是使用重量。然后，我们提出了一种新的信任感知模型，可以从其历史任务中累积车辆的长期声誉。最后，基于车辆信誉，为未来任务分配了初始权重。在天气条件和GPS数据集进行的实验表明，我们的信任感知模型可以节省执行时间并实现更高的准确性。

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