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UAV-assisted wireless communications facilitate the applications of Internet of Things (IoT), which employ billions of devices to sense and collect data with an on-demand style. However, there are numerous malicious Mobile Data Collectors (MDCs) mixing into the network, stealing or tampering with data, which greatly damages IoT applications. So, it is urgent to build a ubiquitous trust communication system. In this paper, a UAV-assisted Ubiquitous Trust Evaluation (UUTE) framework is proposed, which combines the UAV-assisted global trust evaluation and the historical interaction based local trust evaluation. We first propose a global trust evaluation model for data collection platforms. It can accurately eliminate malicious MDCs and create a clean data collection environment, by dispatching UAVs to collect baseline data to validate the data submitted by MDCs. After that, a local trust evaluation model is proposed to help select credible MDCs for collaborative data collection. By letting UAVs distribute the data verification hash codes to MDCs, the MDCs can verify whether the exchanged data from the interacted MDCs is reliable. Extensive experiments conduct on a real-life dataset demonstrate that our UUTE system outperforms the existing trust evaluation systems in terms of accuracy and cost.
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Stored only when both papers are in the corpus — 93 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.