The widespread deployment of road sensors in the Internet of Things (IoT) allows for fine-grained data integration, which is a fundamental demand for data-driven applications. Sensing data with inevitable missing and substantial anomalies are unavoidable, due to unstable network communication, faulty sensors, etc. Recent tensor completion studies have demonstrated the superiority of deep learning in imputation tasks by precisely capturing the intricate spatiotemporal dependencies/correlations. However, ignoring the significance of initial interpolation in these methods results in unstable performance, especially for complicated missing scenarios across large-scale data. Additionally, the existing interpolation methods utilize recursive signal propagation along spatiotemporal dimensions, which produce noise accumulation where the dependencies are uncorrelated. In this study, we design a multiattention tensor completion network (MATCN) for modeling multidimensional representation in the presence of missing entries. MATCN sparsely sampled historical fragments and utilized a gated diffusion convolution layer to generate the initial schemes, which mitigate the exposure bias existing in previous traffic imputation models. In addition, we develop a spatial signal propagation module and a temporal self-attention module as the basic stack block of deep networks, which executes representation aggregation and dynamic dependencies extraction at the spatiotemporal level. This architecture empowers MATCN with progressive completion capacities for complex data missing scenarios. Numerical experiments on four real-world traffic data sets with various missing scenarios demonstrate the superiority of MATCN over multiple state-of-the-art imputation baselines.

中文翻译：

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RIS 协助无线供电的物联网网络解决相移误差和收发器硬件损坏问题


道路传感器在物联网（IoT）中的广泛部署可以实现细粒度的数据集成，这是数据驱动应用程序的基本需求。由于网络通信不稳定、传感器故障等，传感数据不可避免地丢失和实质性异常是不可避免的。最近的张量补全研究通过精确捕获复杂的时空依赖性/相关性，证明了深度学习在插补任务中的优越性。然而，在这些方法中忽略初始插值的重要性会导致性能不稳定，特别是对于大规模数据中的复杂缺失场景。此外，现有的插值方法利用沿时空维度的递归信号传播，这会在依赖性不相关的情况下产生噪声累积。在本研究中，我们设计了一个多注意张量完成网络（MATCN），用于在存在缺失条目的情况下对多维表示进行建模。 MATCN 对历史片段进行稀疏采样，并利用门控扩散卷积层来生成初始方案，从而减轻了先前流量插补模型中存在的暴露偏差。此外，我们开发了空间信号传播模块和时间自注意力模块作为深度网络的基本堆栈块，在时空层面执行表示聚合和动态依赖关系提取。该架构赋予 MATCN 渐进式补全复杂数据缺失场景的能力。对四个具有各种缺失场景的真实交通数据集进行的数值实验证明了 MATCN 相对于多个最先进的插补基线的优越性。