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Iterative graph processing is widely used as a significant paradigm for large-scale data analysis. In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions and flexible replaceable communication module. In terms of graph data preprocessing, RAGraph introduces a contribution-driven edge migration algorithm to effectively utilize network resources. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Experimental results show that RAGraph can achieve an average speedup of 9.7× (up to 98×) and an average WAN cost reduction of 78.5$\%$% (up to 97.3$\%$%) compared with state-of-the-art systems.

With the development of science and technology, human motion recognition as an important auxiliary technology for smart home has important research significance and broad application prospects in the fields of security monitoring, family aging, and human-computer interaction. At present, most of the traditional radar-based human motion recognition methods perform multidimensional fast Fourier transform (FFT) on human motion echo signals to obtain distance, Doppler, angle information and to construct various data spectra, which are fed into the neural networks for classification and recognition. However, the data preprocessing process is complicated. To address this problem, our article proposes a dual-stream network structure model, which consists of convolutional neural network (CNN) and bidirectional long and short-term memory network (BiLSTM) for human motion recognition with radar raw data. First, digital compensation technology and background frame difference method are used to preprocess the original in-phase/orthogonal (I/Q) raw data to eliminate static interference; then the processed I/Q signal are converted into amplitude/phase (A/P) data format. Finally, the processed I/Q signal and A/P signal are simultaneously fed into the network structure to extract the temporal and spatial characteristics of human motions, and then the dual-flow network is integrated to enhance the feature interaction and improve the recognition accuracy. The experimental results show that the method is simple in data preprocessing, makes full use of the interframe correlation of action data, has fast model convergence, and achieves 98.57% recognition accuracy.

Deep learning (DL) has found extensive applications in wireless communication, propelling DL-based physical layer orthogonal frequency division multiplexing (OFDM) receivers to the forefront. However, existing research mostly focuses on training frequency-domain signals to achieve bit recovery, which involves additional pre-processing steps that add complexity to receiver design and loss of temporal information. In this letter, we propose a novel attentive deep convolutional network (ADCN) with a complexity of $\mathcal {O}(N^{2})$ design that uses a learned linear transform and exploitation of the cyclic prefix (CP), which can replace these pre-processing procedures. Additionally, we enhance ADCN with an attention mechanism that captures the time dependence between OFDM signals, employs causal convolution to reveal the long-term properties of the signals, and addresses the issue of temporal information loss. According to 3GPP-defined channel models, we demonstrate that ADCN is superior to traditional methods and performs well in channels with large delay spread.