Summary In wireless sensor networks, a lot of applications need the sensed information be transmitted to the sink node within a predefined time threshold. So end‐to‐end delay is an important performance metric in wireless sensor networks. Opportunistic routing protocols have been proposed to reduce the waiting delay. In the duty cycle networks, increasing the duty cycle ratio can also reduce the end‐to‐end delay. However, this method will consume more energy. It is obvious that there exists a trade‐off between delay and energy consumption. So adjusting the duty cycle ratio of each node can investigate this trade‐off. To the best of our knowledge, no existing work takes both of end‐to‐end delay and energy efficiency into consideration in the opportunistic routing networks. In this paper, we want to minimize the whole energy consumption while guaranteeing the expected end‐to‐end delay between the source nodes and the sink node is below the given threshold. To deal with this problem, we propose a dynamic duty cycle scheme which can significantly reduce the energy consumption and guarantee the expected end‐to‐end delay demand in the opportunistic routing network. To be specific, firstly, we formulate a new metric with the wake‐up time slots as the variable to measure the end‐to‐end delay. Secondly, for simplifying the complex problem, we decompose it into a set of single‐hop delay guarantee problems. Feedback controller has been used to solve the problem. We also analyze the influence of the multiple receivers in the same forwarding set. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithm. The experimental results reveal that our scheme can guarantee the delay requirement, meanwhile, significantly reduce the energy consumption compared with prior schemes.

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Most attempts on extending Graph Neural Networks (GNNs) to Heterogeneous Information Networks (HINs) implicitly take the direct assumption that the multiple homogeneous attributed networks induced by different meta-paths are complementary. The doubts about the hypothesis of complementary motivate an alternative assumption of consensus. That is, the aggregated node attributes shared by multiple homogeneous attributed networks are essential for node representations, while the specific ones in each homogeneous attributed network should be discarded. In this paper, a novel Heterogeneous Graph Information Bottleneck (HGIB) is proposed to implement the consensus hypothesis in an unsupervised manner. To this end, information bottleneck (IB) is extended to unsupervised representation learning by leveraging self-supervision strategy. Specifically, HGIB simultaneously maximizes the mutual information between one homogeneous network and the representation learned from another homogeneous network, while minimizes the mutual information between the specific information contained in one homogeneous network and the representation learned from this homogeneous network. Model analysis reveals that the two extreme cases of HGIB correspond to the supervised heterogeneous GNN and the infomax on homogeneous graph, respectively. Extensive experiments on real datasets demonstrate that the consensus-based unsupervised HGIB significantly outperforms most semi-supervised SOTA methods based on complementary assumption.

Many efforts have been paid to enhance Graph Convolutional Network from the perspective of propagation under the philosophy that ``Propagation is the essence of the GCNNs". Unfortunately, its adverse effect is over-smoothing, which makes the performance dramatically drop. To prevent the over-smoothing, many variants are presented. However, the perspective of propagation can't provide an intuitive and unified interpretation to their effect on prevent over-smoothing. In this paper, we aim at providing a novel explanation to the question of "Why do attributes propagate in GCNNs?''. which not only gives the essence of the oversmoothing, but also illustrates why the GCN extensions, including multi-scale GCN and GCN with initial residual, can improve the performance. To this end, an intuitive Graph Representation Learning (GRL) framework is presented. GRL simply constrains the node representation similar with the original attribute, and encourages the connected nodes possess similar representations (pairwise constraint). Based on the proposed GRL, exiting GCN and its extensions can be proved as different numerical optimization algorithms, such as gradient descent, of our proposed GRL framework. Inspired by the superiority of conjugate gradient descent compared to common gradient descent, a novel Graph Conjugate Convolutional (GCC) network is presented to approximate the solution to GRL with fast convergence. Specifically, GCC adopts the obtained information of the last layer, which can be represented as the difference between the input and output of the last layer, as the input to the next layer. Extensive experiments demonstrate the superior performance of GCC.

A data interaction transformation model, XYJSON, that is suitable for all data using current standard SQL syntax and JSON document data is proposed to solve the problem of increasing development workload and difficulty caused by using different control methods for corresponding types of databases under the cloud hybrid storage. A control program was studied to control relational and NoSQL data at the same time, by establishing a general conversion model between relational and NoSQL data and converting standard SQL statements into JSON. The performance of XYJSON was compared with that of the traditional mode. The results show that the performance difference between XYJSON and the traditional mode is small. In addition, a developer survey was conducted on XYJSON for user friendliness and compatibility. All developers rated XYJSON as excellent. The current cloud hybrid storage cannot use a unified control model to realize data control. XYJSON breaks through this bottleneck, making it easier and more efficient to control different types of databases under cloud hybrid storage.