Optimal scheduling for concurrent transmissions in rate-nonadaptive wireless networks is NP-hard. Optimal scheduling in rate-adaptive wireless networks is even more difficult, because, due to mutual interference, each flow's throughput in a time slot is unknown before the scheduling decision of that slot is finalized. The capacity bound derived for rate-nonadaptive networks is no longer applicable either. In this paper, we first formulate the optimal scheduling problems with and without minimum per-flow throughput constraints. Given the hardness of the problems and the fact that the scheduling decisions should be made within a few milliseconds, we propose two simple yet effective searching algorithms which can quickly move towards better scheduling decisions. Thus, the proposed scheduling algorithms can achieve high network throughput and maintain long-term fairness among competing flows with low computational complexity. For the constrained optimization problem involved, we consider its dual problem and apply Lagrangian relaxation. We then incorporate a dual update procedure in the proposed searching algorithm to ensure that the searching results satisfy the constraints. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheduling algorithms which are found to achieve throughputs close to the exhaustive searching results with much lower computational complexity.

Recommendation systems are necessary to enhance the consumer shopping experience. As one of the classical algorithms, collaborative filtering (CF) is the main recommendation model. Currently, the graph neural network realize collaborative filtering through graph convolution and model the interaction between users and items. However, the ability of ordinary graph to express the higher-order relationship between vertices and to process sparse data is limited. It cannot fully capture the potential interaction between users and items. So the hypergraph is used to model the interaction, and the recommendation question is transformed into the link prediction problem on the hypergraph. An Encoder Framework based on Bipartite Hypergraph (EFBH) is proposed. First, the hypergraph convolution module is constructed, and the user-user hypergraph and the item-item hypergraph are constructed to solve the data sparsity problem; then the multi-hypergraph encoder module is constructed to encode the cooperation signals hidden in the user-item interaction to obtain high-quality vertex embedding vectors and better recommendation results. Experiments on three common datasets show that our model improves the AUC on average by 5.35%, 2.91%, and 3.26% respectively. The RMSE also decreased by an average of 1.43%, 15.33%, and 16.06% respectively, which is the best one compared to the existing models.

Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size 2 ⁸ ; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to 24.