ABSTRACT With the increasingly widespread application of multiprocessor systems, some processors in multiprocessor systems are inevitably prone to malfunctions. The reliability and effectiveness of the system are key issues. As a standard for measuring system fault tolerance, connectivity, and edge connectivity have many drawbacks. Therefore, Haray proposed conditional connectivity by restricting the connected components in disconnected subgraphs to satisfy certain properties, where and represent the interconnection network and its set of faulty vertices, respectively. Restricted connectivity is a special type of conditional connectivity. Exchanged crossed cube, as a deformation of hypercube, has more favorable properties, such as smaller diameter, smaller link size, and lower cost. We prove that the 2‐restricted connectivity of the exchanged crossed cubes is for .

The rapid growth and increasing complexity of Internet of Things (IoT) devices have made network intrusion detection a critical challenge, especially in edge computing environments where data privacy is a primary concern. Machine learning-based intrusion detection techniques enhance IoT network security but often require centralized network data, posing significant risks to data privacy and security. Although federated learning (FL)-based network intrusion detection methods have emerged in recent years to address privacy concerns, they have not fully leveraged the advantages of graph neural networks (GNNs) for intrusion detection. To address this issue, we propose a federated spatiotemporal graph convolutional network (FedSTGCN) model, which integrates the capabilities of spatiotemporal GNNs (STGNNs) and federated learning. This framework enables collaborative model training across distributed IoT devices without requiring the sharing of raw data, thereby improving network intrusion detection accuracy while preserving data privacy. Extensive experiments are conducted on two widely used IoT intrusion detection datasets to evaluate the effectiveness of the proposed approach. The results demonstrate that FedSTGCN outperforms other methods in both binary and multiclass classification tasks, achieving over 97% accuracy in binary classification tasks and over 92% weighted F1-score in multiclass classification tasks.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube ($LeTQ$) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this article, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for$LeTQ$networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

Summary Data center network (DCN) is used for transmission, storage, and processing of big data, which plays an important role in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server‐centric DCN topology can ensure the scale of the DCN by recursively increasing the number of network nodes and links. Relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server‐centric DCN topology, which has more advantages in scalability, fault tolerance, and so on. The PTNet network topology is theoretically analyzed in terms of network diameter, bottleneck throughput, and total number of links in the network. Based on the deep research of PTNet network, this article analyzes and studies the network topology, multicast, and broadcast routing algorithm.

Summary Network‐on‐chip (NoC) is a new design method of system‐on‐chip used in very large scale integrated circuit (VLSI) systems. It is an important issue for choosing the appropriate topology for NoC. Wirelength and layout area are significant parameters affecting NoC due to the restriction of chip area. In this paper, we propose a new interconnection network called the incomplete ternary n‐cube for parallel computing systems. Then, a linear algorithm is proposed to layout incomplete ternary n‐cube network onto torus NoC. Furthermore, the failure of interconnection network is also taken into account, and a fault‐tolerant layout of incomplete ternary n‐cube with faulty edges into torus NoC is verified. Theoretical analysis demonstrates that the proposed algorithm can reduce the network cost and wirelength, which be conducive to estimate the wire length and chip area.

Fog computing is a distributed computing model as the middle layer between the cloud data center and the IoT device/sensor. It provides computing, network, and storage devices so that cloud based services can be closer to IOT devices and sensors. Cloud computing requires a lot of bandwidth, and the bandwidth of the wireless network is limited. In contrast, the amount of bandwidth required for “fog computing” is much less. In this paper, we improved a new protocol Peer Assistant UDT‐Based Data Transfer Protocol (PaUDT), applied to Iot‐Cloud computing. Furthermore, we compared the efficiency of the congestion control algorithm of UDT with the Adobe’s Secure Real‐Time Media Flow Protocol (RTMFP), based on UDP completely at the transport layer. At last, we built an evaluation model of UDT in RTT and bit error ratio which describes the performance. The theoretical analysis and experiment result have shown that UDT has good performance in IoT‐Cloud computing.