DCell has been proposed for data centers as a server-centric interconnection network structure. DCell can support millions of servers with high network capacity by only using commodity switches. With one exception, we prove that a $$k$$ level DCell built with $$n$$ port switches is Hamiltonian-connected for $$k \geq 0$$ and $$n \geq 2$$. Our proof extends to all Generalized DCell connection rules for $$n\ge 3$$. Then, we propose an $$O(t_k)$$ algorithm for finding a Hamiltonian path in $$DCell_{k}$$, where $$t_k$$ is the number of servers in $$DCell_{k}$$. Furthermore, we prove that $$DCell_{k}$$ is $$(n +k - 4)$$-fault Hamiltonian-connected and $$(n +k - 3)$$-fault Hamiltonian. In addition, we show that a partial DCell is Hamiltonian-connected if it conforms to a few practical restrictions.

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.

Localization has always been an essential application of underwater acoustic sensor networks (UASNs), which plays an important role in routing strategies design, node recycling, and so on. The complex ocean condition, prior infrastructure deployment, and time synchronization among beacons challenge the application of the UASN's localization. To solve these problems, we propose a novel underwater acoustic sensor networks localization algorithm based on the virtual node assistance. The algorithm is classified into two parts based on the current marine environment, including Virtual node Assisted Static (VAS) localization algorithm and Virtual node Assisted Dynamic (VAD) localization algorithm. An auxiliary node, which does not directly participate in the localization, is deployed for virtual node setup, error measurement, and RSSI ranging. The GPS-equipped ship utilizes virtual node and geometry to realize the UASN's localization without complicated deployment procedures and time synchronization. The simulation results show that our proposed algorithm can achieve the properties, including high localization coverage and small localization error as well as low communication overhead in the UASNs.

In order to solve the problem that the training time of existing neural network multi-label classification algorithms is too long, we propose a new BP neural network multi-label classification (BP-AEPML) algorithm based on approximate extreme points. Firstly, the original multi-label data set is transformed into multiple separate binary sub data sets using binary relevance transformation strategy. And then the representative data set of each binary sub data set is extracted using approximate extreme points. Finally, the representative data set is trained with BP neural network. To validate and evaluate the classification results and training time of the proposed algorithm, three public data sets are used to compared with traditional BP neural network multi-label classification algorithm, basic SVM multi-label classification algorithm and ML-BVM algorithm. The experiment results shows that the classification results of BPAEPML is similar to that of the traditional BP neural network multi-label classification algorithm and a little better than that of basic SVM multi-label classification algorithm and ML-BVM algorithm. At the same time, the training time is greatly reduced.

Continuous missing data is a prevalent challenge in long-term air quality monitoring, undermining the reliability of public health protection and sustainable urban development. In this paper, we propose ConFill, a novel contrastive learning-based framework for reconstructing continuous missing data in air quality time series. By leveraging temporal continuity as a supervisory signal, our method constructs positive sample pairs from adjacent subsequences and negative pairs from distant and shuffled segments. Through contrastive learning, the model learns robust representations that preserve intrinsic temporal dynamics, and enable accurate imputation of continuous missing segments. A novel data augmentation strategy is proposed, to integrate noise injection, subsequence masking, and time warping to enhance the diversity and representativeness of training samples. Extensive experiments are conducted on a large scale real-world dataset comprising multi-pollutant observations from 209 monitoring stations across China over a three-year period. Results show that ConFill outperforms baseline imputation methods under various missing scenarios, especially in reconstructing long consecutive gaps. Ablation studies confirm the effectiveness of both the contrastive learning module and the proposed augmentation technique.