With the development of the Industrial Internet of Things (IIoT), proactive fault tolerance through multi-node collaboration has emerged as a key approach to ensuring system stability. However, the distributed nature of edge environments introduces significant challenges to the robustness of existing proactive fault-tolerant systems. Outside the system, malicious nodes may disrupt the fault tolerance process, necessitating a robust collaborative mechanism to mitigate their impact. Inside the system, frequent node failures and other dynamic factors result in a highly dynamic network topology, requiring robust methods to optimize the effectiveness of fault identification and task migration decisions. In this paper, we utilize blockchain and Generative Adversarial Network (GAN) to construct a robustness enhanced proactive fault-tolerant framework. In our framework, we use blockchain for edge node supervision, and design an on-chain state lock mechanism to ensure the reliability of task migration during fault-tolerance processes. Considering QoS objectives and the credibility evaluations of blockchain on edge nodes, we construct proactive fault-tolerant task migration problem formulas and design a robust GAN-assisted proactive fault-tolerant task migration decision method based on these formulas. Finally, in an edge network built with Raspberry Pi devices, we validated the robustness of the proposed framework and the effectiveness of the proposed scheduling method. Compared with the baseline method, our method improved the task completion rate by an average of 13.8%, and reduced task completion delay and energy consumption by an average of 24.5% and 6.8%, respectively.

Recently, with wide use of internet and rapid growth of computer networks, the problem of intrusion detection in network security has become an import issue of concern. In this paper, a new intrusion detection algorithm DB-Kmeans has been introduced which combines K-means with DBSCAN. DB-Kmeans uses a new selection method of initial cluster center in K-means and set the neighborhood radius in DBSCAN to dynamic. Compared to K-means algorithm, it overcomes the shortage of sensitivity to initial centers and reduces the impact of noise points. Compared to DBSCAN algorithm, it reduces the influence of fixed neighborhood radius. The experiments on the NSL-KDD data set indicate that the proposed method is more efficient than that based on MinMax K-means algorithm. Also, the method has higher detection accuracy and lower false alarm rate.

With the development of Internet of Things (IoT), a large number of terminals and devices are connected to the network. Mobile edge computing (MEC) is proposed to assist cloud computing, to relieve the pressure of network and satisfy the requirements of delay-sensitive applications. Considering reasonable allocation of computing resources is the most important aspect corresponding to delay, this paper designs geographic clustering and collaborative scheduling (GC-CS) mechanism. This mechanism can be divided into two parts, which are the decentralized deployment of MEC servers and the resource allocation optimization in MEC. For the first part, this paper designs the load balancing based geographic clustering (LBGC) algorithm which combines the idea of greedy algorithm to realize the initial allocation of computing resources. For the second part, delay minimization oriented collaborative scheduling (DMCS) algorithm is designed to decrease the response delay without increasing system overhead. Finally, the effectiveness of the mechanism is verified by simulation in the IoT scene.

Motivation: Antigens that contain overlapping epitopes have been occasionally reported. As current algorithms mainly take a one-antigen-one-epitope approach to the prediction of epitopes, they are not capable of detecting these multiple and overlapping epitopes accurately, or even those multiple and separated epitopes existing in some other antigens. Results: We introduce a novel subgraph clustering algorithm for more accurate detection of epitopes. This algorithm takes graph partitions as seeds, and expands the seeds to merge overlapping subgraphs based on the term frequency-inverse document frequency (TF-IDF) featured similarity. Then, the merged subgraphs are each classified as an epitope or non-epitope. Tests of our algorithm were conducted on three newly collected datasets of antigens. In the first dataset, each antigen contains only a single epitope; in the second, each antigen contains only multiple and separated epitopes; and in the third, each antigen contains overlapping epitopes. The prediction performance of our algorithm is significantly better than the state-of-art methods. The lifts of the averaged f-scores on top of the best existing methods are 60, 75 and 22% for the single epitope detection, the multiple and separated epitopes detection, and the overlapping epitopes detection, respectively. Availability and implementation: The source code is available at github.com/lzhlab/glep/. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequencing platforms have produced huge amounts of sequence data. This is revolutionizing every aspect of genetic and genomic research. However, these sequence datasets contain quite a number of machine-induced errors-e.g. errors due to substitution can be as high as 2.5%. Existing error-correction methods are still far from perfect. In fact, more errors are sometimes introduced than correct corrections, especially by the prevalent k-mer based methods. The existing methods have also made limited exploitation of on-demand cloud computing. RESULTS: We introduce an error-correction method named MEC, which uses a two-layered MapReduce technique to achieve high correction performance. In the first layer, all the input sequences are mapped to groups to identify candidate erroneous bases in parallel. In the second layer, the erroneous bases at the same position are linked together from all the groups for making statistically reliable corrections. Experiments on real and simulated datasets show that our method outperforms existing methods remarkably. Its per-position error rate is consistently the lowest, and the correction gain is always the highest. AVAILABILITY AND IMPLEMENTATION: The source code is available at bioinformatics.gxu.edu.cn/ngs/mec. CONTACTS: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.