Large-scale sewage treatment plants are one of the typical Industrial Internet of Things systems, where the presence of a large number of sensors generates massive dynamic time series data, and such multivariate time series data are usually time-dependent and random. Therefore, there is a certain risk when fitting the potential anomalies of real-world data, which will bring great challenges to anomaly detection. In this article, we propose a time-series mutual adversarial network (TMAN), a novel reconstruction model for anomaly detection on multivariate time series. It is based on the idea of adversarial learning and consists of two identical subnetworks. During the training process, two subnetworks can independently complete the learning of the time distribution of normal samples of industrial time series data for mutual adversarial. In the process of detecting, we obtain the residual values of TMAN reconstructed for different time series samples to discriminate anomalies. We combine TMAN and anomaly determination mechanisms to build a new industrial time series anomaly detection framework named TMANomaly. In addition, we select the dataset features with a grey correlation algorithm to achieve very high performance with a small number of features. Experimental results show that our proposed TMANomaly outperforms five popular anomaly detection methods and effectively improves the accuracy of industrial multivariate time series anomaly detection.

In lossless data center networks (DCN), many existing congestion control schemes are used to address the impact caused by priority-based flow control (PFC), such as congestion spreading, and victim flow problems. However, in some special cases, this problem is not solved. Through observation, we examine the interaction between flow control and congestion control, and realize that the mismatch between hop-by-hop flow control and end-to-end congestion feedback, as well as inaccurate rate regulation, are the root causes of frequent PFC triggering. Therefore, we propose Egress Queue Congestion Information Notification (EQCIN). EQCIN implements threshold-based flow identification to avoid packet buildup due to congestion spreading being considered as the root cause of congestion, while using direct feedback from the congestion side to reduce unnecessary link loss. For different flow identifiers, EQCIN adopts different algorithms to achieve targeted rate control. Experimental results show that EQCIN can reduce the number of PFC PAUSEs tends to zero, compared to TIMELY, DCQCN, DCQCN+TCD and improve the link utilization by 7%-77%, respectively.

In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile users (MUs) during task offloading or the allocation of profits between two parties during the allocation of limited resources (i.e., the resource competition). In this paper, we jointly optimized these two problems. First, to reduce the task offloading delay and the service interruption due to movement, we develop a mobility-aware model, based on which we propose the MWBS algorithm to select the appropriate offloading base station (BS) for MUs. Second, considering the resource competition and the delay constraint of the task, we develop a double auction model and then propose the DARA algorithm, which efficiently allocates the BS resources and maximizes the total system revenue (i.e., social welfare) through a multi-session auction. Finally, we combine MWBS and DARA to propose the BS resource allocation algorithm called MD-BSRA in mobile scenarios. Simulation results show that MD-BSRA can effectively improve task offload success rate, total system revenue and resource utilization while reducing offload delay and service interruption.

Traditional flowlet routing is a traffic distribution-based network routing algorithm that avoids packet disorder problems. In dynamic network environments, however, this approach may lead to suboptimal performance and network congestion. In this paper, we design a multi-agent flowlet level routing optimization (MAFRO) framework that combines multi-agent deep reinforcement learning (MADRL) and graph neural networks (GNN). MADRL uses multiple agents that interact with the network environment and learn through a trial-and-error process to optimize flowlet routing. Meanwhile, MADRL uses the properties of GNN to model the network topology, interacting and capturing the complex relationships between network nodes. MAFRO enables agents to make more informed and adaptive routing decisions based on the current state of the network, leading to better end-to-end delay and packet loss rate performance. Experimental results demonstrate that MAFRO achieves better performance than the baseline algorithm.

By offloading computation-intensive tasks to the edge cloud, mobile edge computing (MEC) has been regarded as an effective technology for enhancing computational capacity and extending the battery lifetime of mobile devices (MDs). However, due to the limitation of bandwidth and computing resources in MEC, unreasonable task offloading might lead to intensive resource competition, which recedes the performance gains benefit from offloading. When the tasks are latency-sensitive, a proper task offloading strategy is more important. Considering the heterogeneous delay constraints and resource competition comprehensively, we aim at minimizing the energy consumption of MDs subject to the individual delay constraints of tasks by jointly optimizing the task offloading and resource allocation in terms of wireless channel and remote computation capacity in a multi-MD MEC system in this paper. Due to the complexity of the primal optimization problem, a heuristic algorithm is devised. In the algorithm, a subset of tasks to be offloaded is incrementally constructed, and the corresponding offloading sub-problem is then repeatedly solved for this task subset using a two-stage algorithm until the total energy consumption can no longer be further reduced. The first stage of solving the sub-problem is to find the optimal full offloading scheme for the to-offload tasks, which is proved to be a convex optimization problem. For the task subset without a full offloading solution, an effective iterative algorithm is employed in the second stage where the channel allocation and computing resource allocation are optimized alternately. A great number of experiments are given to verify the performance of the proposed algorithm. We observe that the heuristic algorithm shows different performance when adopting different task ordering schemes. The proposed heuristic algorithm is evaluated against three reference schemes, and the results show that it can save up to 14.20% of energy consumption while guaranteeing the delay requirements of all tasks.

Binding of short antigenic peptides to major histocompatibility complex (MHC) molecules is a core step in adaptive immune response. Precise identification of MHC-restricted peptides is of great significance for understanding the mechanism of immune response and promoting the discovery of immunogenic epitopes. However, due to the extremely high MHC polymorphism and huge cost of biochemical experiments, there is no experimentally measured binding data for most MHC molecules. To address the problem of predicting peptides binding to these MHC molecules, recently computational approaches, called pan-specific methods, have received keen interest. Pan-specific methods make use of experimentally obtained binding data of multiple alleles, by which binding peptides (binders) of not only these alleles but also those alleles with no known binders can be predicted. To investigate the possibility of further improvement in performance and usability of pan-specific methods, this article extensively reviews existing pan-specific methods and their web servers. We first present a general framework of pan-specific methods. Then, the strategies and performance as well as utilities of web servers are compared. Finally, we discuss the future direction to improve pan-specific methods for MHC-peptide binding prediction.

Energy efficient dense wireless sensor network design makes extensive use of spatial redundancy and appropriate MAC mechanism. CSMA is integrated with TDMA while filtering out the spatial correlation with a so-called HS-Sift MAC protocol. The entire sensing region is divided into three sub-areas for different channel access methods. The nodes near to the border sleep most of the time, nodes close to the event claim high channel utilisation and those lie in between compete under different priorities. A software simulation verifies effectiveness of the proposed scheme for energy consumption and access delays.