As a promising distributed computing paradigm, edge computing (EC) enhances service quality and reduces service latency by deploying computational and storage resources at the network edge. However, due to edge servers' geographic distribution and resource constraints, EC is challenged by edge denial-of-service (EDDoS) attacks. Although various approaches have been proposed to mitigate EDDoS attacks, the impact of capacity uncertainties in edge server processing capacity and transmission latency has been largely overlooked. This limits the adaptability and robustness of mitigation strategies in edge computing environments, which could result in overload and paralysis of edge servers. To address this issue, this paper introduces uEDDoS-D, an uncertainty-aware EDDoS mitigation approach based on an improved deep deterministic policy gradient algorithm. uEDDoS-D models the uncertainties of computing capacities and transmission latency of edge servers in EC environments and leverages the collective computational resources of edge servers to mitigate EDDoS attacks without relying on traditional attack detection mechanisms. uEDDoS-D aims to minimize the impact of uncertainties in edge server processing capacity and transmission latency on mitigation decisions while effectively reducing service latency. Experimental results demonstrate that uEDDoS-D significantly reduces service latency, outperforming the state-of-the-art approaches by an average of 16.75%, and enhances system robustness in EDDoS attack scenarios.

Multivariate time series anomaly detection is crucial for the reliability and stability of operations in cloud-edge computing systems. However, fast model training requirements, unlabeled datasets, and high-dimensional time series made it challenging to build a model that can quickly and accurately localize anomalies with good robustness. Therefore, this paper proposes a robust anomaly detection approach with spatiotemporal representation learning for multivariate time series (RAD-SRL) to address the above challenges. RAD-SRL leverages improved Dilated Causal Convolutions (DCNs) to extract spatiotemporal representations in MTS and incorporates a multi-head self-attention mechanism and an Update Gate Recurrent Neural Network (UGRNN) to capture multiple-dimensional information. Moreover, the self-adjusting mechanism of RAD-SRL reduces computational resource consumption and improves the generalizability of the model. Experimental studies on four public datasets demonstrate that the RAD-SRL outperforms the baseline methods in terms of both detection performance and time efficiency.

In the industrial Internet, intelligent operation and maintenance can ensure the secure and stable operation of industrial software. To achieve effective intelligent operation and maintenance, it’s essential to conduct time series anomaly detection within software systems and other devices. To enhance the detection effectiveness, extensive research has been conducted. However, multivariate time series are composed of high-dimensional, high-noise, and random data. These anomalies are both subtle and dense. It is difficult to detect anomalies accurately. The increased complexity of existing methods results in lower operational efficiency. To address these issues, this paper proposes the time series anomaly detection method DG-LSFNet. DG-LSFNet adeptly captures feature correlations across various temporal states to extract additional valuable insights. Next, DG-LSFNet establishes long-term and short-term temporal correlations. It can capture normal information within short intervals between anomalies, thereby reducing false alarm in anomaly detection. Then, DG-LSFNet approximate and unearth the original data information to improve anomaly detection performance. In addition, DG-LSFNet reduces time complexity by simplifying the model structure and effectively improves the efficiency of anomaly detection. This paper conducts experiments to compare DG-LSFNet with state-of-the-art methods on four benchmark datasets. The experimental results indicate that DG-LSFNet outperforms state-of-the-art methods in terms of anomaly detection performance, enhancing the interpretability of anomaly detection.

As smart mobile applications increasingly demand timely situational awareness and energy efficiency, the Age of Information (AoI) metric plays a vital role in maintaining data freshness. This need is further supported by the End-Edge-Cloud Computing (EECC) paradigm, which enhances application performance by facilitating task offloading to the edge or the cloud. However, existing AoI optimization solutions focus solely on task offloading, often neglecting critical aspects such as system resource allocation and energy efficiency, which can lead to resource waste, increased energy consumption, compromised Quality of Service (QoS), and system performance degradation. Therefore, this paper investigates the joint optimization of task offloading, communication and computing resource allocation in EECC systems, aiming to minimize AoI and energy consumption under constraints of deadlines and capacity constraints. To address this problem, we divide the decision space into multiple non-intersecting decision areas based on the characteristics of the studied problem and design a task offloading and resource allocation algorithm based on slow-movement particle swarm optimization (SPSO) to handle each decision area individually. In the algorithm design, we customize the position, velocity, update rules, and fitness function for the optimization problem. Finally, extensive simulation-based and testbed experiment results show that the proposed algorithm can save up to 14.56% of energy consumption, shorten AoI by up to 27.80%, and improve utility (weighted sum of AoI and energy consumption) by up to 15.89% compared with existing algorithms.

When a large grid disturbance occurs, the grid-following inverter is prone to cause transient instability due to the weak damping or negative damping induced by a synchronous reference frame phase-locked loop. First, the transient instability mechanism and characteristics of grid-following inverters are analyzed. As the grid disturbance increases, the transient damping will decrease or even become negative, and the transient stability of the system will deteriorate. To address this issue, a constant damping phase-locked loop (CD-PLL) is proposed, which can achieve a constant positive damping control. Meanwhile, the working principle, characteristics, and design method of the proposed CD-PLL are introduced in detail. The proposed CD-PLL can not only enhance the transient stability of the system but also improve the dynamic performance. Finally, the theoretical analysis and the proposed CD-PLL are verified by simulation and experimental studies.

A new concept of center processing unit (CPU)-integrated simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed, namely computing STAR-RIS. Computation-intensive and delay-sensitive tasks from mobile users can be partially processed at the computing STAR-RIS. We aim to minimize the energy consumption of users and the computing STAR-RIS, and formulate a joint task offloading and transmission resource allocation problem. The solution of this problem is affected by the offloading decision and the amplitude and phase-shift of the computing STAR-RIS. To solve the non-convex problem, we decompose it into two subproblems: 1) For the task offloading subproblem, the offloading decision is optimized utilizing the Karush-Kuhn- Tucker (KKT) conditions; and 2) For the transmission resource allocation subproblem, the transmission-reflection coefficient matrix are optimized via successive convex approximation (SCA). Simulation results show that our proposed algorithm can converge faster and have lower energy consumption than the conventional STAR-RIS.