Function as a Service (FaaS) is a popular cloud computing service model that incorporates an auto-scaling mechanism, enabling applications to dynamically adjust computing resources, achieving rapid response to load changes and efficient resource utilization. However, the limited resource allocation mode for function containers can frequently cause function performance degradation before scaling is complete, so some FaaS platforms address this issue by default through a shared-resource mode. But existing constant target load-based autoscalers fail to perceive node-level load under this mode, leading to numerous scaling decisions to nodes that have already reached their load bottlenecks, without bringing actual resource or performance gains. This makes the system underutilized and even degrades its performance. To solve this issue, in this paper, we design a horizontal autoscaler, NDScaler, which efficiently scales functions in shared-resource mode by using the node load-aware scaling strategy, thereby eliminating invalid scaling behaviours and the resulting degradation of function performance. We implement this strategy through the proposed node load-aware and dynamic target load algorithm, which models the scale-up problem as a load transfer problem between nodes and functions and adopts a greedy search strategy to identify the optimal target functions for scale-up. Furthermore, it introduces a dynamic load target to assess the extent of load reduction for functions and accurately scales functions down. We have implemented NDScaler and evaluated it in detail on the OpenFaaS platform. Experimental results show that, compared with existing methods, NDScaler can ensure scaling effectiveness and achieve high-efficiency scaling in both simple single-function scenarios and complex multifunction scenarios, effectively improving function throughput while significantly reducing latency.

Federated Learning (FL) has gained widespread attention due to its strong privacy protections and collaborative learning capabilities. Recently, Personalized Federated Learning (PFL) has garnered significant attention for its ability to address statistical heterogeneity. Most existing PFL methods either focus on feature extraction, struggling to balance collaborative learning and personalization, or emphasize dynamic weight adjustments, relying on heuristic designs that lead to lower communication efficiency in large-scale federated learning systems. However, these methods fail to effectively integrate these two aspects to achieve both efficient collaborative learning and personalized goals. To address these issues, this paper proposes an Adaptive Feature-Driven Weighting Based Personalized Federated Learning (FedAFW) approach. FedAFW first utilizes local feature representations to guide the generation of global and personalized weights, enhancing the personalization effect. Subsequently, it uses gradient similarity for weight allocation, balancing the relative contributions of the global and personalized models, thus improving overall performance. Experiments on diverse datasets under heterogeneous settings show that FedAFW improves accuracy by up to 5.84%, boosts communication efficiency by 54.6%, and outperforms advanced methods in scalability and stability, demonstrating its robustness in handling statistical heterogeneity.

Medical image segmentation is crucial for separating tissue structures and anatomical regions. However, due to significant variations in size, shape, and density of target tissues in medical images, this task faces many challenges. Neural networks are widely used in medical image segmentation due to their powerful feature extraction and pattern recognition capabilities. But traditional Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies, and Transformer models may lack sufficient channel interaction and detail representation. To address the above issues, this paper proposes a novel architecture called TSNet, which innovatively integrates SimAM (Neural Attention Module) and Triplet Attention mechanism. First, Triplet Attention adopts a three-branch structure to effectively encodes channel and spatial information. By reducing information loss and achieving direct correspondence between channels and weights, it significantly enhances the model’s feature extraction and representation capabilities in complex medical image processing. Meanwhile, the parameter-free SimAM module generates adaptive 3D attention weights by optimizing the energy function, further optimizing the interaction and fusion between features. Finally, extensive experiments on real datasets for heart and CT segmentation have shown that the proposed TSNet performs significantly better than the baseline method in terms of Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95).

With the support for data-intensive, rate-hungry and delay-sensitive applications, 6G enabled massive IoT is surely becoming the most potential computing paradigm. Along with this trend, the scale of mobile devices and data traffic in the network is increasing explosively, resulting in huge transmission pressure on the backbone network, accompanied by serious security problems. All above call for a secure and high-throughput data communication system for 6G enabled massive IoT. In this paper, an Active-Trust based security Service Orchestration (ATSO) framework is proposed. First, the active-trust evaluation mechanism is introduced at the data acquisition layer, and direct trust is combined with indirect trust to accurately evaluate the trust of data providers. Then, service orchestration mechanism is proposed, which orchestrates data into services through edge devices to implement the service-oriented architecture, and conducts progressive aggregation at routing layer to form more advanced services. Extensive simulation results demonstrate that ATSO effectively improve performance in data security, energy efficiency and delay. Finally, we discuss the potential challenges in promoting the study of ATSO.

Abstract Due to the frequent sourcing of Intelligent Electronic Devices (IEDs) from third-party sources, they are highly susceptible to targeted attacks on Substation Automation Systems (SASs). However, most current anomaly detection methods are ineffective against insider stealthy attacks, which simulate benign operations to mask malicious behavior. Furthermore, the lack of annotated datasets within current SAS environments hinders the training of various detection methods. Therefore, this paper introduces TranRAT, a lightweight anomaly detection model for insider stealthy attacks in SAS, which employs unsupervised learning and deep Transformer to adapt to scene requirements. TranRAT is designed to detect covert internal attacks initiated by untrusted IEDs within SAS environments. Initially, it identifies and extracts thirteen critical features from Generic Object Oriented Substation Event messages, emphasizing system-wide characteristics over individual device specifics. Subsequently, it applies suitable label expansion strategies to capture temporal correlations and employs attention-based sequence encoders to bolster robust adversarial training. Experimental results demonstrate that TranRAT surpasses baseline methods. Compared to leading models for multivariate time-series data, TranRAT achieves a 15%–60% enhancement in F1 scores on complete and limited training datasets, while reducing training duration by up to 99%.

As a collaborative and open network, billions of devices can be free to join the IoT-based data collection network for data perception and transmission. Along with this trend, more and more malicious attackers enter the network, they steal or tamper with data, and hinder data exchange and communication. To address these issues, we propose a Proactive Trust Evaluation System (PTES) for secure data collection by evaluating the trust of mobile data collectors. Specifically, PTES guarantees evaluation accuracy from trust evidence acquisition, trust evidence storage, and trust value calculation. First, PTES obtains trust evidence based on active detection of drones, feedbacks from interacted objects, and recommendations from trusted third parties. Then, these trust evidences are stored according to interaction time by adopting a sliding window mechanism. After that, credible, untrustworthy, and uncertain evidence sequences are extracted from the storage space, and assigned with positive, negative, and tendentious trust values, respectively. Consequently, the final normalized trust is obtained by combining the three trust values. Finally, extensive experiments conducted on a real-world dataset demonstrate PTES is superior to benchmark methods in terms of detection accuracy and profit.

With the advantages of maneuverability and low cost, Unmanned Aerial Vehicles (UAVs) are widely deployed in mobile edge computing as micro servers to provide computing service. However, tasks usually require a large amount of energy and have strict time constraints, while the battery energy and endurance of UAVs are limited. Therefore, energy consumption and delay have become key issues in such architectures. To address this issue, an Entropy Normalized Soft Actor-Critic (ENSAC) computation offloading algorithm is proposed in this paper, aiming to minimize the weighted sum of task offloading delay and energy consumption. In ENSAC, we formulate the task offloading problem as a Markov Decision Process (MDP). Considering the non-convexity, high-dimensional state space, and continuous action space of this problem, the ENSAC algorithm fully combines deviation strategy and maximum entropy reinforcement learning, and designs a system utility function under entropy normalization as a reward function, thus ensuring fairness in weighted energy consumption and delay. What’s more, ENSAC algorithm also considers UAV trajectory planning, task offloading ratio, and power allocation in the UAV-assisted MEC system. Therefore, compared with previous methods, ENSAC algorithm has stronger stability, better exploration performance, and can handle more complex environments and larger action space. Finally, extensive experiments demonstrate that, in both energy-saving and delay-sensitive scenarios, the ENSAC algorithm can quickly converge to the optimal solution while maintaining stability. Compared with four benchmark algorithms, it reduces the total system cost by 52.73%.

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. However, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this paper. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%-21.87% and reducing cost by 18.8%-50.32%.

Future heterogeneous services and applications require the provisioning of unprecedented massive user access, heterogeneous data traffic, high bandwidth efficiency, and low latency services in next generation multiple access. In response to the requests from these services and applications, a large number of workers with scattered computing power need to be managed uniformly and scheduled in an efficient manner to perform various tasks. Therefore, task allocation has become a crucial issue to determining whether next generation multiple access can support future heterogeneous services and applications. In this paper, we propose a novel Two-stage Multi-task Allocation method based on Discrete Particle Swarm Optimization (TMA-DPSO). TMA-DPSO is easy to implement and has good search efficiency, which is suitable for large-scale task allocation in next generation networks. Under TMA-DPSO, we redefine the particles in discrete coding form, iteratively update the position and velocity based on the individual optimal particles and the global optimal particle, and finally obtain a corrected optimal solution. Unlike previous methods that only focused on the first-stage task allocation, we make full use of workers’ remaining time to perform second-stage redundant task allocation, which can not only increase workers’ income, but also potentially improve fault tolerance and security. As far as we know, this is the first attempt to utilize the remaining time after first-stage allocation. Finally, we evaluate TMA-DPSO extensively using the synthetic and real-life datasets. The results demonstrate that whether in a compactly or uniformly distributed scene, TMA-DPSO outperforms three benchmark methods by increasing 2.15%-42.24% platform revenue and 6.1%-46.63% workers income.

UAV-assisted wireless communications facilitate the applications of Internet of Things (IoT), which employ billions of devices to sense and collect data with an on-demand style. However, there are numerous malicious Mobile Data Collectors (MDCs) mixing into the network, stealing or tampering with data, which greatly damages IoT applications. So, it is urgent to build a ubiquitous trust communication system. In this paper, a UAV-assisted Ubiquitous Trust Evaluation (UUTE) framework is proposed, which combines the UAV-assisted global trust evaluation and the historical interaction based local trust evaluation. We first propose a global trust evaluation model for data collection platforms. It can accurately eliminate malicious MDCs and create a clean data collection environment, by dispatching UAVs to collect baseline data to validate the data submitted by MDCs. After that, a local trust evaluation model is proposed to help select credible MDCs for collaborative data collection. By letting UAVs distribute the data verification hash codes to MDCs, the MDCs can verify whether the exchanged data from the interacted MDCs is reliable. Extensive experiments conduct on a real-life dataset demonstrate that our UUTE system outperforms the existing trust evaluation systems in terms of accuracy and cost.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

Billions of devices are connected to the Internet of Things (IoT). These devices generate a large volume of data, which poses an enormous burden on conventional networking infrastructures. As an effective computing model, edge computing is collaborative with cloud computing by moving part intensive computation and storage resources to edge devices, thus optimizing the network latency and energy consumption. Meanwhile, the software-defined networks (SDNs) technology is promising in improving the quality of service (QoS) for complex IoT-driven applications. However, building SDN-based computing platform faces great challenges, making it difficult for the current computing models to meet the low-latency, high-complexity, and high-reliability requirements of emerging applications. Therefore, a cloud-mobile edge computing (MEC) collaborative task offloading scheme with service orchestration (CTOSO) is proposed in this article. First, the CTOSO scheme models the computational consumption, communication consumption, and latency of task offloading and implements differentiated offloading decisions for tasks with different resource demand and delay sensitivity. What is more, the CTOSO scheme introduces orchestrating data as services (ODaS) mechanism based on the SDN technology. The collected metadata are orchestrated as high-quality services by MEC servers, which greatly reduces the network load caused by uploading resources to the cloud on the one hand, and on the other hand, the data processing is completed at the edge layer as much as possible, which achieves the load balancing and also reduces the risk of data leakage. The experimental results demonstrate that compared to the random decision-based task offloading scheme and the maximum cache-based task offloading scheme, the CTOSO scheme reduces delay by approximately 73.82%-74.34% and energy consumption by 10.71%-13.73%.

The oceans cover more than 71% of the Earth's surface and have a surging amount of data. It is of great significance to seek energy-effective and ultrareliable communication and transmission mechanism for effectively gathering abundant maritime data. In this article, we propose an autonomous underwater vehicle (AUV)-assisted data gathering scheme based on clustering and matrix completion (ACMC) to improve the data gathering efficiency in the underwater wireless sensor network (UWSN). Specifically, we first improve the K-means algorithm by adopting the Elbow method to determine the optimal K and setting a distance threshold to select the separate initial cluster centers. Then, we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary nodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we present an in-cluster data collection mechanism based on matrix completion. An extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.

Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

Energy‐efficient data gathering techniques play a crucial role in promoting the development of smart portable devices as well as smart sensor devices based Internet of Things (IoT). For data gathering, different applications require different delay constraints; therefore, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed to improve the delay differentiated services constraint that is missed from previous data gathering studies. The DSDR scheme has three advantages: first, DSDR greatly reduces transmission delay by establishing energy‐efficient routing paths (E2RPs). Multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. Secondly, DSDR intelligently chooses transmission method according to data urgency: the direct‐forwarding strategy is adopted for delay‐sensitive data to ensure minimum end‐to‐end delay, while wait‐forwarding method is adopted for delay‐tolerant data to perform data fusion for reducing energy consumption. Finally, DSDR make full use of the residual energy and improve the effective energy utilization. The E2RPs are built in the region with adequate residual energy and they are periodically rotated to equalize the energy consumption of the network. A comprehensive performance analysis demonstrates that the DSDR scheme has obvious advantages in improving network performance compared to previous studies: it reduces transmission latency of delay‐sensitive data by 44.31%, reduces transmission latency of delay‐tolerant data by 25.65%, and improves network energy utilization by 30.61%, while also guaranteeing the network lifetime is not lower than previous studies.