Satellite edge computing (SEC) extends the capabilities of task offloading from the ground to near-Earth space by leveraging the wide coverage of satellites. However, the high-speed movement and long-range communication of satellites lead to Doppler shifts and signal loss, which can result in a high bit error rate (BER) and degrade communication link quality. As a result of the high BER, error correction is required, inevitably incurring additional delay during task offloading. Therefore, in this paper, we focus on the problem of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u>rror <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>orrection aware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u>ependency <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u>ask <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u>ffloading (EC-DTO) in satellite environments. By jointly considering task dependencies and BER-induced error correction delays, we formulate the EC-DTO problem as a constrained optimization problem with the objective of minimizing the makespan of users' tasks. To solve this problem, we propose APOS, which is an event-driven offloading framework based on task-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>rioritization and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u>ptimistic-finish-time-based node <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>election. Specifically, APOS adopts an online offloading framework that triggers scheduling upon task arrivals and completions with task dependency awareness. It iteratively updates task readiness and performs offloading decisions to reduce the overall makespan in the SEC environment. Simulation results show that APOS outperforms the representative methods, lowering the average makespan by 20.7% and the deadline violation ratio by 84.5% on average across all cases.

Deploying ultra-reliable and low-latency communication (URLLC) service function chains (SFCs) is imperative for applications demanding stringent latency and reliability performances. In these applications, ensuring uninterrupted service hinges on establishing fault-disjoint primary and backup service function paths (SFPs). However, existing techniques for deploying SFCs fall short in optimizing latency differentials between the primary and backup SFPs, posing risks of service disruptions in critical URLLC applications like remote surgery, smart factory, and unmanned vehicle systems. In this work, we investigate pioneering techniques to efficiently optimize the primary and backup SFP latencies while minimizing their differentials. We formally formulate the problem of ultra-reliable and low-latency SFC deployment (URLLC-SD) and show its NPhardness. We develop an innovative algorithm, the Yen-based SFP Identification in Layered Graph (YANG), which optimizes the equal-weight composite latency objectives with symmetric QoS/SLA for primary and backup SFPs at the expense of runtime complexity. Through extensive simulations, we demonstrate the YANG's superiority, surpassing state-of-the-art benchmarks. In particular, YANG achieves the highest acceptance rates under specific constraints on SFP latency and latency differentials. Furthermore, our analysis reveals interesting insights into the selection of good path candidates to optimize URLLC-SD.

Microservice architecture is widely adopted in cloud computing for building large-scale distributed systems. However, due to frequent calls between microservices, such systems become increasingly complex and fragile, leading to reduced reliability of the system behavior. Therefore, implementing process discovery and constructing process models for the historical logs of microservice systems becomes crucial to analyzing system behavior. However, existing process discovery methods do not effectively capture the synchronous or asynchronous call relations between microservices at the endpoint level, limiting the ability to analyze the impact of microservice interactions on system performance. This paper proposes a novel process discovery capable of capturing call relations in microservice systems at the endpoint level. First, the call events related to the microservice calls are extracted from the interleaved fragmented raw logs by event serialization to form event sequences. Then, an invocation tree is defined and constructed, and pruning is applied to obtain the subsequence that can be used to derive call relations correctly. Based on the defined event relations, the call relations between endpoints are discovered. Finally, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-based miner is applied to perform the mining and generation of the process model to obtain the defined microservice call net. Experimental results show that, compared to state-of-the-art methods, the proposed approach can accurately and effectively build the microservice system process model.

Using low-rank adaptation to fine-tune pre-trained neural network models has attracted widespread attention due to its advantages of low resource requirements, high precision, and no additional inference delay. However, most existing methods are designed for large language models based on the Transformer structure and lack adaptation to convolutional neural networks (CNN) widely used on Internet of Things (IoT) devices. In addition, IoT devices are usually deployed outdoors and collect large amounts of data that are affected by environmental conditions. Providing a highly robust CNN model is a prerequisite for providing high-quality services. To this end, this paper proposes a highly robust seed-filter adaptation method (SeFA) for pre-trained CNNs. SeFA introduces an adaptation branch with the same structure as the backbone network. In the adaptation branch, some filters are first designated seed filters and grouped. Then, additional filters are generated from the grouped seed filters and nonlinear transformation functions (NLFs) with different hyperparameters. The seed filters' parameters are updated during model training, and the NLF hyperparameters are randomly initialized and frozen. Both grouping seed filters and configuring NLFs with non-learnable hyperparameters can improve the model's robustness. This is because grouping seed filters can generate diverse filters on demand without increasing the model's complexity, and the NLFs' rules can regularize the model. It is worth mentioning that the number of fine-tuning parameters of the pre-trained model that can adapt to downstream tasks can be flexibly controlled by specifying the number of seed filters. The key idea of this paper is to propose SeFA with flexible, controllable, learnable parameters, high robustness, and adaptability to pretrained CNN models, thereby facilitating fine-tuning on resource-constrained IoT devices and providing highly robust visual services. Experimental results on the CIFAR-10, CIFAR-10-C, CIFAR-100, CIFAR-100-C, and Icons-50 datasets demonstrate that the proposed SeFA outperforms other state-of-the-art methods. Specifically, on the ResNet-152 model and the CIFAR-10-C dataset, the accuracy of our SeFA is about 7% higher than that of the full fine-tuning method. In addition, we verify the efficiency of SeFA-based models for fine-tuning and inference on the device.

The increasing availability of Web APIs has amplified the complexity of mashup creation, where developers must identify compatible and functionally relevant APIs based on often ambiguous natural language descriptions. Traditional methods also fall short in capturing hierarchical semantic cues, modeling compatibility, and aligning with developer intent. Although large language models (LLMs) offer strong generalization capabilities, they remain unreliable in mashup recommendation due to hallucinated outputs, limited controllability, and token-length constraints when dealing with large-scale API repositories. To overcome these limitations, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LIMR</i>, an intent-aware mashup recommendation framework that combines LLM-augmented semantic reasoning with structured, multi-scale neural modeling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LIMR</i> first prompts a LLM to extract high-level intent from user requirements, which serves as a global semantic signal. This intent is fused with low-level, multi-scale features extracted by a convolutional encoder, which are designed to capture fine-grained lexical/phrasal patterns at different granularities and provide precise semantic grounding for API matching. These heterogeneous representations are further contextually refined through a Transformer-based interaction module. To handle nonlinear semantic dependencies and compositional complexity, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LIMR</i> integrates a Kolmogorov-Arnold Network (KAN) with learnable activation functions, enhancing the model's capacity to capture intricate feature interactions. The entire framework is optimized via LLM, incorporating auxiliary objectives such as mashup category prediction and API quality estimation to guide generalization and reduce overfitting. Comprehensive experiments on the ProgrammableWeb and APIBench datasets show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LIMR</i> significantly outperforms state-of-the-art baselines, which the ranking-oriented metrics, including NDCG and mAP, achieves improvements of 17.1%–34.2% over the strongest competitors. These results confirm the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LIMR</i>'s hybrid design in delivering precise, robust, and intent-aware mashup API recommendations, especially in scenarios where LLMs alone fail to meet accuracy and scalability demands.

Cross-chain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration services for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) solutions for cross-chain data exchange across multiple domains are constrained by synchronous networks, hindering efficient data exchange in intermittent network environments. Furthermore, there is a lack of research on asynchronous cross-chain transaction pool mechanisms, which are crucial for optimizing system utility. In this paper, we propose AC-BaaS, an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>synchronous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u>ross-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u>lockchain <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>s <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ervice framework tailored for the multi-domain IoT. Built upon a specially designed asynchronous sidechain architecture, the system leverages a committee to provide AC-BaaS for data exchange across multiple IoT domains. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We further propose a heuristic for optimizing the utility of the buffer pool mechanism to strike a balance between performance and resource consumption. We also examine DAS delay size settings to trade-off security and efficiency. We analyze and prove the security of AC-BaaS, simulate asynchronous communication environments under various security levels, and conduct a comprehensive evaluation. The results show that AC-BaaS outperforms SOTA schemes, improving throughput by an average of 1.71 to 5.09 times, reducing transaction latency by 64.36% to 85.49%, and maintaining comparable resource overhead.

The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance.

Data collection and distributed task execution in Internet of Things (IoT) networks require efficient coordination among autonomous agents to handle the growing volume of sensing and computational demands. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present promising candidates for these operations due to their complementary capabilities and mobility advantages. However, effective cooperation between these heterogeneous agents faces significant challenges including communication limitations, energy constraints, and suboptimal task allocation efficiency. In this paper, we aim to maximize data collection capacity, task completion rates, while minimizing energy consumption across all UAVs. We propose U2GNet, a novel UGV-assisted framework for UAV that enables efficient task offloading and resource allocation in dynamic environments by leveraging Deep Reinforcement Learning (DRL) enhanced with Heterogeneous Graph Attention Networks (HGAT). The framework employs HGAT to process local observations and information shared by neighboring agents, while Gated Recurrent Units (GRU) address partial observability by integrating historical information, and DRL optimizes the decision-making process. Simulation results demonstrate that U2GNet improves the average data collection rate and task completion rate by 16.90% and 10.81% respectively compared to the baseline HGN approach.

Deep graph clustering aims to uncover latent structural patterns within user behavior graphs to effectively group users based on behavioral similarity, thereby enhancing the efficiency of personalized service recommendation. However, most existing generative clustering methods heavily rely on encoder–decoder architectures and their pretraining procedures, which not only increase training complexity but also result in insufficient robustness and unstable optimization. To address these limitations, this paper proposes an Anchor-Guided Graph Clustering Network (AGCN) that eliminates the need for pretraining. The proposed method introduces an anchor-guided learning strategy that automatically selects high-confidence anchor samples farthest from the distribution boundary and utilizes the k-means++ algorithm to generate pseudo-labels in the diffusion feature space. Furthermore, AGCN integrates an anchor expansion strategy and a commonality enhancement mechanism to significantly improve clustering discriminability and recommendation accuracy. Theoretical analysis and extensive experiments on multiple real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both clustering accuracy and stability.