With the rapid development of ubiquitous networks and unmanned devices, air quality monitoring data is increasingly collected via wireless networks from sensors at multiple monitoring stations. However, the complexity of these data introduces significant challenges. Existing spatiotemporal methods for air quality prediction often struggle with issues such as inadequate handling of spatial relationships, difficulties in modeling long-term temporal dependencies, and limited generalization capabilities. To address these challenges, this paper proposes a novel spatiotemporal modeling approach—Spatiotemporal λ-Bernstein Graph Convolutional Network with Transformer for Multi-Site Air Quality Prediction (ST-BernT). This method constructs a graph structure based on spatiotemporal correlations, integrating a Gaussian-weighted adjacency matrix derived from geographic distances with an adjacency matrix capturing the temporal correlations of pollutant concentration time series, thereby precisely modeling spatial dependencies. Subsequently, a dynamic filter adjusted by λ-Bernstein polynomials is proposed to adaptively process spatiotemporal data characterized by the coexistence of low- and high-frequency components on the graph. Furthermore, a hierarchical generative transformer (GPHT) is introduced to enhance the model’s ability to capture long-term temporal patterns, such as periodicity and seasonality, while supporting parallel prediction across multiple sites, significantly improving computational efficiency and accuracy. Experimental results demonstrate that ST-BernT exhibits strong accuracy, adaptability, and generalization capability in multi-site air quality prediction tasks, particularly showing enhanced robustness in large-scale long-term forecasting scenarios.

Ubiquitous, low-latency intelligence at the network edge is central to large-scale Internet of Things (IoT) deployments, yet effectively coordinating communication, computing, and backhaul operations across heterogeneous layers remains challenging. This paper presents a unified cross-layer framework for terrestrial–satellite edge computing IoT systems that integrates Non-Orthogonal Multiple Access (NOMA)-based terrestrial access with local, satellite, and cloud execution. Unlike conventional terrestrial Multi-access Edge Computing (MEC)/edge– cloud scheduling, we jointly optimize partial offloading and path selection over a NOMA-coupled uplink and a multi-hop satellite edge/cloud execution chain under end-to-end latency coupling. The framework jointly determines path selection and partial offloading to minimize a latency–energy objective using accurate end-to-end models. Within this framework, two complementary scheduling algorithms are developed. The Centralized Optimal Cross-Layer Scheduler (COCS) formulates the joint scheduling problem as a mixed-integer nonlinear program (MINLP) with logarithmic and bilinear terms. It employs the spatial branch-and-bound (sBB) method within a commercial solver to obtain a global solution, serving as a performance benchmark. The Decentralized Game-Theoretic Scheduler (DGTS) models user decisions as an ordinal potential game (OPG) and achieves distributed convergence via best-response dynamics (BRD), enabling scalability and adaptability to large networks. Extensive simulations demonstrate that COCS achieves the global optimum while DGTS attains near-optimal performance with much lower complexity. These results validate the effectiveness of the proposed cross-layer framework and highlight the importance of coordinated communication–computation–backhaul design for terrestrial–satellite integrated edge computing.

Convolutional Neural Networks (CNN) have been widely adopted in multi-access edge computing system due to their powerful feature extraction and high inference accuracy. Harnessing the heterogeneous computational capacities of end de vices and edge servers, end-edge collaborative inference addresses resource constraints of end devices and latency challenges of real time inference. However, existing studies typically assume that all potential inference models are pre-employed on the server, which may not always align with real-world deployment scenarios. Moreover, the economic incentives of edge service providers are often overlooked in current distributed inference research. To address these issues, we propose an end-edge collaborative inference framework for CNN in multi-access edge computing system, named DisCNN. We first model the interaction between latency-sensitive end devices (EDs) and resource-constrained edge service providers as a Stackelberg game and prove the existence of a Subgame Perfect Equilibrium. Next, we derive the optimal response characteristics of the EDs and develop an efficient algorithm to allocate computation and communication resources to EDs while partitioning the CNN accordingly. To compute the optimal offloading set, we introduce a linear-time algorithm with a bounded approximation ratio. Then, to jointly optimize caching, resource allocation, and pricing, we design an efficient approximate algorithm to compute a near-optimal solution. Finally, extensive simulation results validate the effectiveness of the proposed framework.

Federated learning (FL) enables collaborative model training without exposing raw data, offering a privacy-aware alternative to centralized learning. However, FL remains vulnerable to various privacy attacks that exploit shared model updates, including membership inference, property inference, and gradient inversion. Source inference attacks further threaten FL by identifying which client contributed a specific training sample, posing severe risks to user and institutional privacy. Existing source inference attacks mainly assume passive adversaries and overlook more realistic scenarios where the server actively manipulates the training process. In this paper, we present an enhanced source inference attack that demonstrates how a malicious server can amplify behavioral differences between clients to more accurately infer data origin. Our approach introduces active training manipulation and data augmentation to expose client-specific patterns. Experimental results across five representative FL algorithms and multiple datasets show that our method significantly outperforms prior passive attacks. These findings reveal a deeper level of privacy vulnerability in FL and call for stronger defense mechanisms under active threat models.

Sequential recommendation generates embedding representations from historical user-item interactions to recommend the next potential interaction item. Due to the complexity and variability of historical user-item interactions, extracting effective user features is quite challenging. Recent studies have employed sequential networks such as time series networks and Transformers to capture the intricate dependencies and temporal patterns in historical user-item interactions, extracting more effective user features. However, limited by the scarcity and suboptimal quality of data, these methods struggle to capture subtle differences in user sequences, which results in diminished recommendation accuracy. To address the above issue, we propose a contrastive learning framework with LLMs-based view augmentation (CLLMRec), which effectively mines differences in behavioral sequences through sample generation. Specifically, CLLMRec utilizes LLMs (Large Language Models) to augment views and expand user behavior sequence representations, providing high-quality positive and negative samples. Subsequently, CLLMRec employs the augmented views for effective contrastive learning, capturing subtle differences in behavioral sequences to suppress interference from irrelevant noise. Experimental results on three public datasets demonstrate that the proposed method outperforms state-of-the-art baseline models, and significantly enhances recommendation performance.

Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectiveness in capturing complex patterns and learning meaningful representations. However, the above methods are constrained by hardware memory, hindering the detection for large-scale graph data. In this paper, we propose a Memory-Efficient framework for large-scale attributed Graph Anomaly Detection (MEGAD). Specifically, MEGAD first generates node embeddings and then refines them through a lightweight joint optimization model, ensuring minimal memory overhead. The optimized embeddings are subsequently fed into a detector to compute anomaly scores. Extensive experiments demonstrate that our framework achieves comparable accuracy to state-of-the-art methods across multiple datasets while significantly reducing memory consumption on large-scale graphs.

Abstract With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

Serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the function instances with inter-dependencies are scheduled to proximate edge nodes in a distributed manner. However, the heterogeneity and unpredictability of edge networks bring significant challenge in realizing optimal scheduling decision to guarantee execution performance of applications without any prior. In view of this challenge, a Serverless Function Scheduling Method, named SFSM, is proposed in this paper for FaaS applications over edge computing. First, a long-term optimization problem is formulated to reduce completion time and decoupled into time-slot sub-problems via Lyapunov optimization. To avoid the cross-edge redundant data transmission overhead of inter-functions, a two-level graph optimization is designed with vertical and horizontal data merging. Then, SFSM incorporates an online multi-armed bandit-based scheduling algorithm that only requires the context of requests without complete information of edge networks. Finally, extensive experimental results based on real-world datasets demonstrate the effectiveness and superiority of SFSM.

Abstract In the past few years, latency‐sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self‐driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across‐domain business task computing on distributed edge servers. With this observation, a trade‐off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low‐latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low‐latency edge server. The experiments and comparison analysis verify the availability of our method.

Abstract The advent of federated learning (FL) has presented a viable solution for distributed training in edge environment, while simultaneously ensuring the preservation of privacy. In real‐world scenarios, edge devices may be subject to label noise caused by environmental differences, automated weakly supervised annotation, malicious tampering, or even human error. However, the potential of the noisy samples have not been fully leveraged by prior studies on FL aimed at addressing label noise. Rather, they have primarily focused on conventional filtering or correction techniques to alleviate the impact of noisy labels. To tackle this challenge, a method, named DETECTION, is proposed in this article. It aims at effectively detecting noisy clients and mitigating the adverse impact of label noise while preserving data privacy. Specially, a confidence scoring mechanism based on local intrinsic dimensionality (LID) is investigated for distinguishing noisy clients from clean clients. Then, a loss function based on prototype contrastive learning is designed to optimize the local model. To address the varying levels of noise across clients, a LID weighted aggregation strategy (LA) is introduced. Experimental results on three datasets demonstrate the effectiveness of DETECTION in addressing the issue of label noise in FL while maintaining data privacy.

Generally, a collaborative business process is a distributed process, in which a set of parallel business processes are involved. These business processes have complementary competencies and knowledge, and cooperate with each other to achieve their common business goals. To ensure the correctness of collaborative business processes, we propose a novel plan-based correctness enforcement approach in this article, which is privacy-preserving, available and efficient. This approach first requires participating organizations to define their business processes. Then, each participating organization employs a set of reduction rules to build the public process of its business process, in which all internal private activities and the flows formed by them are removed. Next, a set of correct plans is generated from these public processes. A plan is essentially a process fragment without alternative routings. From the external perspective (i.e., ignoring all internal private activities and the flows formed by them), a parallel execution of the business processes corresponding to these public processes follows only one such plan. Lastly, each participating organization independently refactors its business process using these resulting correct plans. Using the message places (corresponding to the actual communication interfaces), these refactored processes are composed in parallel. Thus, a correct and loosely coupled enforced process is constructed. This approach is evaluated on actual collaborative business processes, and the experimental results show that compared with state-of-the-art enforcement proposals, it can achieve correctness enforcement while protecting the business privacy of organizations and is available. Meanwhile, it is also more efficient and scalable, even a collaborative business process with tens of millions of states can be enforced within a few seconds.

When an emergency occurs, disposal needs to be built to reduce the risk imposed on life, property, and environment. Generally, the disposal is organized as a cross-organization emergency response process (CERP). To achieve better-emergency response services, its correctness analysis is an important task that needs to be dealt with at design time. In this article, we propose a novel correctness analysis approach for CERPs. Given a CERP, this approach first decomposes it into a set of instance nets. Then, it excludes the invalid instance nets (each corresponds to an incomplete process instance) and adopts the stubborn set to check the structural correctness of each valid instance net without considering resource factors, as well as introduces a structure-based resource analysis (SRA) method to determine whether the resources in it are sufficient. Finally, it determines the correctness of the CERP by comparing the numbers of the correct instance nets and all valid instance nets. If the CERP is incorrect, it returns the correct instance nets, which capture the parts of the CERP that can be executed correctly. This approach is evaluated on an actual data set, and the comparison results show that it outperforms the state-of-the-art technique in terms of effectiveness and efficiency.

Since the data-aware business process usually covers the control flow and data flow, and both of them have a direct impact on its execution, it is challenging to ensure its correctness. In this article, we propose a novel correctness enforcement approach for the data-aware business the processes. Given a data-aware business process, this approach first relies on the notion of the execution paths to capture all the parts of it that can be executed correctly. More specifically, it first splits the data-aware business process into a set of the execution paths, and then presents the notion of well-formedness to determine whether each execution path is correct. Based on these captured correct execution paths, an execution path-oriented strategy is generated, which is nonintrusive, and can enforce it to follow one of its correct execution paths during each execution, thereby realizing its correct execution. This approach is evaluated using the extensive experiments, which shows that it is effective and efficient, as well as scalable in practice.

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this article, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks.

With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of nonlinearity, nonstationary, and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the autoregressive integrated moving average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real data sets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-the-art baselines.

The Intelligent Meteorological System (IMS) with cloud computing (CC) provides users with various meteorological services, but the long-distance communication of CC often brings high latency, which makes the IMS perform poorly in the series of real-time services for meteorological emergency decision-making. Considering the shortcomings of CC, edge computing is adopted to the IMS to process most service requests. In the IMS with cloud-edge computing, the commonly used contents of services is cached on edge servers (ESs) to reduce resource scheduling, thus avoiding high time costs. Due to the storage and computing resource limitations of ESs, the massive types of services and the changing service requests, how to determine the caching contents is still a challenge. In this paper, a service caching scheme based on deep reinforcement learning, named SCDR, is proposed. Specifically, a service caching framework for IMS with cloud-edge computing is designed. Then, the distributed distributional deep deterministic policy gradient (D4PG) is leveraged to realize the optimization of the service caching strategy with the highest service coverage rate and low processing latency. Besides, the performance of the generated caching strategy is evaluated through simulation experiments.

Summary Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software‐defined network (SDN)‐based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm‐III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time.

The Internet of Vehicles (IoV) environment consists of a number of latency-critical and data-intensive application (e.g., real-time video analytics). In this article, we posit the potential of leveraging the sixth-generation (6G) mobile networks to minimize communication delay, particularly for latency-critical task execution. In particular, the 6G-enabled network in boxes (NIBs) deployed in the vehicles can communicate in real time with the edge servers or the NIBs in other vehicles. Although NIBs are capable of providing dynamic and flexible computing resources to support real-time IoV services, there are significant energy costs associated with the communication and computing activities. Seeking to achieve an optimal balance between energy consumption and time cost during service migration, we design a NIB task migration (NTM) method for IoV in this article. In our approach, the IoV framework is designed and the routing mechanism is established. The strength Pareto evolutionary algorithm (SPEA2) is then utilized to determine the migration strategy. Findings from our experiments demonstrate the reliability and efficiency of our proposed approach.

Abstract Microservices architecture provides a promising solution for developing sustainable cyber‐physical systems (CPSs). However, checking the compatibility of CPSs over a set of microservices communicating asynchronously via unbounded buffers are undecidable due to their infinite state spaces. In this article, we propose a new approach for checking the compatibility of CPSs with infinite state spaces without restricting the size of buffers or the number of communication cycles. First, we integrate CPSs with microservice architecture and design the system architecture for building CPSs over a set of cyber‐physical microservices with unbounded buffers. Second, we model CPSs composed of asynchronously communicating cyber‐physical microservices via FIFO buffers as labelled transition systems. Third, we adopt the stability notion and present a sufficient condition for checking the unspecified receptions of CPSs through stability checking. Finally, we implement our approach in Process Analysis Toolkit for automatic compatibility checking and conduct experiments to show our approach is effective and efficient.

Traffic flow prediction plays a critical role in reducing traffic congestion in transportation systems. However, accurate traffic flow prediction becomes challenging due to the impact of complex spatio-temporal (ST) correlations and the diversity of ST correlations. When modeling complicated ST correlations, researchers usu did not take the diversity of ST correlations into consideration, resulting in poor prediction accuracy. In this paper, we propose ST-InNet, a deep spatio-temporal Inception network for collectively predicting traffic flow in each city region. Specifically, ST-InNet employs two Inception networks to simultaneously capture various spatial and temporal correlations of traffic data, including temporal closeness, temporal periodicity, nearby spatial dependencies, and distant spatial dependencies. For the diversity of spatial correlations, ST-InNet presents an improved variant of an Inception module to explicitly capture the different contributions of spatial correlations for each region. For the diversity of temporal correlations, ST-InNet designs a fusion component to explicitly model the varying contributions of temporal correlations on prediction. The experiments are conducted on a real-world traffic dataset in Nanjing, demonstrating that ST-InNet outperforms five state-of-the-art baselines in short-term and long-term traffic flow predictions with an average accuracy improvement of 32.09% and 30.97%, respectively.

Collaborative business processes gather a set of business processes with complementary competencies and knowledge to cooperate to achieve more business successes. To ensure their successful implementation, correctness is a key issue that needs to be addressed during their development. To this end, a novel correctness enforcement approach is proposed to support the development of collaborative business processes. In this approach, we first give an algorithm to check the correctness of an original process specified by Petri nets. Then, we prune its reachability graph to obtain its core in case of partially correct, which is a reduced reachability graph that doesn't cover invalid states. Finally, we generate a set of controllers from the core using coordination mapping (i.e., inserting some coordination activities into controllers), and then an enforced process is built by the composition of the original process and the controllers. Our approach is implemented as an analysis module called cetool in the PIPE (Platform Independent Petri Net Editor) and it is validated on a set of real-world cases. The results show the effectiveness and efficiency of the proposed approach.

With the development of the vehicular network (VN), emerging driver assistance applications are adhibited in daily life. Commonly, edge computing is adopted to satisfy the timeliness requirements of these applications, as the vehicular devices are usually insufficient in computation resources. Nevertheless, the increasing volume of service requests (SRs) are potential to overload the edge servers (ESs), thus increasing the task execution time. Besides, the randomness and the diversity of the SRs also challenge the dynamic resource allocation for the users. To deal with these challenges, a task offloading and resource allocation scheme based on game theory and reinforcement learning (RL) named TORA is proposed. Specifically, game theory is leveraged to determine the optimal task offloading strategy for improving the quality of service (QoS). Meanwhile, RL is applied to implement the dynamic resource allocation of the ES. Finally, the robust performance of the proposed method is validated by comparative experiments.

Nowadays, Edge computing is being introduced as a powerful paradigm to collaborate with the cloud to provide sufficient computing resources for IoT to implement intelligent analysis and data mining. Generally, due to the edge nodes are closer to mobile users, the access latency and the cost of using cloud services are effectively reduced. However, the implementation of cross-layer resource scheduling between edge nodes and servers deployed in the cloud to meet service requirements (i.e., shortest completion time, maximum resource utilization, lower energy consumption, etc.) still faces great challenges. To address this challenge, a cross-layer resource scheduling method, named CRSM, for the IoT applications is proposed in this paper. Technically, the Pareto archived evolution strategy (PAES) is employed to optimize the time cost of IoT applications, resource utilization and energy consumption of edge node. Then, the technique for order preference by similarity to ideal solution (TOPSIS) and the multiple criteria decision making (MCDM) are leveraged to acquired the optimal cross-layer resource scheduling strategy. Finally, the comprehensive analysis of CRSM is introduced in detail.

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.

Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.

Microservice Architecture is a new development paradigm that transforms the traditional business-oriented information management system into the collaborative work of microservices. Microservices are usually distributed in a loosely coupled manner in the network. In order to coordinate tasks, microservices must coordinate their executions through message interactions with each other. Therefore, modeling and analyzing the interaction between microservices becomes a key issue. The choreography defines the interaction contract between services, and the realizability analysis is the key task to ensure the correct implementation of the microservices interaction contract. The choreography is realizable if interaction contract satisfies choreography specification. This paper use CSP# to analyze the realizability of microservice choreography under synchronous communication and bounded asynchronous communication, and a solution is proposed to repair the unrealizable microservice choreography and made it realizable.