To cope with the challenges of the rapid growth of Industrial Internet of Things devices, deploying cache servers (CS) at the edge of the network becomes a promising solution. The cache node (CN) can reduce request latency and the traffic load on backhaul link by pre-caching relevant data generated by sensors. However, the storage resources of CSs are limited, and select the appropriate data item to cache is a critical issue for improving efficiency. In addition, we introduce the Age of Information (AoI) to describe the freshness of cached data. However, increasing the freshness of data through frequent caching updates could result in increased energy consumption of sensor. In this paper, we study the caching problem of multiple CNs, and further model the problem as a multi-agent Markov Decision Process to minimize long-term request latency, AoI, and energy consumption. Furthermore, to solve this problem, we propose a variant of the Soft Actor-Critic-based multi-agent caching algorithm. Finally, experiments show that the proposed algorithm can converge to a better caching strategy and has better performance in multiple objectives than existing algorithms.

The integration of satellite communications with mobile edge computing (MEC) into space-air-ground integrated networks, known as satellite edge computing (SEC), has become a crucial research field for future communication systems to provide extensive global coverage services. This paper investigates the joint dependent task offloading and resource allocation problem for remote Internet-of-Things (IoT) applications within the SEC architecture. The proposed system leverages unmanned aerial vehicles (UAVs) as mobile access points and edge servers and utilizes low- earth orbit (LEO) satellites and ground stations as cloud computing resources. Multiple applications with dependent tasks from IoT devices (IoTDs) are modeled as directed acyclic graphs (DAGs). To address the challenges of reducing the system cost in UAV-assisted SEC, we first propose a one-to-many matching algorithm to associate IoTDs with UAVs. Then, a multi-application task sequence algorithm is devoted to merging the multiple DAGs and sorting the task order. Finally, a graph-aware asynchronous multi-agent reinforcement learning approach empowers the agents to autonomously discover optimal offloading and resource allocation strategies. Extensive simulations based on real-world datasets demonstrate the effectiveness of the proposed approach in minimizing the system costs while meeting application latency requirements, outperforming other benchmark algorithms.

The deployment of multimodal large language models (MLLMs) in edge-cloud networks faces critical challenges, including computational resource heterogeneity, memory bottlenecks, and bandwidth constraints. To address these issues, we propose EdgeSD, a novel framework that accelerates MLLM inference by integrating speculative decoding (SD) with edge-cloud collaboration. First, EdgeSD decouples the vision encoding and decoding processes of the draft MLLM across heterogeneous edge servers (ESs). This disaggregation architecture overcomes single-node memory constraints, enabling optimized resource utilization and high-resolution input processing. Second, to resolve the communication bottleneck and computational burden inherent in this distributed architecture, EdgeSD integrates a bandwidth-aware dynamic image token merging (ITM) method. Unlike general pruning techniques, this EdgeSD-specific ITM method focuses on minimizing inter-ES transmission latency for vision-decoding disaggregation while maintaining draft quality. Third, to optimize SD efficiency on consumer-grade ESs, EdgeSD employs an adaptive and scalable token tree structure solved using a parallel delta-stepping algorithm. This structure maximizes the number of accepted tokens under strict edge latency constraints. Extensive experiments on six multimodal datasets and five benchmarks with various MLLM pairs demonstrate that EdgeSD achieves substantial acceleration and throughput gains in edge-cloud collaboration scenarios using a lightweight draft MLLM, achieving 3.04-5.12x speedup compared to baseline methods.

With the rapid development of 6G and Internet of Vehicles (IoV) technologies, the volume of computation-intensive tasks generated by intelligent vehicles is growing exponentially. Given limited onboard processing capabilities, vehicles increasingly rely on edge servers deployed by service providers (SPs) at roadside units to offload tasks. Vehicle clients can offload the tasks to SPs to mitigate their onboard computation load, while SPs derive economic benefits through the provision of computation resources. However, this interaction introduces a conflict of interest, as vehicles aim to minimize their offloading costs, while SPs seek to maximize revenue. To address this problem, we propose SPOR, a Stackelberg game-based service priority-aware computation offloading and resource pricing scheme in IoV. SPOR is a hierarchical game-theoretic framework in which SPs act as leaders setting prices, while vehicles act as followers determining their offloading strategies. A novel service prioritization function is introduced, incorporating booking price, system load, and reputation to ensure fair and balanced resource allocation. We provide a theoretical proof of the existence and uniqueness of a Nash equilibrium. Extensive experiments on a real-world vehicle edge computing dataset show that SPOR outperforms baseline methods in delay, energy consumption, average load, and task completion rate. Notably, SPOR maintains task completion rates above 97% even under heavy workloads, demonstrating its effectiveness in enhancing system reliability and overall performance.

The Mobile Edge Computing (MEC)-Empowered Internet of Vehicles (IoVs), a transformative paradigm characterized by real-time decision-making and distributed processing via data offloading to the network edge, has garnered substantial research interest. Nonetheless, emerging applications such as autonomous driving pose dual challenges of computational bottle necks and inadequate wide-area coverage in resource scheduling. To this end, we propose a Digital Twin (DT)-enhanced, satellite assisted resource allocation framework that synergistically coordinates MEC and satellite networks while leveraging DTs for network virtualization and collaborative training, thereby fully exploiting the low-latency capabilities of edge computing, the high-capacity processing of satellite computing, and the cost efficient simulation advantages of DT technology. During the training phase, we develop DR4SV, a Deep Reinforcement Learning (DRL)-based online optimization method that constructs specialized intelligent agents tailored for satellite-collaborative IoV scenarios. By incorporating depthwise over-parameterized convolutional layers, DR4SV enables multi-channel synergistic processing across heterogeneous communication channels while establishing robust policy alignment between physical agents and their DT counterparts. This architecture facilitates real-time state mapping and multi-dimensional parallel simulation within the digital space, enhancing resource allocation efficiency while dynamically balancing latency and energy consumption with accelerated convergence. Extensive experiments are conducted based on real-world datasets, encompassing hyperparameter con figurations and rigorous performance evaluations. The numerical results demonstrate that DR4SV achieves reductions in both time and energy consumption, with optimization rates reaching up to 6.8% and 19.1%, respectively. Meanwhile, the correlation between DTs and physical entities reaches up to 97%, thereby validating the effectiveness and feasibility of DT technology in satellite-terrestrial integrated vehicular networks.

With the rapid advancement of communication technologies, smart factories increasingly rely on Mobile Edge Computing (MEC) to manage the surging volume of task requests. The computational burden on industrial sites can be effectively mitigated when computation-intensive tasks are offloaded to nearby edge servers by MEC. However, the limited computational and storage capacities of edge servers, combined with their susceptibility to dynamic failures in industrial environments, pose significant challenges for reliable and efficient task offloading. To overcome these challenges, we propose a Robust Task Offloading Method for Smart Factories (RTOMSF). By utilizing digital twin technology, RTOMSF provides real-time monitoring and prognostic fault detection for edge servers. Moreover, we enhance the Group Relative Policy Optimization algorithm by incorporating a noisy linear layer into the policy network, thereby improving the robustness and adaptability of task offloading decisions in a dynamic environment. Extensive experiments conducted on realistic industrial scenarios demonstrate that RTOMSF consistently surpasses state-of-the-art baselines in both reliability and efficiency.

Smart ports rely on the low-latency and high-reliability data processing capabilities of mobile edge computing (MEC) to sustain the stable operation of core tasks such as real-time terminal scheduling. However, extreme events like typhoons trigger a surge in heterogeneous multi-source data, leading to im-balances in edge computing resource supply and demand, which in turn degrade the responsiveness and accuracy of emergency responses. To address the challenge of dynamic task offloading in MEC-enabled smart ports under such risks, this paper proposes a Risk-Aware Proximal Policy Optimization (RAPPO) algorithm. RAPPO integrates a multi-dimensional risk-aware mechanism into the traditional PPO framework, incorporates a dynamic reward function for proactive risk perception and response, and enhances convergence stability and robustness via clipped target restriction and advantage estimation. Experiments based on typhoon data from the China Meteorological Administration and port throughput data from the Fujian Provincial Department of Transportation show that RAPPO outperforms benchmark algorithms in terms of average response latency, energy consumption, and the number of overdue tasks, confirming its effectiveness and feasibility.

With the rapid evolution of artificial intelligence (AI) technologies, supporting computing-intensive and latencysensitive applications in resource-constrained environments has become increasingly challenging. In response, we propose APPOMNLS, a multi-objective optimization approach for Satellite Edge Computing (SEC) that targets application response latency, energy consumption, and on-time completion rates. It integrates a proximal policy optimization (PPO) with a self-attention mechanism under a multi-neighborhood local search framework. The Transformer-based self-attention module enhances the PPO network's representational capability, while multi-neighborhood local search switches flexibly between global and local exploration of the solution space. Experiments based on Iridium-NEXT constellation Two-Line Element (TLE) data demonstrate that our approach clearly outperforms its peers in terms of terminal response speed, energy efficiency and on-time application completion rates. APPO-MNLS brings value to SEC with the capability of guaranteeing reliable and effective global satellite network service.

The integration of Mobile Edge Computing (MEC) into the Industrial Internet of Things (IIoT) has markedly improved resource accessibility and propelled digital-intelligent advancements. Nevertheless, the substantial costs of MEC infrastructure also pose critical challenges to incentive mechanism design. Specifically, there is still an absence of a standardized and widely recognized incentive framework for the dynamic and non-cooperative interactions between edge service requesters and providers. Furthermore, the highly complicated characteristics of IIoT necessitate a greater reliance on dependable and trustworthy edge resource provision than other paradigms, which implies that human-centric factors (e.g., credit) are equally crucial as profit-driven metrics (e.g., price) in incentive design. To tackle these challenges, we propose In3Edge, a Stackelberg game-based incentive mechanism that systematically considers the interplay between profit-driven and interest-oriented indicators while accommodating heterogeneous peers, subjective interest divergences, and objective resource disparities. Particularly, leveraging convex optimization theory, we provide rigorous proofs and in-depth analyses of the intrinsic properties of In3Edge, encompassing the concavity/convexity of utility functions, equilibrium solution boundaries, optimal responses under peer/interest heterogeneity, and closed-form solutions for symmetric multipeer scenarios while articulating a series of propositions and theorems to underpin future research. Finally, extensive experiments are constructed under diverse dynamically changing scenarios with distinct characteristics, confirming the strong motivational capabilities of In3Edge in MEC-empowered IIoT.

The deployment of multimodal large language models (MLLMs) on resource-constrained mobile devices poses significant challenges due to their high computational demands. This paper introduces a novel two-timescale optimization framework for efficient MLLM inference in Edge-Cloud networks, addressing the problem of multi-timescale resource management by jointly optimizing slow-timescale MLLMs deployment decisions and fast-timescale batch scheduling, GPU resource allocation, and bandwidth allocation under dynamic network conditions and spatiotemporal request heterogeneity. Our key innovation is a hierarchical twin delayed deep deterministic policy gradient (HALTD3) algorithm that integrates attention mechanisms and long short-term memory networks to optimize slow-timescale MLLMs deployment and fast-timescale resource allocation, minimizing weighted system costs including deployment cost, end-to-end latency, and energy consumption, while meeting stringent quality-of-service requirements. Extensive experiments demonstrate that the HALTD3 algorithm substantially outperforms baseline methods in reducing system costs across diverse MLLM workloads and dynamic network scenarios, validating its effectiveness for practical edge-cloud collaborative inference.

The predominant obstacles confronting contemporary cyber-physical systems (CPSs) are their extensive heterogeneity and stringent constraints, such as diverse applications and real-time service requirements. While the incorporation of mobile edge computing could alleviate some of these constraints, challenges persist in equilibrating services and loads due to the finite computational resources of edge servers. Concurrently, the implementation of associated modules or methodologies has been prompted by escalating apprehensions regarding service security, which may have a detrimental impact on the performance of CPS, particularly in terms of resource occupation and service overhead. To this end, we develop an end-edge-cloud-collaborative CPS framework in which tasks are modeled as workflow applications, followed by constructing a multi-stage service-enhanced method named SerFlow. In SerFlow, a relevance-aware security precaution mechanism is devised, which evaluates the connection hierarchy and correlation metrics across subtasks, subsequently establishing the service anti-conflict mechanism to augment security levels. Particularly, a comprehensive evaluation score for security precaution levels is provided, which enables the security precaution to engage in the following optimization operations and evaluation modules as a quantifiable metric. Leveraging the non-Euclidean geometry framework, we then develop a Pareto frontier modelling method that integrates Newton-Raphson and geodesic while a survival value evaluation strategy with correlation constraints is involved in accomplishing coarse-grained cluster selection. Subsequently, an improved value-based and model-free deep reinforcement learning algorithm is suggested to generate fine-grained service strategies in end-edge-cloud collaborative scenarios. Finally, comprehensive experiments demonstrate the effectiveness of SerFlow in achieving enhanced security precaution levels while maintaining superior service efficiency.

With the explosive growth of information and the continuous expansion of applications, Industrial Internet of Things (IIoT) is facing huge data processing and storage pressure. With mobile edge computing (MEC) technology, the computing power network connects the geographically distributed computing nodes and then coordinates the allocation and scheduling of resources, transmits data, and eventually relieves the pressure of the industrial site. However, the rigorous demands of IIoT for real-time and stability pose some daunting challenges. To this end, we propose an online computation offloading approach with dual stability guarantee, named OCODSG. Specifically, the Lyapunov function is used to optimize the stability of the virtual queue, and the system stability is optimized based on the network jitter measurement. Moreover, the Dueling Double Deep Q Network (D3QN) algorithm based on deep reinforcement learning (DRL) is used for model autonomous training, while Gaussian noise is added to the network parameter space to encourage exploration and enhance algorithm robustness. Finally, experimental results on both simulated and real datasets demonstrate that OCODSG improves service efficiency and system stability.

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme.

The growing need for applications that require significant computational power and high responsiveness has significantly driven the advancement of multi-access edge computing (MEC), with satellite edge computing (SEC) emerging as a formidable solution for regions where devices are marooned in areas with sparse computational resources. We present a study on enhancing task scheduling and resource allocation efficiency under the SEC framework, introducing a novel system model that simulates a heterogeneous network characterized by variable bandwidths, channel gains, and transmission powers. We propose a tailored SEC architecture that addresses stringent latency requirements and devise a dynamic scheduling method that adjusts task priorities based on urgency. Our experiments, grounded in realistic parameters from the Iridium and OneWeb satellite constellations, demonstrate the efficacy of our algorithm. The findings underscore significant improvements in managing the SEC landscape, providing robust solutions that enhance overall system performance and reliability in global service networks.

Summary By providing distributed and ultra‐low‐latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher‐quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness‐aware task offloading method, called FOIMAM. Specifically, the ‐norm is introduced to accommodate the Pareto plane under the non‐Euclidean geometry framework while the evaluation and elimination of low‐quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi‐constraint problem and resolved using weighted max‐min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees.

Computation offloading involves transmitting compute-intensive tasks from mobile users (MUs) to edge environments. This compensates for the limitations of terminal devices in computing performance and resource storage capabilities. However, with the growing demand for offloading compute-intensive tasks, the burden on edge server networks has intensified. On the other hand, the response time of application has increased, leading to a decline in user experience. Therefore, selecting a reliability metric caused by congestion becomes an important issue for analyzing the quantitative characteristics of edge networks. The current research primarily focuses on the precise validation offloading task results. Typically, methods like task redistribution and third-party assistance are employed to ensure task reliability. However, these measures lead to increased time and energy consumption. In this paper, we propose a more versatile reliability metric based on the probability distribution of average waiting time in a multi-queue model. Additionally, to incentive MUs to offload more tasks and enhance the economic utility of edge server providers (ESPs), we employ a Stackelberg game to model the dynamic interaction between ESPs and MUs. Finally, we utilize the alternating direction method of multipliers (ADMM) algorithm to derive the optimal strategies for ESPs and MUs. Simulation results demonstrate that our proposed approach surpasses the baselines in terms of reliability indicator. Moreover, it achieves faster convergence and decision-making in comparison to conventional heuristic methods.

With the significant increase in the number of motor vehicles, road-related issues such as traffic congestion and accidents have also escalated. The development of an accurate and efficient traffic flow forecasting model is essential for helping car owners plan their journeys. Despite advancements in forecasting models, there are three remaining issues: (i) failing to effectively use cyclical data; (ii) failing to adequately capture spatial dependencies; and (iii) high time complexity and memory usage. To tackle the aforementioned challenges, we present a novel Spatial-Temporal Graph Attention Gated Recurrent Transformer Network (STGAGRTN) for traffic flow forecasting. Specifically, the use of a Spatial Transformer module allows for the extraction of dynamic spatial dependencies among individual nodes, going beyond the limitation of only considering neighboring nodes. Subsequently, we propose a Temporal Transformer to extract periodic information from traffic data and capture long-term dependencies. Additionally, we utilize two additional classical techniques to complement the aforementioned modules for extracting characteristics. By incorporating comprehensive spatial-temporal characteristics into our model, we can accurately predict multiple nodes simultaneously. Finally, we have successfully optimized the computational complexity of the Transformer module from O(n2) to O(nlogn). Our model has undergone extensive testing on four authentic datasets, providing compelling evidence of its superior predictive capabilities.

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Owing to the breakthrough in mobile wireless communication technologies, almost everyone has been immersed into social networks, while fake news and misinformation are also being pushed into people’s minds with astonishing speed and breadth. The rising disparity between limited computing resources and the exploding news size necessitates innovative solutions to handle the challenge posed by booming data volume and make it more likely to differentiate fake news. In response to the aforementioned dilemma, the social-aware computation offloading system is analyzed, where the digital twin (DT) paradigm is used to simulate tasks offloading and assess the associated costs. Next, to obtain the best offloading choice, we fully consider the social relationship constraints and further propose an online task execution method that includes two stages of cluster selection and computing offloading, named TOFDS. Specifically, it exploits the technologies from multiobjective optimization and deep reinforcement learning (DRL) and realizes the joint optimization of resource utilization, load balancing, service latency, and energy consumption. Eventually, the comparative experiments demonstrate that TOFDS performs well when dealing with fake news data and can adapt to changes in dataset size and service clusters.

Internet-of-Things (IoT) has played a critical role in developing sustainable smart cities and emerging numerous latency-sensitive IoT applications. Mobile edge computing (MEC) federation has the capability to incorporate a transparent resource management approach, which enables the sharing and utilization of MEC services from edge infrastructure providers (EIPs) and provides agile access services to mobile devices (MDs). In this paper, we investigate the joint optimization problem of computation offloading, task migration, and resource allocation in the MEC federation. The objective is to minimize the weighted sum of latency and energy consumption while maintaining load balancing under the constraint of the long-term migration cost budget of EIPs. To address the problem, we decompose it into two sub-problems: 1) the MDs clustering sub-problem and 2) the sub-problem of joint computation offloading, task migration, and resource allocation. Firstly, an MDs clustering matching (MDCM) algorithm is proposed to cluster the MDs in edge servers (ESs) according to the differences in channel gains. Afterward, the second sub-problem is simplified by the Lyapunov optimization technique, and then we propose a Transformer-based mobility prediction model and a decentralized deep deterministic policy gradient (DDPG)-based framework to solve it. Extensive simulation results demonstrate the cost-efficiency of the proposed algorithm.

Industry 5.0 facilitates the intelligent upgrade of smart factories in Industrial Internet of Things (IIoT), and also introduces a plethora of data processing challenges. Mobile edge computing offloads data to edge servers for processing, easing the data processing pressure and reducing system cost. However, smart factories contain numerous sensitive information, and offloading them to edge servers directly may pose a risk of data leakage. To address these challenges, we investigate a localedge collaborative smart factory system. Specifically, we firstly model the tasks as a directed acyclic graph, and formulate the offloading problem as a Markov decision process, considering the optimization of latency, energy consumption and the number of overtime tasks. Then, we propose a security-aware computation offloading method using federated reinforcement learning in IIoT, named SCOF. SCOF employs federated learning, keeping data for local computation and uploading model parameters to edge servers for aggregation. The data transmission passes through an artificial noise channel to protect against eavesdropping. Meanwhile, SCOF utilizes differential privacy to protect data security and employs deep reinforcement learning for selecting near-optimal offloading decisions. Finally, abundant experiments are conducted under a real dataset. The results show that SCOF has better perfomance than the state-of-the-art baseline algorithms.

The widespread adoption of Industrial Internet of Things (IIoT) has significantly transformed various aspects of industrial manufacturing. However, the massive volume and complexity of IIoT data highlight the need for innovative solutions to enhance the overall performance of IIoT systems. In this regard, mobile edge computing, assisted by deep reinforcement learning, can alleviate the burden on IIoT systems through computation offloading. Nevertheless, in increasingly digitized industrial environments, how to make real-time, efficient task offloading decisions remains a subject of deep exploration. To address this issue, we propose a two-stage resource allocation and task offloading method, named MCORM. In the first stage, we use the Combinatorial Upper Confidence Bound algorithm, based on the combinatorial multi-armed bandit problem, to solve the resource allocation problem. In the second stage, the Multi-Agent Proximal Policy Optimization algorithm is employed to determine the approximate optimal offloading strategy. Specifically, the Convolutional Block Attention Module is utilized to process observation information, focusing on important features. Finally, extensive experiments are conducted using both simulated and real datasets. The results demonstrate that our proposed algorithm MCORM can reduce latency and energy consumption effectively, promoting efficient industrial production.

Abstract Nowadays, as the smart campus concept becomes a reality, virtual reality (VR) applications are being applied to connect students with the virtual teaching world via VR devices (VDs), enhancing learning efficiency. Nevertheless, VR applications are latency‐sensitive while VDs are subjected to shortcomings to handle many VR applications simultaneously. Fortunately, mobile edge computing (MEC) has been recognized as a promising solution that can bring abundant resources to VDs to relieve hardware limits. However, the computing resources of edge nodes in MEC are limited, and thus how to allocate resources effectively is critical. Meanwhile, some sensitive information of students collected by VD needs to be protected. In view of this, we investigate computation offloading for educational VR applications in MEC‐enabled smart campus. The aims of this issue are to optimize the motion‐to‐photon latency, energy consumption, and resource utilization while satisfying the privacy and security constraints. To this end, we propose a new multi‐objective optimization method using MoCell. Eventually, the experimental evaluations are designed to illustrate the effectiveness and superiority our proposed method.

With the rapid growth of the Industrial Internet of Things, a large amount of industrial data that needs to be processed promptly. Edge computing-based computation offloading can well assist industrial devices to process these data and reduce the overall time overhead. However, there are dependencies among tasks and some tasks have high latency requirements, so completing computation offloading while considering the above factors faces important challenges. In this paper, we design a computation offloading method based on a directed acyclic graph task model by modeling task dependencies. In addition to considering traditional optimization objectives in previous computation offloading problems (e.g., latency, energy consumption, etc.), we also propose an age of information (AoI) model to reflect the freshness of information and transform the task offloading problem into an optimization problem for latency, energy consumption, and AoI. To address this issue, we propose a method based on an improved dueling double deep Q-network computation offloading algorithm, named ID3CO. Specifically, it combines the advantages of deep Q-network, double deep Q-network, and dueling deep Q-network algorithms while further utilizing deep residual neural networks to improve convergence. Extensive simulations are conducted to demonstrate that ID3CO outperforms the existing baselines in terms of performance.

Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.

Mobile edge computing (MEC), which is regarded as a promising paradigm, is proposed to provide smart cities that are supported by the Internet of Things (IoT) with low processing latency at the edge of the network, by offloading latency-critical tasks from MDs to edge service providers (ESPs). In this paper, we study the interaction between ESPs and MDs by formulating a Stackelberg game model, to optimize the strategies of computation offloading and resource allocation of the MDs, and the prices and investment spending on the privacy level of ESPs. Additionally, the social effect of MDs on privacy concerns is incorporated to study the impacts on the payoffs of players. We utilize distributed Alternating Direction Method of Multipliers (ADMM) algorithm to address the Stackelberg equilibrium problem in a distributed manner. Finally, numerical results illustrate that our proposed scheme can jointly achieve the maximum profits of ESPs and utilities of MDs.

With the emergence of the Internet of things (IOT), smart cities have changed from concept to reality. Meanwhile, those countless IOT devices are scattered in every corner of the city which generate a mass of sensing big data and IoT services every minute. However, the computing capability of IoT devices is so constrained that IoT devices fail to process computational-intensive services. Mobile edge computing (MEC) is an emergent architecture for reinforcing the computing capabilities of IoT devices to cope with the resource-hungry IoT services. In the MEC system, IoT devices are capable of offloading part of IoT services to the cloudlet for execution. Although offloading can prominently mitigate the computing burden on IoT devices, it may result in enormous transmission cost and consuming resource of cloudlets. Therefore, it poses major challenges to how to achieve trade-offs in terms of time cost, energy cost of IOT devices and resource utilization of cloudlets. In consideration of the challenge, we devise a collaborative computation offloading approach to acquire the above trade-off in the collaboration of IoT device-cloudlet-cloud three ends. Firstly, the balancing strategies of the trade-off are obtained by leveraging the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We then employ a multi-criterion decision-making method that combines the entropy weight (EW) method and technique for order preference by similarity to an ideal solution (TOPSIS), is known as (EW-TOPSIS), to acquire the optimum offloading decision in the acquired balancing strategies. Finally, extensive experiments and theoretical analysis validate the effectiveness of the proposed method.

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs‐oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi‐MU offloading scheme. Then we survey edge server‐ (ES‐) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Fog computing, as the supplement of cloud computing, can provide low‐latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty‐two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.