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.

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.

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.

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.