## 2 Related Work
### Transmission Efficiency  
Some studies focus on improving the data transmission efficiency of MEC systems. For instance, [PERSON] et al. [17] proposed an effective task offloading scheme to address access control and task offloading problems in multiple-user MECs, while [PERSON] et al. [18] suggested a stable offloading strategy based on the preferences of specific tasks and edges. At the same time, the performance of such offloading strategies depends on both communication and computing resources, which impact data transfer rates, the energy use of terminal devices, and task processing times. Therefore, it is worthwhile to discuss how to manage communication and computing resources effectively in the Industrial Internet. In addition, early research on MEC prioritized enhancing the performance of MEC systems. For instance, for the best allocation of resources in MEC, [PERSON] et al. [19] suggested a convex optimization approach. The integrated optimizing for communication, processing resources, and offloading is the subject of some MEC research at the moment. [PERSON] et al. [20] proposed a game theory methods optimization scheme for edge-terminal collaborative computing offloading and resource allocation, which maximized system utility in a cloud-assisted MEC system. [PERSON] et al. [21] proposed a pipeline-based offloading strategy that uses both mobile smart devices' and edge servers' offloaded computing-intensive tasks to specific edge servers and central cloud servers according to their computing and communication capabilities, respectively. [PERSON] and colleagues [22] developed a cloud-edge-client collaborative edge intelligent layered dynamic pricing mechanism and presented an enhanced two-layer game approach to describe the collaboration. Their study introduced a unique forecasting method based on K-nearest neighbors that improves game convergence and lowers the amount of invalid games. [PERSON] et al. [23] proposed a routing protocol with a multiple-objective cost function to optimize the performance and minimize power consumption in distributed medical detection systems. [PERSON] et al. [24] investigated the resource allocation problem in a multiple-user MEC on orthogonal frequency division multiple access (OFDMA) and suggested a low-complexity suboptimal algorithm. To determine the appropriate resource allocation, they created an average offloading prioritization function. Many studies have developed multilayer optimization methods and iterative algorithms to solve the above problems [22; 25]. Extracting probable correlations between alterations in the environment and the operations of entities in Industrial Internet scenarios are difficult tasks for algorithms due to their complex and dynamic nature [26]. As a solution, [PERSON] et al. [27] introduced mobile offloading schemes on DRL that can enhance computational performance by choosing the best offloading strategy. To reduce the computing cost of the MEC in multiple inputs and multiple outputs, the capabilities of local processing and task offloading may be automatically allotted by learning rules from the local observations of each user on the MEC system. [PERSON] et al. [28] studied a decentralized dynamic computing offloading technique based on DRL. To be able to determine the best offloading and resource allocation for each task in a MEC system that operates under the influence of wireless fading channels and random edge computing, [PERSON] et al. [29] proposed a DRL algorithm that utilizes the actor-critic (AC) learning architecture. [PERSON] et al. [30] suggested implementing the DRL algorithm to devise a stochastic scheduling scheme for MEC, aiming to minimize the driving distance of autonomous vehicles. To choose the best cloud or edge server for offloading, [PERSON] et al. [31] created a combined task-offloading and load-balancing optimization technique based on DRL. [PERSON] et al. [32] explored the difficulties of dynamical caches, computing offloading, and allocation of resources in multiple users MEC.