Dynamic edge networks revolutionize mobile edge computing by enabling real-time applications in intelligent transportation, augmented reality, and industrial Internet of Things (IoT). Efficient workload offloading in dynamic edge networks is crucial for addressing the increasing demands of time-varying workloads while contending with limited computational and communication resources. Existing deep reinforcement learning (DRL)-based offloading decision-making schemes are inadequate for managing scenarios involving multiple workloads and edge servers, particularly when faced with time-varying workload arrivals and fluctuating channel states. To this end, we propose a flexible module weighted fusion DRL framework (DRL-MWF) for scalable and robust multi-workload offloading in edge environments. Unlike traditional monolithic networks, DRL-MWF employs a weighted fusion modular architecture that adapts flexibly to diverse workload distributions. Specifically, DRL-MWF introduces a state representation and normalization strategy to model state and workload characteristics, enabling precise and adaptive decision-making. Furthermore, we design two key mechanisms: a weighted policy correction method to stabilize learning and a prioritized experience replay with weighted importance sampling to accelerate convergence by emphasizing critical transitions. Extensive evaluations on real-world datasets demonstrate that DRL-MWF consistently outperforms state-of-the-art baselines. These results reveal DRL-MWF's potential to transform workload offloading in next-generation edge computing systems, ensuring high performance in dynamic scenarios.

Abstract Mobile user terminals in 5G networks can generate massive computational workloads, which require sufficient computation and caching resources, and the processors of user terminals cannot tackle these workloads. Emerging mobile edge computing (MEC) has become the key to solving the computation problem by offloading computation‐intensive workloads to the MEC server. To make full use of the limited resources on the MEC side, service caching can pre‐store specific executable programs, databases, or libraries for executing offloaded workloads from user terminals. In this study, a service caching‐assisted MEC model is designed. According to the proposed MEC model, a decentralized model‐free deep reinforcement learning algorithm‐based server caching optimization policy (DDSCOP) is proposed to minimize the long‐term weighted average cost. Considering the time‐varying request workloads from user terminals, the stochastic channel state, and the arrival of renewable resources of the MEC server, DDSCOP can find the near‐optimal service caching decision‐making policy by training the neural networks, that is, whether the service caching can be hosted by the MEC server, and which MEC server hosts the service caching. The numerical experimental results verify the convergence and effectiveness of DDSCOP through extensive parameters configuration, and DDSCOP outperforms the three baseline algorithms.

In heterogeneous wireless networks, massive mobile terminals randomly generate a large number of computation tasks (payloads). How to better manage these mobile terminals located in wireless networks to achieve acceptable quality of service (QoS) such as latency minimization, energy consumption minimization is crucial. A multi-access edge computing (MEC) server can be leveraged to execute the offloaded payloads generated from mobile terminals owing to its powerful processing power and location proximity features. However, an MEC server cannot tackle all offloaded tasks from multiple mobile terminals, and its energy consumption needs further consideration. We introduce an edge server model combined with the unmanned aerial vehicle (UAV) and equipped with the macro base station (MBS-MEC) to process the arrival payloads, and all UAVs and MBS-MECs can harvest renewable energy by using energy harvesting equipment. Furthermore, we model the computation offloading as a deep reinforcement learning scheme withoutprioriknowledge. Considering the infeasibility of deep-reinforcement learning-based centralized learning for the proposed edge computing framework, we propose a distributed computation offloading scheme based on deep reinforcement learning (DCODRL) to minimize the weighted average cost, including the latency cost and the energy cost. Each mobile terminal can be regarded as a learning agent for the DCODRL. To compensate for the lack of cooperation of the DCODRL, we propose a gated-recurrent-unit-assisted multi-agent computation offloading scheme based on deep reinforcement learning (MCODRL) to improve the offloading policy by obtaining global observation information and designing a common reward for all agents. Comprehensive numerical results reflect the convergence and effectiveness of the DCODRL and MCODRL, and the efficacy of the proposed algorithms is further verified through comparisons with two baseline algorithms.

Saliency region detection methods have become one of the hotspots in the field of image processing as an important method to improve the real-time and accurate analysis of massive data. Integrating more effective prior knowledge is a viable direction for improving the performance of saliency region detection methods. Most of the methods based on background prior and boundary connectivity prior assume the boundary area of the image as the background, by restraining the background to highlight the salient area. When the boundary area of the image does not describe the background well (such as a large difference in border area features), if the entire frame of the image is put together to compute the background feature, the calculation of the background feature will be inaccurate. In view of the above shortcomings, this paper proposed a saliency region detection method based on background and spatial position. This method carried on the image boundary super pixel clustering, determined the background feature according to the clustering center, and used the difference between the super pixel on the image and the background super pixel, and its spatial position to calculate the salient of the super pixels. This approach used MATLAB to program and experiment. The method was compared with a series of the state-of-the-art methods. The AUC of proposed algorithm reaches 0.839, and the MAE is 0.220, showing the effectiveness of the proposed algorithm.