Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This paper first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate.

Multimodal medical image fusion (MMIF) integrates complementary information from different imaging modalities to enhance image quality and remove redundant data, benefiting a variety of clinical applications such as tumor detection and organ delineation. However, existing MMIF methods often struggle to preserve sharp edges and maintain high contrast, both of which are critical for accurate diagnosis and treatment planning. To address these limitations, this paper proposes ECFusion, a novel MMIF framework that explicitly incorporates edge prior information and leverages a cross-scale transformer. First, an Edge-Augmented Module (EAM) employs the Sobel operator to extract edge features, thereby improving the representation and preservation of edge details. Second, a Cross-Scale Transformer Fusion Module (CSTF) with a Hierarchical Cross-Scale Embedding Layer (HCEL) captures multi-scale contextual information and enhances the global consistency of fused images. Additionally, a multi-path fusion strategy is introduced to disentangle deep and shallow features, mitigating feature loss during fusion. We conduct extensive experiments on the AANLIB dataset, evaluating CT-MRI, PET-MRI, and SPECT-MRI fusion tasks. Compared with state-of-the-art methods (U2Fusion, EMFusion, SwinFusion, and CDDFuse), ECFusion produces fused images with clearer edges and higher contrast. Quantitative results further highlight improvements in mutual information (MI), structural similarity (Qabf, SSIM), and visual perception (VIF, Qcb, Qcv).

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.

Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multi-agent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

Dynamic consolidation of virtual machines (VMs) is presented as a significant technique of energy conservation in cloud environments, which can eliminate the hotspot of overloaded hosts and switch the under loaded hosts to sleep mode through live migration of virtual machines. However, since the fact that migrating VM consumes a certain amount of extra resources, the process of reallocation can cause Service Level Agreement (SLA) violations. In this paper, a novel proactive framework which considers both predicted resource utilization and Performance-to-power Ratio (PPR) of heterogeneous hosts is proposed to perform dynamic VM consolidation to achieve balance of performance and energy. More precisely, a workload predictor is proposed based on the modified Weighted Moving Average (WMA) algorithm, representing the support for dynamic resource allocation; a cluster controller is proposed based on reinforcement learning for exploring the optimal matching relationship between resource requests and host at different PPR levels; a resource allocator is designed based on greedy strategy for achieving the trade-off between energy consumption and application performance across the cluster. Moreover, the framework is implemented based on distributed architecture and off-line learning pattern, which are able to not only scale up quickly but also improve the computing efficiency of the system. To validate the effectiveness of the proposed method, we have performed experimental evaluation on CloudSim with real-world workload traces of PlanetLab, and the simulation results demonstrate that it reduces the energy consumption up to 45.25% and effectively deals with high Quality of Service (QoS) requirements and heterogeneous distributed infrastructures in comparison with other competitive approaches.