UAV-enabled edge storage systems provide data storage services to users by deploying UAVs in areas lacking infrastructure coverage, overcoming delay limitations and improving Quality of Service (QoS). Most existing studies focus on storing replicas on UAVs to ensure low-latency data access. Nonetheless, replica-based strategies incur high storage cost, posing significant challenges for UAVs with limited storage resources. In this paper, we introduce erasure coding into the UAV-enabled edge storage system, aiming to reduce user data access latency while minimizing storage cost. However, the mobility of users and the non-fully-connected nature of the UAV network pose new challenges for the coupled decisions of data encoding, block placement, and access. In this paper, we propose a Mobility-Enhanced Hierarchical Deep Reinforcement Learning algorithm (ME-HDRL). Specifically, we design a trajectory prediction algorithm combining CNN and ConvLSTM to account for user mobility in decision-making. We further decompose the original problem into two subproblems: data encoding and placement, as well as block access. A hierarchical deep reinforcement learning algorithm involving multiple UAV agents and an edge agent is proposed to collaboratively learn optimal decisions. To improve the convergence of the algorithm, we design an invalid action filter to reduce the action space. Experimental results show that our approach outperforms existing rule-based and reinforcement learning-based algorithms in various scenarios, exhibiting significant convergence improvements and a substantial reduction in both storage cost and user data access latency.

With increasing computational demands and limited network resources in marine environments, efficient service caching and task offloading have become critical. In such environments, Autonomous Underwater Vehicles (AUVs) rely on Unmanned Surface Vehicles (USVs) as relays, forming a cross-domain network comprising underwater acoustic and above-water RF links. However, the heterogeneity in bandwidth, latency, and bit error rates introduces challenges for reachability analysis and delay estimation. This paper addresses the joint optimization of caching, task offloading, and resource allocation in a cross-domain marine network composed of offshore base stations, USVs, and AUVs. To tackle the inherent heterogeneity in network links and decision timescales, we formulate the problem as a two-time-scale Hierarchical Markov Decision Process (H-MDP) and propose a Two Time-Scale Deep Reinforcement Learning (T2S-DRL) approach that integrates a hybrid policy network and a lightweight structure-aware action masking mechanism. The large time-scale agent optimizes caching decisions, while the short time-scale agent focuses on offloading and resource allocation. Extensive simulations show that our approach significantly reduces task execution delay and energy consumption, validating its effectiveness.

Satellite-assisted Mobile Edge Computing (MEC) is a promising paradigm for enabling low-latency and high-efficiency computing in deep-sea and far-offshore marine environments. However, the inherent heterogeneity of three-layer marine networks—comprising satellites, Unmanned Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs)—introduces unique challenges. These include the coupling of underwater acoustic and above-water Radio Frequency (RF) communication links, the highly constrained computing and caching resources of edge devices, and the strong interdependence between service caching, vertical task offloading, and horizontal migration. Existing solutions often overlook these cross-layer dynamics and the spatio-temporal interactions among network nodes, leading to suboptimal task scheduling and degraded system utility. To address these challenges, we formulate a joint optimization problem that maximizes the Quality of Experience (QoE), with decision variables spanning caching placement, task migration, and offloading under resource constraints. Through rigorous theoretical analysis, we prove that the formulated problem is NP-hard, highlighting its inherent computational intractability. To overcome this, we propose an Attention-Enhanced Multi-Agent Reinforcement Learning algorithm (AE-MARL), which adopts a hybrid policy network to learn discrete decisions and continuous resource allocation. Furthermore, a lightweight attention module is integrated to infer the importance of partial observations and guide collaborative decision-making across agents. Extensive experiments and analysis under diverse system configurations demonstrate that AE-MARL consistently outperforms state-of-the-art baselines.

Emerging memory-semantic fabrics such as CXL and UB enable direct load/store access to remote memory at byte granularity, opening new opportunities for cluster-wide memory pooling and sharing. However, building a high-performance, fault-tolerant memory pool atop such fabrics remains challenging. Systems must coordinate application transparency with heterogeneous memory topologies, ensure safe memory reuse across machines, and handle instruction-level memory failures that manifest as hardware exceptions in user code. We present Oak, a resilient, high-performance memory pool service that enables transparent and efficient memory pooling and sharing across machines via memory-semantic interconnects. Oak decouples memory metadata from control logic via a stateless global memory manager, which is backed by a distributed KV store enabling scalable, fault-tolerant orchestration. To tolerate memory faults ranging from device loss to single-page uncorrectable errors, Oak provides a lightweight kernel-user cooperative recovery mechanism that intercepts memory failures in the kernel, performs microsecond-scale recovery, and defers metadata updates to user-space asynchronously. We demonstrate Oak's practicality by building Oak-KV, a fault-tolerant, zero-copy key-value store that runs entirely on Oak-managed shared memory. Evaluations show that Oak-KV delivers higher throughput than representative baselines under both normal and failure conditions.

Autonomous aerial vehicle (AAV) crowdsensing, as a complement to mobile crowdsensing, can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this article, we investigate the issue of sensing data storage in AAV crowdsensing without edge assistance, where sensing data is stored locally in the AAVs. In this scenario, replication scheme is usually adopted to ensure data availability, and our objective is to find an optimal replica distribution scheme to maximize data availability while minimizing system energy consumption. Given the NP-hard nature of the optimization problem, traditional methods cannot achieve optimal solutions within limited timeframes. Therefore, we propose a centralized training and decentralized execution deep reinforcement learning (DRL) algorithm based on actor-critic, named “MUCRS-DRL.” Specifically, this method derives the optimal replica placement scheme based on AAV state information and data file information. Simulation results show that compared to the baseline methods, the proposed algorithm reduces data loss rate, time consumption, and energy consumption by up to 88%, 11%, and 11%, respectively.

With the integration of IoT and 5G technologies, UAV crowd sensing has emerged as a promising solution to overcome the limitations of traditional Mobile Crowd Sensing (MCS) in terms of sensing coverage. As a result, UAV crowd sensing has been widely adopted across various domains. However, existing UAV crowd sensing methods often overlook the semantic information within sensing data, leading to low transmission efficiency. To address the challenges of semantic extraction and transmission optimization in UAV crowd sensing, this paper decomposes the problem into two sub-problems: semantic feature extraction and task-oriented sensing data transmission optimization. To tackle the semantic feature extraction problem, we propose a semantic communication module based on Multi-Scale Dilated Fusion Attention (MDFA), which aims to balance data compression, classification accuracy, and feature reconstruction under noisy channel conditions. For transmission optimization, we develop a reinforcement learning-based joint optimization strategy that effectively manages UAV mobility, bandwidth allocation, and semantic compression, thereby enhancing transmission efficiency and task performance. Extensive experiments conducted on real-world datasets and simulated environments demonstrate the effectiveness of the proposed method, showing significant improvements in communication efficiency and sensing performance under various conditions.

Visual Crowdsensing (VCS) has gradually become an emerging research field as the built-in cameras of smart mobile devices have become a common recording tool in daily life. To meet the task requirements of VCS applications in the sensing process, sensing platforms usually use distributed data acquisition to collect image data from different sources. However, this leads to a large amount of redundant data in the final collected data set, which seriously affects the data quality. To solve the above problems, this paper proposes a dynamic redundancy detection method (VCSRD) for visual crowdsensing data based on contrastive learning. The method fuses the multimodal information of metadata and visual content through comparative clustering to realize the redundancy detection of data. Not only improves the accuracy of redundant data detection but also has some flexibility for data under different tasks. The effectiveness and flexibility of the proposed method under different data sizes are verified through experiments on real image datasets. Compared to the baseline method, VCSRD shows superior performance on all three clustering metrics.