This work studies the Age-of-Information (AoI) optimization problem in the information-gathering wireless network systems, where time-sensitive data updates are collected from multiple information sources, and each source is equipped with a battery and harvests energy from ambient energy, such as solar, wind, etc. The arrival of the harvested energy can be modeled as the stochastic process, and an information source can deliver its data update only when 1) there is energy in the battery, and 2) this source is selected to transmit its data update based on the transmission policy. This work analyzes how the energy arrival pattern of each source and the transmission policy jointly influence the average AoI among multiple sources. To the best of our knowledge, this is the first work that formally develops the closed-form expression of average AoI in the Stationary Randomized Sampling (SRS) policy space and proposes approximation schemes with constant ratios in multi-source systems under a stochastic energy harvesting model. More specifically, under the perfect wireless channel, the closed-form expression of AoI under the SRS policy space with arbitrary finite battery size is developed. Based on the result, we propose the Max Energy-Aware Weight (MEAW) policy, which is proven to achieve 2-approximation in the full policy space. Under the uncertain wireless channel, we develop the closed-form expression of Whittle’s index to address the target problem. Based on the result, we propose the Energy-aware Whittle’s index policy (EWIP) and prove its approximate performance by using the Lyapunov optimization techniques. Experimental results show that MEAW under the perfect channel setting and EWIP under the uncertain channel setting both perform close to the theoretical lower bound and outperform the state-of-the-art schemes.

Introducing mobile edge devices in IoT systems for real-time data retrieval can reduce energy consumption and human interaction, and thus has attracted significant research attentions in recent years. In the meanwhile, leveraging mobile edge device(s) to charge the sensor devices in proximity through RF-based Wireless Energy Transfer (WET) technologies can provide controllable and stable energy supply, and is being increasingly deployed in the field. Based on these two recent trends, this work aims to formulate and solve the joint device charging and fresh data retrieval problem where a speed-adjustable mobile edge device judiciously powers a set of sensor devices in emergent energy shortage through WET while retrieving designated data items through multi-hop transmissions. To this end, an efficient 3-phase method for Joint Charging and Retrieving (3JCR) is proposed. 3JCR first identifies the sensor devices to be charged and develops the moving trajectory of the mobile edge device. It then applies a max-flow min-cost based clustering scheme to determine the routing path of the required data items to be transmitted to the mobile edge device, and finally constructs the data retrieval schedule and adjusts the speed of the mobile edge device in the run time. Our extensive experimental results show that 3JCR outperforms all state-of-the-art methods in terms of the number of feasibly retrieved data items, while achieving similar node survival rate.

As the strategic importance of space continues to grow, space target recognition technology is critical for advancing space surveillance systems, optimizing the use of space resources, and ensuring space security. In response to challenges in analyzing radar cross-section (RCS) sequences, we propose a deep learning-based method designed to enhance the robustness and accuracy of RCS sequence analysis for space target recognition. We introduce a novel period estimation method based on combination functions and analysis of variance (ANOVA), which effectively suppresses noise and captures periodic characteristics with greater accuracy. Building on this, we propose a Transformer-based approach for size estimation from RCS sequences, leveraging Transformers’ advanced sequence modeling to reduce common errors in traditional methods, further improving space target characterization. We then integrate period and size features into a unified feature set and introduce a cross-attention-based multi-feature interaction module to fuse physical and statistical features, learning dependencies between them to enhance target recognition accuracy. Experimental results demonstrate that our approach significantly improves both performance and stability of space target recognition, providing a solid foundation for further advancements in space surveillance technology.

Self-supervised depth estimation from monocular image sequences provides depth information without costly sensors like LiDAR, offering significant value for autonomous driving. Although self-supervised algorithms can reduce the dependence on labeled data, the performance is still affected by scene occlusions, lighting differences, and sparse textures. Existing methods do not consider the enhancement and interaction fusion of features. In this paper, we propose a novel parallel multi-scale semantic-depth interactive fusion network. First, we adopt a multi-stage feature attention network for feature extraction, and a parallel semantic-depth interactive fusion module is introduced to refine edges. Furthermore, we also employ a metric loss based on semantic edges to take full advantage of semantic geometric information. Our network is trained and evaluated on KITTI datasets. The experimental results show that the methods achieve satisfactory performance compared to other existing methods.

UAV clusters operating in harsh environments must maintain connectivity under sudden node failures and dynamic traffic demands. Existing studies often address maintenance and routing separately, which leads to unnecessary reorganization overhead and forwarding bottlenecks around gateway nodes. To overcome these limitations, we propose a unified UAV Cluster Maintenance and Routing System (UCMR) that combines a firefly-inspired clustering method, a DQN–GCN-based medium-level interference recovery strategy (DGMR), and load-aware routing over forwarding neighbors. The maintenance component improves backbone repair by integrating global topology features with local observations, while the routing component exploits edge and node features to distribute forwarding pressure beyond gateway nodes. Extensive experiments demonstrate that UCMR outperforms representative benchmark methods in terms of average end-to-end delay and packet delivery ratio.