Low Earth orbit (LEO) satellite networks have gained significant attention due to their flexibility in deployment, global coverage, and high data rates, making them a critical component of future space-air-ground integrated networks. However, their broad coverage introduces wireless security vulnerabilities, while stringent onboard power budgets and the energy supply demands of satellite terminals challenge their energy efficiency and sustainability. To address these cross-domain issues, this paper first derives a secure energy efficiency entropy formula from an information-theoretic perspective, which focuses on security, power utilization, and energy harvesting. Guided by this formula, we integrate movable antennas (MAs), hybrid precoding, and simultaneous wireless information and power transfer (SWIPT) to dynamically shape beams, control transmit power and wireless power transfer to terminals. To mitigate empirical model mismatches to practical scenarios (channel characteristics, user attributes, and hardware constraints), we propose a generative AI agent framework that automatically constructs system configurations based on natural language descriptions. The agent employs a semantic router and retrieval-augmented generation (RAG) to recommend modeling parameters for optimization objectives, channel characteristics, user attributes, and radio-frequency chain topologies. Based on the customized model, we formulate a joint optimization problem for hybrid beamforming and MA positioning to maximize energy efficiency under secrecy rate and energy harvesting constraints. As the problem is NP-hard, a multi-agent deep reinforcement learning (DRL) approach is developed to optimize the transmit power, analog/digital precoders and MA positions. Simulations show that the proposed multi-agent AI framework can translate requirements into system configurations, effectively avoiding configuration pitfalls, and the DRL-based solution significantly outperforms conventional fixed-antenna and random-positioning baselines in energy efficiency. This work paves a new path from entropy-based theoretical modeling to AI-based optimization in building efficient, secure, and energy-self-sufficient LEO satellite IoT infrastructures.
Information diffusion prediction comprises macroscopic and microscopic prediction tasks. The former aims to estimate the overall impact of the information propagation process, while the latter focuses on predicting the next participating user, both of which are crucial to understanding how information spreads in a network. Hence, as a recent trend, it is important to investigate multi-scale prediction models. Nevertheless, conventional methods primarily focus on extracting shared features but fail to consider the complex interactions between users and cascades, as well as the feature similarity of the same user across tasks. In this connection, we propose a multi-dimensional feature interaction (MDFI) prediction model with two novel designs. Specifically, to model the latent hierarchical interactions between users as well as users and cascades, we construct a heterogeneous weighted user-cascade graph at the macro-level. At the micro-level, we account for the social homophily of users in the social network and develop the cascade neighbor contrastive learning as a constraint under the multi-task learning framework. Experimental results on four publicly available datasets demonstrate that MDFI exhibits superior performance to all competing models.
Spiking neural networks (SNNs) are gaining attention for energy-efficient, event-driven computing due to their ability to encode information through discrete spikes over multiple time steps. Their use of a biologically plausible neuron model makes them well-suited for asynchronous and sparse data processing in edge workloads. However, this temporal and stateful behavior introduces burst-driven execution patterns and inter-step dependencies, posing significant challenges for GPU-based deployment. Existing GPU schedulers, optimized for dense and feedforward DNNs, fail to fully exploit the irregularity and sparsity of SNNs, leading to poor resource utilization and unpredictable latency under dynamic workloads. To address these challenges, we propose MPL-schedule, a multi-preemptive, priority-aware scheduling scheme that dynamically coordinates heterogeneous SNN tasks on GPUs. Our approach formulates the scheduling process as a reward maximization problem under resource constraints, integrating adaptive priority computation, overhead-aware time-slicing, and state-preserving preemption. We evaluate MPL-schedule across diverse scenarios, including task complexities, spike arrival patterns, and time-slice configurations. Compared to state-of-the-art baselines, our method improves throughput by up to 15.0
Wireless indoor localization and tracking systems often implicitly assume that all anchors are continuously available. However, this assumption is often impractical under bandwidth and energy constraints. With limited wireless resources, tracking systems must balance localization accuracy with sensing and communication overhead. Therefore, anchor scheduling is crucial as it enables the system to activate only the most informative anchors for a better accuracy-resource tradeoff. This paper proposes a self-supervised framework for joint tracking and anchor scheduling by first utilizing a physics-guided deep state space model to infer target positions and uncertainty without ground truth trajectories. Building on the learned latent representation, we further design an actor-critic-based reinforcement learning scheduler that proactively selects a resource-budgeted set of informative anchors for future ranging. Experiments on real-world ultra-wideband datasets demonstrate that the proposed method achieves a mean absolute localization error of 0.458 m, outperforming classical filters and learning-based baselines. By activating only 3 of the 8 anchors, the proposed method reduces the number of ranging requests by 62.5
The deployment of large-scale satellite networks demands high-capacity and stable inter-satellite communication links, thereby driving the adoption of coherent optical satellite communication (COSC). However, relative satellite motion introduces Doppler shifts, which severely degrade link performance. To address this challenge, we discover the periodic correlation between Doppler shifts and the Gardner timing error detector (TED). Based on this correlation, we propose a novel frequency-offset estimation (FOE) algorithm that estimates frequency offset by computing the Gardner TED gain. The proposed algorithm surpasses the conventional FOE’s half-baud-rate limitation and maintains high estimation accuracy under strong noise conditions. We conduct 25-Gbaud DP-QPSK transmission experiments to evaluate the performance of the proposed FOE and to further examine its role in Doppler-shift compensation. The experimental results demonstrate that the proposed algorithm achieves a Doppler-shift estimation range 1.9 times that of conventional FOE algorithms. In addition, providing noise-robust Doppler-shift estimation improves receiver sensitivity by 0.6 dB and ensures successful signal demodulation even at an OSNR of 10 dB, which is where conventional FOE algorithms fail.
This paper proposes a hierarchical attitude planning method to address the complex attitude planning problem for multi-rigid-body spacecraft equipped with multiple laser communication terminals under constraints. The proposed method comprises three key steps: attitude decomposition, constrained path search, and trajectory generation. First, attitude decomposition determines the desired spacecraft platform attitude and feasible terminal joint angles (satisfying range-of-motion limits) corresponding to the desired terminal pointing directions. Next, the multi-heuristic A* algorithm is employed to perform a search for a feasible attitude and joint angle sequence within the high-dimensional configuration space satisfying all pointing constraints. Finally, a novel Lie group SO(3)-based trajectory planning method is applied to generate smooth and dynamically feasible attitude trajectories connecting the discrete sequence, by solving a sequence of quadratic programs. Numerical simulations under different operational constraints demonstrate the effectiveness of the proposed method in generating feasible and smooth trajectories.
Group communication among first responders is critical to the success of public safety missions. The operational environment of such missions introduces unique requirements, including highly dynamic group membership, tolerance to communication disruptions, the need to maintain confidentiality, and enable auditability. However, commercial group communication applications are typically designed for networks that provide timely and reliable delivery, which do not necessarily satisfy these requirements. Thus, a group communication solution specifically designed for public safety missions—one that accounts for all of these requirements—is needed. We present NameShield, a secure and efficient framework for dynamic group communication in public safety and emergency response scenarios. NameShield adopts a holistic, layered architecture that integrates security, information dissemination, and networking to support reliable and confidential communication among highly dynamic groups of first responders. At the information layer, NameShield builds on a role-based graph namespace to realize an efficient publish/subscribe communication model that naturally captures organizational structure and group dynamics. The network layer of NameShield is intentionally decoupled from specific communication technologies and can operate over any substrate that provides multicast, making it suitable for network conditions during public safety missions. To maintain message confidentiality, NameShield incorporates a message-oriented key-policy attribute-based encryption (KP-ABE) mechanism that enables fine-grained, per-message access control and supports frequent membership changes without requiring expensive group rekeying. Together, these design choices allow NameShield to provide strong security guarantees while remaining flexible, scalable, and robust under the operational constraints of emergency response environments.
While deep neural networks (DNNs) are increasingly deployed on graphics processing units (GPUs) at the network edge, the performance interference caused by co-locating and executing multiple models on a single GPU is often overlooked, leading to high inference latency and excessive energy consumption. Pursuing the best performance faces several challenges, including characterizing the non-linear interference-incurred latency, dispatching the unpredictable inference workloads, and balancing the different trade-offs related to performance and accuracy. In this study, we first construct an interference-incurred latency model via real-world profiling and measurements. With insights from this model, we formulate a time-varying integer program to minimize the long-term total cost of the edge AI inference system, including the inference latency, the inference error rate, the query-dispatching communication cost, and the energy consumption, subject to resource and workload constraints. We then propose a set of polynomial-time online algorithms that continuously make fractional control decisions for each time slot based on the feedback from the previously applied decisions and strategically round these decisions into integers. We conduct a formal theoretical analysis and prove that both the time-averaged gap between the total cost incurred by our online decisions and the total cost of the series of one-shot offline optimums and the time-averaged constraint violation gradually diminish over time. Extensive experiments on real-world testbeds and datasets demonstrate that the proposed algorithms can reduce the total cost by 40
Industrial anomaly detection (IAD) has developed strong methods for one-class, zero-shot, and few-shot deployments, each effective in its own setting, yet no single approach adapts quickly across them. Large multimodal models (LMMs) offer a different route, since broad knowledge and strong visual-language understanding allow an LMM to interpret the outputs of diverse IAD methods and to calibrate their predictions using image evidence. To realize such an adaptive system, we present a novel large multimodal model applying vision experts for industrial anomaly detection (abbreviated as Myriad). Myriad treats conventional IAD models as VEs and converts their anomaly maps into lightweight prompts that steer a frozen Q-Former toward suspicious regions, while a compact low-rank adapter shapes features for IAD. The language pathway then fuses VE positional cues with visual evidence to produce calibrated, machine-usable decisions, effectively regularizing noisy or ambiguous anomaly maps. By simply switching the VE (e.g., one-class or zero-/few-shot experts) without modifying the architecture, Myriad adapts uniformly across deployment scenarios and remains robust to the choice of expert. Extensive experiments on MVTec-AD, VisA, and PCB Bank benchmarks demonstrate that our proposed method not only performs favorably against state-of-the-art methods under one-class and few-shot settings, but also inherits the flexibility and instruction-following ability of LMMs in the field of IAD. Source code and pre-trained models are publicly available at https://github.com/tzjtatata/Myriad .
This paper intends to address the outlier-resistant fusion filtering problem for a class of nonlinear two-dimensional systems with the energy harvesting sensors under the measurement censoring scheme. The energy harvesting technology is considered to provide the energy required for measurement signal transmission, where the energy harvested at each shifting point is characterized by a random variable, and the signal can be successfully transmitted only when the sensor accumulates sufficient energy. The censored measurements are modeled by the well-known Tobit model and further characterized by a series of Bernoulli random variables with local probability approximation. Additionally, a saturation function is introduced to mitigate the impact of abnormal innovations caused by measurement outliers. The design of the fused filter in this paper follows two main steps: firstly, the local filters are established in the presence of incomplete measurements that are caused by energy constraints and censoring, where upper bounds for the filtering error variances are derived and subsequently minimized at each shifting point by selecting appropriate parameters; and secondly, the obtained local estimates are fused by deploying a suitable fusion scheme, where the fused estimate is shown to be more accurate than the local ones. Finally, a numerical example is provided to verify the effectiveness of the proposed outlier-resistant fusion filtering algorithm.
