This letter investigates blocked job offloading in a satellite-ground integrated network, which allows each low-earth orbit (LEO) satellite to execute more jobs on-board instead of sending the raw data to ground users. We propose an approximation offloading model to approximate the satellite-ground integrated network and analyze the average execution time, which is defined as when the user receives the raw data or processed result from the LEO satellite. Furthermore, we propose the maximum equivalent arrival rate and blocked job offloading algorithms. Numerical results validate the approximation model’s effectiveness and the proposed algorithms’ performance advantages and indicate a way to select the proper algorithm.

This letter proposes a distributed path selection algorithm based on the meta multi-agent proximal policy optimization (Meta-MAPPO). The algorithm leverages transferable knowledge to achieve faster and more stable policy optimization in dynamic satellite networks. We integrate meta-learning into the MAPPO framework, equipping agents with rapid adaptation capabilities and enhancing convergence efficiency through experience sharing. Simulation results on a 96-satellite Walker–Delta constellation demonstrate that the proposed framework achieves at least a 5% reduction in average end-to-end delay, maintains zero packet loss, and converges faster, demonstrating its efficiency and robustness in dynamic satellite network environments.

Epidemic forecasting is vital for public health, yet privacy concerns impede inter-institutional data sharing and limit model performance. Federated learning has emerged as a promising approach, but previous research has been limited to specific datasets and temporal prediction. In this paper, we present a privacy-preserving framework, federated framework for epidemic on networks (FFEN), for node-level epidemic prediction on networks that leverages federated learning (FL) to model the spatio-temporal propagation of epidemic severity across data-isolated subnetworks. A Spatio-Temporal Graph Attention Network (STGAT) is proposed to enhance federated epidemic prediction by effectively capturing spatio-temporal dependencies. Extensive simulations on various epidemic processes within a real-world airline network comprehensively evaluate FL’s efficacy under diverse scenarios. To further assess robustness, we introduce the efficacy energy metric, systematically analyzing key factors affecting FL performance. Numerical results validate the effectiveness of FFEN in complex epidemic prediction and demonstrate that STGAT outperforms traditional temporal approaches in capturing dynamic epidemic propagation.

Edge computing is near end users and provides them with fast computing services. Compared to the cloud, edge provides services with low communication delays but can only service limited users due to constrained storage and computing resources. Thus, allocating more computing resources to some jobs will block the execution of others, and the edge sends them either to the cloud or back to the users. This article focuses on minimizing the average system time by exploring blocked job scheduling (BJS) and redundant computing resource allocation (RCRA) in the cloud–edge–user system. Since edge nodes often operate in highly unpredictable environments, we adopt the replication redundancy to use the resources to shorten the job execution time. First, we propose an approximate model to evaluate the average system time theoretically. Second, we analyze the optimal scheduling for the blocked jobs and the optimal resource allocation for the redundant computing resources. Finally, we propose an algorithm combining BJS and RCRA. Simulation results show that the proposed model approximates the cloud–edge–user system well, and the combined algorithm significantly outperforms the other algorithms under different service time distributions.

This letter proposes a computing resource sharing strategy based on blocked job offloading in the low-Earth orbit (LEO) satellite network. The proposed strategy allows a satellite to share all or part of its computing resources with other satellites, and each satellite offloads or receives the blocked jobs from the adjacent satellites on the same meridian and latitude lines. Furthermore, we analyze the job execution probability, which evaluates the likelihood of the job being executed in the satellite network, for resource sharing strategies with different blocked job offloading hops. The numerical results validate the performance advantages of the computing resource sharing strategies and indicate a way to select the proper strategy.

Non-Terrestrial Networks (NTNs), as one core infrastructure of the sixth-generation (6G) communication technology, integrate heterogeneous nodes, such as High Earth Orbit (HEO) satellites, to achieve three-dimensional ubiquitous connectivity. However, NTNs face with spectrum scarcity, imposing stringent demands on spectral efficiency and interference management. To address these challenges, we propose a NOMA-oriented spectrum sensing technique for uplink scenarios, where High-Altitude Platforms (HAPs) serve as dynamic aerial nodes for opportunistic transmission within HEO coverage. Specifically, we derive multi-user sensing thresholds to optimize detection accuracy and suppress false alarms. Numerical simulations demonstrate the technique achieves a 33.5% throughput gain over benchmarks at 10 dB and maintains satisfactory performance across PUs’ varying elevation angles and transmission willingness.

Edge computing operates between the cloud and end users and strives to provide low-latency computing services for simultaneous users. Redundant use of multiple edge nodes can reduce latency, as edge systems often operate in uncertain environments. However, since edge systems have limited computing and storage resources, directing more resources to some computing jobs will either block the execution of others or pass their execution to the cloud, thus increasing latency. This paper uses the average system computing time and blocking probability to evaluate edge system performance and analyzes the optimal resource allocation accordingly. We also propose blocking probability and average system time optimization algorithms. Simulation results show that both algorithms significantly outperform the benchmark for different service time distributions and show how the optimal replication factor changes with varying parameters of the system.

We introduce a gossip-like protocol for covert message passing between Alice and Bob as they move in an area watched over by a warden Willie. The area hosts a multitude of Internet of (Battlefield) Things (Io$\beta \text{T}$) objects. Alice and Bob perform random walks on a random regular graph. The Io$\beta \text{T}$objects reside on the vertices of this graph, and some can serve as relays between Alice and Bob. The protocol starts with Alice splitting her message into small chunks, which she can covertly deposit to the relays she encounters. The protocol ends with Bob collecting the chunks. Alice may encode her data before the dissemination. Willie can either perform random walks as Alice and Bob do or conduct uniform surveillance of the area. In either case, he can only observe one relay at a time. We evaluate the system performance by the covertness probability and the message passing delay. In our protocol, Alice splits her message to increase the covertness probability and adds (coded) redundancy to reduce the transmission delay. The performance metrics depend on the graph, communications delay, and code parameters. We show that, in most scenarios, it is impossible to find the design parameters that simultaneously maximize the covertness probability and minimize the message delay.

Distributed computing enablesparallelexecution of smaller tasks that make up a large computing job. Its purpose is to reduce the job completion time. However, random fluctuations in task service times lead to straggling tasks with long execution times. Redundancy providesdiversitythat allows job completion when only a subset of redundant tasks is executed, thus removing the dependency on the straggling tasks. Under constrained resources (here, a fixed number of parallel servers), increasing redundancy reduces the available resources for parallelism. In this paper, we characterize thediversity vs. parallelismtrade-off and identify the optimal strategy among replication, coding, and splitting, which minimizes the expected job completion time. We consider three common service time distributions and establish three models that describe the scaling of these distributions with the task size. We find that different distributions with different scaling models operate optimally at different redundancy levels, thus requiring very different code rates.

Distributed systems operate under storage access and download service uncertainty. We consider two access models. In one, a user can access each storage node with a fixed probability, and in the other, a user can access any fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the time to transmit file data is negligible compared to the overall average download time. In the second (large file) model, the download time scales with the amount of downloaded data. The performance metric is the system’s service rate. For a fixed redundancy level, the systems’ service rate depends on the allocation of coded chunks over the storage nodes. Since finding the general optimal allocation is prohibitively hard, we consider quasi-uniform allocations, where coded content is equally spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that concentrating the coded content to a minimum-size subset is universally optimal for the small file model. However, for the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

Distributed computing enables parallel execution of tasks that make up a large computing job. Random fluctuations in service times (inherent to computing environments) often cause a non-negligible number of straggling tasks with long completion time. Redundancy, in the form of task replication and erasure coding, has emerged as a potentially powerful way to curtail the variability in service time, as it provides diversity that allows a job to be completed when only a subset of redundant tasks gets executed. Thus both redundancy and parallelism reduce the execution time, but compete for resources of the system. In situations of constrained resources (here fixed number of parallel servers), increasing redundancy reduces the available level of parallelism. We characterize the diversity vs. parallelism tradeoff for three common models of task size dependent execution times. We find that different models operate optimally at different levels of redundancy, and thus may require very different code rates.