In the context of Low Earth Orbit Satellite Edge Computing (LEO-SEC), the rapid expansion of satellite constellations and increasing concurrent demands in hotspot areas high-light the limitations of individual satellites. A single satellite often lacks the resources needed to handle task processing with both low latency and high reliability. As a result, the importance of solving challenges related to multi-satellite collaborative computing and task offloading has grown significantly. To address these challenges, this paper proposes the Decentralized Multi-Agent MAB Task Offloading (DMAMTO) algorithm, which integrates the incremental learning mechanism of the Multi-Armed Bandit (MAB) into multi-satellite collaborative computing. Based on the framework of a multi-agent repeated stochastic game, it achieves joint optimization of distributed collaborative satellite selection and discrete task allocation. Simulation results demonstrate that the DMAMTO algorithm enables autonomous collaborative computing in LEO-SEC scenarios, effectively reduces task offloading costs, and consistently outperforms benchmark algorithms in simulation scenarios involving the Starlink, Kuiper, and OneWeb constellations.

Flood mapping using satellite remote sensing images plays an important role in disaster monitoring and emergency response. However, traditional change detection methods encounter dual challenges in complex environments: ambiguous flood delineation and inadequate fusion of multi-source remote sensing data. To address these limitations, we propose the improved cross-modal change detection CNN (CCDCNet), specifically designed for cross-modal flood change detection tasks in synthetic aperture radar (SAR) and multispectral images. This network adopts a dual-stream encoder-decoder structure. We designed RS_DBlock module to expand the receptive field, enabling the network to capture more flood region information at once. The RDC module is designed to achieve multi-scale feature extraction, enhancing the model's ability to understand complex scenes. Additionally, the CBAM residual structure is introduced in the decoding part, which implements channel-spatial attention mechanisms for adaptive feature selection. Experimental results demonstrate that the proposed method achieves performance enhancement compared to baseline methods, with notable improvements in key metrics such as mIoU, Precision and F1 score, providing an effective solution for cross-modal high-precision flood mapping. The source codes are available at https://github.com/llya6/CCDCNet.git

Forest environment monitoring is crucial for detecting and predicting natural disasters, such as forest fires. Uncrewed aerial vehicle (UAV) is frequently used to acquire environmental data in forest ecosystems for monitoring purposes. However, the complex geographical terrain of forests, limited UAV battery capacity, and power constraints of ground IoT devices pose significant challenges to data collection. In this article, we design a 3-D trajectory optimization framework for a UAV to collect forest environmental data, considering the altitude limitations of the UAV in complex forest environments and the need to recharge IoT devices using wireless power transfer (WPT) technology, aiming to extend the operational lifetime of the network. Specifically, we aim to achieve a desirable balance between the amount of data collected by the UAV and its energy consumption incurred by data collection and sensor recharging. To this end, we first formulate the trajectory design of the UAV for data collection in forest areas as a nonlinear optimization problem, aiming to maximize the amount of sensor data collected while minimizing the energy consumption of the UAV. To solve this problem, we propose a converging-trajectory design and data collection (C-TDDC) method, which includes two subalgorithms. The first is an ant colony optimization-based traveling salesman problem (ACO-TSP) algorithm to generate optimal UAV trajectories. The second is a proximal policy optimization-based reinforcement learning algorithm, which balances data collection and energy consumption for the UAV. The simulation results demonstrate that the proposed C-TDDC algorithm exhibits more stable convergence and performs better in completing data collection tasks, outperforming both the state-of-the-art algorithm and baseline schemes.

In satellite Internet of Things (IoT), the remote sensing satellites capture images and then transmit them to a ground station through low Earth orbit (LEO) communication satellites for model inference. However, this process results in significant transmission latency and communication overhead. In response, researchers have proposed various satellite on-orbit model inference methods. Nonetheless, the limited computation capacity and memory space of a single remote sensing satellite impose processing delays when dealing with large quantities of high-resolution images, thereby making it difficult to ensure real-time service. To tackle this issue, we propose an on-orbit deep neural network (DNN) distributed inference framework for remote sensing images in satellite IoT, leveraging the availability of numerous LEO computing satellites. Designing such a framework involves two crucial questions: first, determining which LEO satellites should participate in distributed DNN inference, and second, how to partition the images among the selected LEO satellites. To address these questions, we formulate the distributed inference process as a mixed integer nonlinear optimization problem, which is known to be NP-hard. The objective is to minimize overall energy consumption while ensuring that the distributed inference is accomplished when the satellite dynamic network remains unchanged. We initially propose a dynamic optimization algorithm that derives the optimal solution with rigorous theoretical guarantees. Subsequently, to reduce computational complexity, we introduce an approximate solution based on an improved simulated annealing algorithm. We demonstrate that the approximate algorithm performs within a limited range of the optimal algorithm. Finally, we build a heterogeneous testbed based on Kubernetes and conduct extensive experiments to validate that our proposed algorithms reduce energy consumption by an average of 24.63% and 25.98% on the Faster-RCNN inference model, 47.09% and 47.51% on the RetinaNet inference model, and 53.36% and 48.08% on the Yolov5 inference model on the two datasets compared to the baselines.

With the advancement of satellite computing capabilities, the diversity of satellite communication services imposes varied quality of service (QoS) requirements. Limited satellite resources necessitate remote deployment and updates of running services for QoS testing, increasing testing difficulty. Existing testing tools are limited in functionality or reliant on specific infrastructures, failing to meet the QoS testing needs of edge cloud services in mobile satellite scenarios. In this paper, we present SECTest, an integrated testing platform for QoS in satellite edge clouds. More precisely, SECTest can integrate changes in satellite network topology, create and manage satellite edge cloud cluster testing environments on heterogeneous edge devices, customize experiments for users, support deployment and scaling of various integrated testing tools, provide test data persistence function to manage data life cycle and store data hierarchically, and publish and visualize test results. We have built a real satellite edge cloud cluster based on Kubernetes, integrating both physical and virtual machines, and deploying a variety of integrated testing tools using containerization technology. Currently, we have evaluated the quality of service in terms of processing latency, packet drop rate, throughput, and average response time for object detection microservice applications, web microservice applications, and data transfer tasks. To demonstrate SECTest's scalability in testing network communication protocols, we evaluated the performance of HTTP and gRPC in microservice communication within the cluster. Our experimental results validate SECTest's ability to test key service quality metrics in a real satellite edge cloud cluster.

The diversification of satellite communication services imposes varied requirements on network service quality, making quality of service (QoS) testing for microservices running on satellites more complex. Existing testing tools have limitations, potentially offering only single-functionality testing, thus failing to meet the requirements of QoS testing for edge cloud services in mobile satellite scenarios. In this paper, we propose a framework for integrating quality of service testing in satellite edge clouds. More precisely, the framework can integrate changes in satellite network topology, create and manage satellite edge cloud cluster testing environments on heterogeneous edge devices, customize experiments for users, support deployment and scaling of various integrated testing tools, and publish and visualize test results. Our experimental results validate the framework’s ability to test key service quality metrics in a satellite edge cloud cluster.