Sep 2026· IEEE transactions on computers· Vol 75, pp. 3454-3467· 0 citations· 50 references
Abstract
Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The dynamic network topology and time-varying resource availability in satellite constellations further degrade the quality and stability of SEC services. To address these challenges, we propose a deep learning-based Collaborative Image Feature-extraction Task Optimization (CIFTO) framework. CIFTO dynamically distributes image processing workloads across multiple Low Earth Orbit (LEO) satellites, enabling continuous temporal updates for task allocation while significantly accelerating convergence and reducing computational overhead. By integrating temporal modeling and iterative optimization, CIFTO effectively mitigates the NP-hard nature of satellite task allocation. Furthermore, a lightweight satellite image processing model is designed to meet the strict constraints of on-orbit computation, achieving efficient image inference with minimal parameters. Extensive experimental evaluations demonstrate that the proposed framework ensures timely task completion, substantially lowers system-wide energy consumption, and enhances the adaptability and training efficiency of SEC services.
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations
The Open Radio Access Network (O-RAN) paradigm, with its open interfaces and intelligent functions, is a key enabler for next-generation wireless systems. We investigate the deployment of O-RAN-based network slice functions over Low Earth Orbit (LEO) satellite networks with Mobile Edge Computing (MEC) capabilities. To provide energy-efficient and low-latency services through distributed data processing, we formulate a slice function data offloading problem aimed at jointly optimizing end-to-end (E2E) latency and energy consumption. We model the problem as an MDP and propose a Deep Reinforcement Learning (DRL)-based solution. The proposed DRL agent learns efficient offloading policies by balancing computation and communication costs in the dynamic satellite environment. Simulation results show that our DRL-based approach significantly outperforms conventional benchmarks, achieving enhanced latency and energy performance, enabling intelligent orchestration of O-RAN slices over LEO satellite networks.
S. Shinde, Daniele Tarchi, Carlo Fischione· International Mediterranean...· 0 citations
With the rapid development of artificial intelligence and low-earth orbit (LEO) satellite edge computing technology, there has been a rapid increase in the demand for intelligence networks and services among users in remote areas, such as federated learning (FL). We propose a satellite aided computation FL (SACFL) system in LEO ubiquitous edge computing (UEC) networks, aiming at improving the efficiency of FL tasks in remote areas. In the considered deployment scenario, the following key factors are considered: 1) terrestrial users in remote areas; 2) offloading data to satellites for aided computation; and 3) global aggregation on satellite. However, the highly dynamic characteristics and the uneven distribution of satellite computation resources pose significant challenges to the low-delay requirements of satellite-based FL tasks. To this end, we formulate an optimization problem to minimize delay by jointly considering access selection, computation offloading, aggregation satellite (AgS) selection, and computation resource allocation. To solve the formulated problem, we propose a novel multi-agent alternating (M2A) optimization method. Specifically, three independent agents are trained alternately to make decisions on access selection, computation offloading, and AgS selection. Comprehensive simulations demonstrate that the proposed method outperforms other benchmark algorithms in terms of convergence, delay, and FL accuracy.
Junyi Yang, Yafeng Ma, Zhenyu Xiao et al.· IEEE Transactions on Cogniti...· 0 citations
With the rapid development of Low Earth Orbit (LEO) satellite networks, computational offloading and Network Function (NF) deployment have emerged as critical enablers for latency-sensitive services. However, existing approaches, such as the GBCO algorithm, typically execute tasks by locating satellites that have cached the required NF across the entire network, resulting in frequent inter-satellite transmissions and consequently increased system latency and energy consumption. To address this issue, this paper proposes a locality-sensitive computational offloading optimization algorithm, termed GCLSO. The proposed method prioritizes task execution on the access satellite to enhance computational locality, and under the condition that cache resources are limited, introduces an NF swapping mechanism to enable adaptive cache adjustment. The algorithm maintains low computational complexity while effectively reducing inter-satellite communication overhead. The simulation results demonstrate that, compared to the GBCO algorithm, the proposed approach achieves a significant improvement in time delay, while maintaining the same time complexity and without any increase in energy consumption.
Xvchao Wang, Tao Zhang, Yang Zhang· 2026 6th International Confe...· 0 citations
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.
Jingjing Qu, Peiying Zhang, Ruixin Wang et al.· Information· 0 citations
Low Earth orbit (LEO) satellite networks are envisioned as a promising solution for providing ubiquitous connectivity and narrowing the digital divide. The extensive footprint of LEO satellite constellations enables broad coverage, resulting in spatially non-uniform traffic demand across the serviced areas. Meanwhile, stringent on-board power constraints make power-intensive transmission architectures less attractive and motivate energy-efficient transmission strategies that effectively exploit scarce satellite network resources. To this end, this paper proposes a cooperative transmission framework that jointly accounts for non-uniform traffic demand and network-wide power consumption. Each LEO satellite integrates hybrid precoding (HPC), radio frequency (RF) chain activation, and hardware quantization, while user-equipment (UE)-centric satellite clusters are organized using statistical channel state information (sCSI) and traffic demands. A framework for joint optimization of cooperative transmission architecture and resource allocation is designed to maximize demand-aware energy efficiency (EE), resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable. Accordingly, a two-stage algorithm is developed under a distributed linear precoding structure, in which a modified cross-entropy (CE) method searches over discrete variables, while fractional programming is employed for transmit power allocation. Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE.
Wooseok Cha, Kyeongsoo Kim, Seonghoon Kim et al.· IEEE Transactions on Wireles...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
