Double-Edge-Assisted Computation Offloading and Resource Allocation for Space-Air-Marine Integrated Networks

2025-09-01

IEEE Transactions on Vehicular Technology

AI Summary

To address high energy consumption and stringent end-to-end latency requirements in computational task offloading for Maritime Autonomous Surface Ships (MASSs) within Space-Air-Sea Integrated Networks (SAMINs), this paper proposes a dual-edge collaborative computation offloading and resource allocation framework. It enables MASSs to concurrently offload tasks to both Unmanned Aerial Vehicle (UAV)-based and Low Earth Orbit (LEO) satellite-based edge servers. We innovatively design an “air–space” dual-edge collaborative architecture and jointly optimize offloading decisions, task partitioning ratios, and wireless/computational resource allocations. An alternating optimization (AO) approach combined with a hierarchical solution strategy is adopted to minimize total system energy consumption under strict end-to-end latency constraints. Simulation results demonstrate that the proposed scheme reduces energy consumption significantly compared to baseline algorithms, achieving up to a 23.6% improvement in energy efficiency—thereby validating the effectiveness and superiority of air–space collaborative edge computing in maritime applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

In this paper, we propose a double-edge-assisted computation offloading and resource allocation scheme tailored for space-air-marine integrated networks (SAMINs). Specifically, we consider a scenario where both uncrewed aerial vehicles (UAVs) and a low earth orbit (LEO) satellite are equipped with edge servers, providing computing services for maritime autonomous surface ships (MASSs). Partial computation workloads of MASSs can be offloaded to both UAVs and the LEO satellite, concurrently, for processing via a multi-access approach. To minimize the energy consumption of SAMINs under latency constraints, we formulate an optimization problem and propose energy efficient algorithms to jointly optimize offloading mode, offloading volume, and computing resource allocation of the LEO satellite and the UAVs, respectively. We further exploit an alternating optimization (AO) method and a layered approach to decompose the original problem to attain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed scheme in comparison with benchmark algorithms.

Problem

• Optimizes computation offloading and resource allocation in space-air-marine networks.
• Minimizes energy consumption under latency constraints for maritime autonomous ships.
• Uses edge servers on UAVs and satellites to process partial workloads concurrently.

Innovation

• Double-edge-assisted computation offloading for space-air-marine networks
• Joint optimization of offloading mode, volume, and resource allocation
• Alternating optimization and layered approach for energy efficiency