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