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

TL;DR

This paper introduces a novel double-edge-assisted offloading scheme for space-air-marine networks, optimizing energy and latency by jointly managing resources across UAVs and satellites, validated through simulations.

Contribution

It proposes a new offloading and resource allocation scheme for SAMINs using double-edge servers, with an optimization framework and algorithms for energy efficiency and latency reduction.

Findings

The proposed scheme reduces energy consumption compared to benchmarks.

Joint optimization improves resource utilization and latency.

Simulation results validate the scheme's effectiveness.

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 unmanned 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.