Energy-Aware Task Offloading for Drone-Enabled SAGSINs: A Lyapunov-Based Approach
TL;DR
Simulation results demonstrate that the proposed strategy effectively reduces energy consumption, ensures low delay, and maintains long-term queue stability in drone-enabled SAGSINs under dynamic task demands from UE.
Abstract
Bolstered by emerging sixth-generation (6G) communication technology, space–air–ground– sea integrated networks (SAGSINs) are reshaping edge computing through the synergistic use of space, aerial, terrestrial, and maritime platforms. However, in such highly dynamic and heterogeneous network environments, long-term energy-efficient computation offloading in drone-enabled SAGSINs has not yet been thoroughly explored, particularly when dynamic task demands from user equipment (UE) are served under the constraints of energy-limited drones. To fill this gap, the problem of service node association and computing-frequency allocation under dynamic computation offloading demands at the edge of the networks is studied in this paper. However, the related problem turns out to be a stochastic optimization problem. To this end, through in-depth mathematical analysis based on the Lyapunov optimization method, it is found that the multi-time-slot long-term optimization problem can be transformed into several single-time-slot optimization problems, which enables an efficient solution to the original problem. The single time-slot problem is solved using graph theory, convex optimization, and optimization theory, where Lyapunov optimization is utilized to achieve queue stability and energy efficiency. Simulation results demonstrate that the proposed strategy effectively reduces energy consumption, ensures low delay, and maintains long-term queue stability in drone-enabled SAGSINs under dynamic task demands from UE.