Computation Offloading in LEO Satellite Networks with Hybrid Cloud and Edge Computing

Qingqing Tang, Zesong Fei, Bin Li, Zhu Han

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Original languageEnglish
Article number9344666
Pages (from-to)9164-9176
Number of pages13
JournalIEEE Internet of Things Journal
Volume8
Issue number11
DOIs
Publication statusPublished - 1 Jun 2021

Keywords

  • Alternating direction method of multipliers (ADMMs)
  • cloud and edge computing
  • computation offloading
  • low earth orbit (LEO) satellite networks

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