Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks

Saurav Sthapit, Subhash Lakshminarayana, Ligang He, Gregory Epiphaniou, Carsten Maple

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
14 Downloads (Pure)

Abstract

The rise of NewSpace provides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites, NewSpace provides the opportunity for delivering computing platforms in space. However, computational resources within space are usually expensive and satellites may not be able to compute all computational tasks locally. Computation offloading (CO), a popular practice in Edge/Fog computing, could prove effective in saving energy and time in this resource-limited space ecosystem. However, CO alters the threat and risk profile of the system. In this article, we analyze security issues in space systems and propose a security-aware algorithm for CO. Our method is based on the reinforcement learning technique, deep deterministic policy gradient (DDPG). We show, using Monte-Carlo simulations, that our algorithm is effective under a variety of environment and network conditions and provide novel insights into the challenge of optimized location of computation.
Original languageEnglish
Article number9651535
Pages (from-to)12351-12363
Number of pages13
JournalIEEE Internet of Things Journal
Volume9
Issue number14
Early online date15 Dec 2021
DOIs
Publication statusPublished - 15 Jul 2022
Externally publishedYes

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Keywords

  • Satellites
  • Space vehicles
  • Earth
  • Security
  • Planetary orbits
  • Sensors
  • Encryption

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