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

10 Citations (Scopus)
29 Downloads (Pure)


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
Issue number14
Early online date15 Dec 2021
Publication statusPublished - 15 Jul 2022
Externally publishedYes

Bibliographical note

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


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


Dive into the research topics of 'Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks'. Together they form a unique fingerprint.

Cite this