Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks
| Autor: | Saurav Sthapit, Subhash Lakshminarayana, Ligang He, Gregory Epiphaniou, Carsten Maple |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Cryptography and Security Computer Networks and Communications Hardware and Architecture Computer Science - Information Theory Information Theory (cs.IT) Signal Processing Cryptography and Security (cs.CR) Computer Science Applications Information Systems |
| DOI: | 10.48550/arxiv.2111.05259 |
| Popis: | 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 paper, we analyse 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 optimised location of computation. |
| Databáze: | OpenAIRE |
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