Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks
This addresses security risks in computation offloading for satellite networks, which is an incremental improvement for small and medium businesses in the NewSpace sector.
The paper tackles the problem of security-aware computation offloading in satellite networks by proposing a reinforcement learning-based algorithm, showing through Monte-Carlo simulations that it is effective under various conditions and provides insights into optimized computation location.
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.