Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks

TL;DR

This paper introduces a reinforcement learning-based algorithm for secure computation offloading in satellite networks, addressing security concerns and optimizing resource use in space-based computing environments.

Contribution

It proposes a novel security-aware offloading algorithm using Deep Deterministic Policy Gradient (DDPG) for satellite systems, considering security risks and resource constraints.

Findings

The algorithm effectively manages offloading decisions under various conditions.

Simulation results demonstrate improved security and efficiency.

Provides new insights into optimal computation placement in space networks.

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 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.