A Joint Delay-Energy-Security Aware Framework for Intelligent Task Scheduling in Satellite-Terrestrial Edge Computing Network

TL;DR

The paper tackles secure task scheduling in STECNs under joint delay, energy, and physical-layer security constraints. It introduces a two-stage approach: Stage 1 discretizes the AN power ratio $\psi$ and selects secure user associations to satisfy secrecy requirements, while Stage 2 applies a Mayfly Algorithm (MA) to minimize a weighted sum of delay and energy for the chosen associations, with $R^{sec}_{n_0,u}(t) \ge \epsilon$ guiding security. Key contributions include the discretization of $\psi$ to maximize the reliable transmission proportion $\Gamma(\psi)$, and the MA-based scheduling framework with a defined complexity budget. Simulations with a 20-satellite STECN demonstrate improved secrecy, reduced delay, and lower energy consumption compared with baselines, validating the framework’s effectiveness for dynamic satellite-edge environments.

Abstract

In this paper, we propose a two-stage optimization framework for secure task scheduling in satellite-terrestrial edge computing networks (STECNs). The framework jointly considers secure user association and task offloading to balance transmission delay, energy consumption, and physical-layer security. To address the inherent complexity, we decouple the problem into two stages. In the first stage, a secrecy-aware user association strategy is designed by discretizing artificial noise (AN) power ratios and identifying feasible links that satisfy secrecy constraints, resulting in a set of candidate secure associations. In the second stage, we formulate a delay-energy-aware task scheduling problem as an integer linear program and solve it using a heuristic Mayfly Algorithm (MA) to obtain low-complexity, high-quality solutions. Extensive simulation results demonstrate the effectiveness and superiority of the proposed framework in achieving secure and efficient task scheduling under dynamic satellite environments.

Figures (4)

• Figure 1: Proposed two-stage heuristic Algorithm.
• Figure 2: Task scheduling converge performance of different schemes.
• Figure 3: Performance of different schemes under different number of Legitimate users.
• Figure 4: Performance of different schemes under different number of Eavesdroppers.