A serious gaming approach for optimization of energy allocation in CubeSats

Abstract

Energy consumption remains an open challenge in aerial systems such as CubeSats and therefore optimization of its allocation is a top priority for maximizing operational capacity. Our research review reveals a plethora of approaches for optimization of energy allocation and all achieving varying degrees of success and not without any compromises. In this paper, we exploit the use of serious gaming in a novel energy allocation algorithm that aims at minimizing energy consumption to maximize the utilities of both CubeSats and terrestrial sensors. To demonstrate this, we use Stackelberg for serious gaming and standalone topology for CubeSat configuration. The experimental results show that the use of a Stackelberg game approach for optimization has led to reduction in the required transmission energy in sensors, an improved link performance between the CubeSat and ground sensors, and an increase in network lifetime and performance without resorting into sensor power enhancements or other external power sources. The overall average operational capacity improvement predictions range between 22 to 27% across all performance indicators of energy efficiency across RF chains of link budgets.