Satellite edge computing for real-time and very-high resolution Earth observation

In real-time and high-resolution Earth observation imagery, Low Orbit (LEO) satellites capture images that are subsequently transmitted to ground create an updated map of area interest. Such maps provide valuable information for meteorology or environmental monitoring, but can also be employed in near-real time operation disaster detection, identification, management. However, the amount data generated by these applications easily exceed communication capabilities LEO satellites, leading congestion packet dropping. To avoid problems, Inter-Satellite Links (ISLs) used distribute among processing. this paper, we address energy minimization problem based on a general satellite mobile edge computing (SMEC) framework very-high resolution observation. Our results illustrate optimal allocation selection compression parameters increase system support factor 12 when compared directly downloading data. Further, savings greater than 11% were observed real-life scenario imaging volcanic island, while sensitivity analysis image acquisition process demonstrates potential as high 92%.