J. J. Borsi

Bio: J. J. Borsi is an academic researcher. The author has contributed to research in topics: Scheduling (computing) & Air Force Satellite Control Network. The author has an hindex of 1, co-authored 1 publications receiving 25 citations.

Collaborative Data Downloading by Using Inter-Satellite Links in LEO Satellite Networks

Abstract: Satellite systems have attracted great attention from academic and industrial communities in recent years. There are many satellites that have been launched for weather forecast, environment monitoring, and target surveillance. One important task of the satellites is to download the data they have collected in space to the ground servers via earth stations (ESs). Since satellites move at high speed along their own orbits and have very limited contact time with ESs, satellites may not be able to download all the data they have to the ground on time. In this paper, we propose a collaborative scheme that allows satellites to offload data among themselves using inter-satellite links (ISLs) before they come into contact with the ES, such that satellites will carry the right amount of data according to the length of their contact time with the ES and the throughput of data downloading at the ES is maximized. We develop an iterative optimization algorithm that jointly schedules data offload among the satellites and data downloading from satellites to the ES. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offload can be increased significantly. In many cases, the throughput reaches close to 100% of the capacity of the ES.

Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem

Abstract: Satellite range scheduling is an important issue in the field of satellite mission planning, which greatly affects the development of satellite industry. This paper analyzed satellite range scheduling problem, constructed a multi-objective SRSP (MO-SRSP) model and proposed an improved multi-objective evolutionary algorithm (MOEA), called learning-guided nondominated sorting genetic algorithm II (LGNSGAII) that contains a learning mechanism. Learning mechanisms can speed up optimization process. Meanwhile, another algorithm called task-time window selection algorithm (TTSA) is also proposed. Specifically, it can select satellite ground stations time windows for tasks. TTSA includes three location selection methods and two location movement methods, both are used to select the appropriate execution location for tasks. Experiments show that the proposed algorithm can solve MO-SRSP better than several comparison algorithms. In other words, this algorithm we proposed has a broad practical application prospect.

Optimal Data Downloading by Using Inter-Satellite Offloading in LEO Satellite Networks

Abstract: In recent years a large number of LEO (Low Earth Orbit) satellites have been launched into space for various applications, such as weather forecast, environment monitoring and military surveillance. One important task of these satellites is to collect data and download them to Earth Stations (ESs) for further processing. Since LEO satellites fly at high speeds and have very limited contact time with ESs, they may not have sufficient time to download data to the ground. Existing work focused on scheduling data downloading from satellites to the ES such that the throughput of the downloading is maximized. However, what the scheduling algorithms can do is very limited in this scenario due to the disparity of the contact time a satellite has and the amount of data it carries for downloading. In this paper, we propose a novel scheme that uses inter-satellite links (ISLs) to offload data among satellites before they contact with the ES. A satellite with large amount of data but little contact time can offload data to other satellites that have surplus contact time. By offloading data among satellites on-the-fly in space, each satellite can carry the right amount of data when it comes to download its data to the ES, such that its contact time can be fully utilized. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offloading can be increased significantly.