Your browser doesn't support javascript.
loading
A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation.
Chen, Qing; Zhang, Jinxiu; Hu, Ze.
Afiliación
  • Chen Q; Research Center on Satellite Technology, Harbin Institute of Technology, Harbin 15001, China. 4B918054@hit.edu.cn.
  • Zhang J; Research Center on Satellite Technology, Harbin Institute of Technology, Harbin 15001, China. jinxiu@hit.edu.cn.
  • Hu Z; School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. huze@ftcl.hit.edu.cn.
Sensors (Basel) ; 17(3)2017 Feb 23.
Article en En | MEDLINE | ID: mdl-28241474
ABSTRACT
This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites' relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2017 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2017 Tipo del documento: Article País de afiliación: China