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Cluster Computing

, Volume 20, Issue 3, pp 2299–2310 | Cite as

Fast lightweight reconfiguration of virtual constellation for obtaining of earth observation big data

  • Lijun DongEmail author
  • Hong Yao
  • Rajiv Ranjan
  • Feng Zhang
  • Mengqi Pan
Article

Abstract

Earth observation (EO) big data is playing the increasingly important role in spatial sciences. To obtain adequate EO data, virtual constellation is proposed to overcome the limitation of traditional EO facilities, by combining the existing space and ground segment capabilities. However, the current configuration pattern of virtual constellation is tightly coupled with the specific application requirements. This leads to the costly reconfigurations. Although the pattern of software defined satellite network can decouple topology reconfigurations from application requirements, it cannot be directly applied to the reconfigurations of virtual constellations because of some drawbacks. To address the problem, we propose a model of LEO-ground links control-covering (LGLC) to implement fast and lightweight reconfiguration for virtual constellation. LGLC uses a bipartite graph model to formalize the dispatch problem of the control information of virtual constellation reconfiguration, and the optimum solution can be got by the classical algorithm in polynomial time. According to the strategy obtained, only if a few satellites and stations receive the control information, virtual constellation can be reconfigured quickly. We also establish some metrics to evaluate the effect of LGLC. Extensive experiments are conducted to confirm the above claims.

Keywords

Earth observation big data Virtual constellation Software defined satellite network Network topology reconfiguration 

Nomenclature

\(\mathcal {T}\)

The set of timeslots

\(V_G\)

The set of GEO satellites

\(V_L\)

The set of LEO satellites

\(U_L\)

The set of LEO satellite-footprints (\(U_L=V_L\times \mathcal {T}\))

\(V_S\)

The set of ground stations

\(C^{(l)}_m(t)\)

The VCL consumption of satellite l covering m ground stations in timeslot t

\(C^n_{(s)}(t)\)

The VCL consumption of station s covered by n LEO satellites in timeslot t

\(Q_1(g,u)\)

The burden rate of the controlling endpoint g on CTL \(\langle g,u\rangle \) (\(g\in V_G,u\in U_L\))

\(Q_2(g,u)\)

The burden rate of the controlled endpoint u on CTL \(\langle g,u\rangle \) (\(g\in V_G,u\in U_L\))

\(Q_I\)

The global burden rate of controlling endpoints

\(Q_{II}\)

The global burden rate of controlled endpoints

\(Q_{c}(g,u)\)

The burden rate of CTL \(\langle g,u\rangle \) (\(g\in V_G,u\in U_L\))

\(Q_{C}\)

The global burden rate of CTLs

\(Q_{F}\)

CTL fairness

\(v\vdash t\)

CTL fairness

OHL

The one-hop link between a LEO satellite and a ground station

ISL

The link between two LEO satellites

IGL

The link between two ground stations

CTL

The control link between a GEO satellite and a LEO satellite (or ground station)

VCL

Virtual covering link

UTVG

Unifying time-varying graph

TVG

Time-varying graph

CC

Control-covering

ICC

Immediate control-covering

EO

Earth observation

MWVC

The minimum weighted vertex cover

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61672474) and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (No. KLIGIP201611).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Intelligent Geo-Information ProcessingChina University of GeosciencesWuhanChina

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