Abstract
According to the challenges of storage and computation faced by massive, multi-source and heterogeneous GNSS data, the design objective of cloud GNSS is analyzed, then the architecture of cloud GNSS from infrastructure, data management, service management to application is designed, the deployment model including service management platform, Web server cluster and multiple Hadoop clusters is provided, and its’ characteristics such as strong expansibility, high reliability, loose coupling, are summarized. Cloud GNSS platform is built in the experiment, the storage model of massive GNSS data and the parallel computing model of GNSS network are built, distributed storage, parallel retrieval, sub-network division distributed computing and data publication are achieved. The result shows that the architecture proposed by the paper can be applied in the storage, processing and service publication of large-scale GNSS network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
UNAVCO (2014) GPS/GNSS Data management overview. http://www.unavco.org/data/gps-gnss/data-management/data-management.html
Li L, Lu Z, Chen Z (2014) Cloud storage model of massive continuous operating reference station system data. J Navig Positioning 2(3):64–70
Lian X, Zhengquan XU, Tao W (2014) On the store strategy of small spatio-temporal data files in cloud environment. Geomatics Inf Sci Wuhan Univ 39(10): 1252–1256
Boomkamp H (2010) Global GPS, reference frame solutions of unlimited size. Adv Space Res 46:136–143
Serpelloni E, Casula G, Galvani A, Anzidei M, Baldi P (2006) Data analysis of permanent GPS networks in Italy and surrounding regions: application of a distributed processing approach. Ann Geophys 49(4/5):897–928
Schrock GM (2006) On-grid goal: seeking support for high-precision networks. GPS World 17(10):34–40
Li J, Li J (2009) A new computing theory model on large-scale CORS. Geomatics Inf Sci Wuhan Univ 34(8):992–995
Lu Z, Chen Z, Cui Y (2013) Distributed processing of large-scale CORS network’s baseline vectors. J Geomatics Sci Technol 30(4):433–438
Ge M, Gendt G, Rothacher M (2008) Resolution of GPS carrier-phase ambiguities in precise point positioning (PPP) with daily observations. J Geodesy 82(7):389–399
Shi C (2002) Theory and application of the adjustment of large scale high precision GPS network. Surveying and Mapping Press, Beijing
Ge M, Gendt G (2006) Dick G. A new data processing strategy for huge GNSS global networks 80(4):199–203
Blewitt G (2008) Fixed point theorems of GPS carrier phase ambiguity resolution and their application to massive network processing: ambizap. J Geophys Res 113:B12410
Hua C, Jiang W, Ge M (2014) An enhanced strategy for GNSS data processing of massive networks. J Geodesy 88(9):857–867
Li D, Yao Y, Shao Z (2014) Big data in small city. Geomatics Inf Sci Wuhan Univ 39(6):631–640
Acknowledgments
The work is funded by the National Natural Science Foundation of China, No. 41274015; the National 863 Program of China, No. 2013AA122501, State Key Laboratory of Geo-information Engineering, NO. SKLGIE2014-M-1-5 and NO. SKLGIE2014-M-1-6.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, L., Lu, Z., Fan, L., Li, J. (2015). Research on the Architecture of Cloud GNSS Based on Hadoop. In: Sun, J., Liu, J., Fan, S., Lu, X. (eds) China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III. Lecture Notes in Electrical Engineering, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46632-2_62
Download citation
DOI: https://doi.org/10.1007/978-3-662-46632-2_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46631-5
Online ISBN: 978-3-662-46632-2
eBook Packages: EngineeringEngineering (R0)