Distributed storage method based on information dispersal algorithm

2013 ◽  
Author(s):  
Jie Ling ◽  
Xiangyang Jiang
Keyword(s):  
Distributed Storage ◽  
Storage Method ◽  
Information Dispersal

scholarly journals Efficient Local Secret Sharing for Distributed Blockchain Systems

2021 ◽  
Vol 9 (VI) ◽  
pp. 2043-2048
Author(s):  
G. Latha
Keyword(s):  
Secret Sharing ◽  
Distributed Storage ◽  
Denial Of Service ◽  
Distributed Database ◽  
Storage Efficiency ◽  
Communication Costs ◽  
Transaction Data ◽  
Private Key ◽  
Information Dispersal ◽  
High Storage

Blockchain system store transaction data in the form of a distributed database where each peer is to maintain an identical copy. Blockchain systems resemble repetition codes, incurring high storage cost. Recently, distributed storage blockchain (DSB) systems have been proposed to improve storage efficiency by incorporating secret sharing, private key encryption, and information dispersal algorithms. However, the DSB results in significant communication cost when peer failures occur due to denial of service attacks. In this project, we propose a new DSB approach based on a local secret sharing (LSS) scheme with a hierarchical secret structure of one global secret node and several local secret nodes. The proposed DSB approach with LSS improves the storage and recovery communication costs.

scholarly journals Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

2020 ◽  
Vol 12 (6) ◽  
pp. 972 ◽  
Author(s):  
Yinyi Cheng ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Jining Yan
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Data Integration ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Integration Framework ◽  
Sensing Data ◽  
Storage Method

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

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