scholarly journals Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6404
Author(s):  
Victor Ortega ◽  
Jose F. Monserrat
Keyword(s):  
Vehicular Networks ◽  
Autonomous Agents ◽  
File System ◽  
Distributed Storage ◽  
Intelligent Vehicles ◽  
Distributed Data ◽  
Distributed Network ◽  
Fast System ◽  
Remote Sensors ◽  
Shared Data

Vehicular networks provide means to distribute data among intelligent vehicles, increasing their efficiency and the safety of their occupants. While connected to these networks, vehicles have access to various kinds of information shared by other vehicles and road-side units (RSUs). This information includes helpful resources, such as traffic state or remote sensors. An efficient and fast system to get access to this information is important but unproductive if the data are not appropriately structured, accessible, and easy to process. This paper proposes the creation of a semantic distributed network using content-addressed networking and peer-to-peer (P2P) connections. In this open and collaborative network, RSUs and vehicles use ontologies to semantically represent information and facilitate the development of intelligent autonomous agents capable of navigating and processing the shared data. In order to create this P2P network, this paper makes use of the Inter-Planetary File System (IPFS), an open source solution that provides secure, reliable, and efficient content-addressed distributed storage over standard IP networks using the new QUIC protocol. This paper highlights the feasibility of this proposal and compares it with the state-of-the-art. Results show that IPFS is a promising technology that offers a great balance between functionality, performance, and security.

Conversion and Display of the Calculated Data of Spectral Remote Sensing Data on the Basis of GeoServer Extensions and Distributed Storage Technologies

2021 ◽  
Vol 12 (2) ◽  
pp. 107-112
Author(s):  
I. E. Kharlampenkov ◽  
◽  
A. U. Oshchepkov ◽  
Keyword(s):  
Remote Sensing ◽  
Information Technologies ◽  
File System ◽  
Distributed Storage ◽  
Calculated Data ◽  
Remote Sensing Data ◽  
Distributed Computing Systems ◽  
Apache Hadoop ◽  
Distributed Information ◽  
Sensing Data

The article presents methods for caching and displaying data from spectral satellite images using libraries of distributed computing systems that are part of the Apache Hadoop ecosystem, and GeoServer extensions. The authors gave a brief overview of existing tools that provide the ability to present remote sensing data using distributed information technologies. A distinctive feature is the way to convert remote sensing data inside Apache Parquet files for further display. This approach allows you to interact with the distributed file system via the Kite SDK libraries and switch on additional data processors based on Apache Hadoop technology as external services. A comparative analysis of existing tools, such as: GeoMesa, GeoWawe, etc is performed. The following steps are described: extracting data from Apache Parquet via the Kite SDK, converting this data to GDAL Dataset, iterating the received data, and saving it inside the file system in BIL format. In this article, the BIL format is used for the GeoServer cache. The extension was implemented and published under the Apache License on the GitHub resource. In conclusion, you will find instructions for installing and using the created extension.

scholarly journals An Architecture for Distributed Electronic Documents Storage in Decentralized Blockchain B2B Applications

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 142
Author(s):  
Obadah Hammoud ◽  
Ivan Tarkhanov ◽  
Artyom Kosmarski
Keyword(s):  
Distributed Systems ◽  
Data Storage ◽  
Distributed Storage ◽  
Distributed Data ◽  
Erasure Coding ◽  
Distributed Data Storage ◽  
Electronic Documents ◽  
File Storage ◽  
Load Balancer ◽  
The Cost

This paper investigates the problem of distributed storage of electronic documents (both metadata and files) in decentralized blockchain-based b2b systems (DApps). The need to reduce the cost of implementing such systems and the insufficient elaboration of the issue of storing big data in DLT are considered. An approach for building such systems is proposed, which allows optimizing the size of the required storage (by using Erasure coding) and simultaneously providing secure data storage in geographically distributed systems of a company, or within a consortium of companies. The novelty of this solution is that we are the first who combine enterprise DLT with distributed file storage, in which the availability of files is controlled. The results of our experiment demonstrate that the speed of the described DApp is comparable to known b2c torrent projects, and subsequently justify the choice of Hyperledger Fabric and Ethereum Enterprise for its use. Obtained test results show that public blockchain networks are not suitable for creating such a b2b system. The proposed system solves the main challenges of distributed data storage by grouping data into clusters and managing them with a load balancer, while preventing data tempering using a blockchain network. The considered DApps storage methodology easily scales horizontally in terms of distributed file storage and can be deployed on cloud computing technologies, while minimizing the required storage space. We compare this approach with known methods of file storage in distributed systems, including central storage, torrents, IPFS, and Storj. The reliability of this approach is calculated and the result is compared to traditional solutions based on full backup.

scholarly journals Distributed Data Processing in Vehicular Networks using Mobile Agents

1970 ◽  
Vol 2 ◽  
pp. 61-62
Author(s):  
Óscar Urra ◽  
Sergio Ilarri
Keyword(s):  
Wireless Communications ◽  
Mobile Agent ◽  
Open Problem ◽  
Mobile Agents ◽  
Short Range ◽  
Vehicular Networks ◽  
Vehicular Network ◽  
Agent Technology ◽  
Distributed Data ◽  
Distributed Data Processing

In a vehicular network, vehicles can exchange interesting information (e.g., about accidents, traffic status, etc.) using short-range wireless communications. Besides, the vehicles can be equipped with additional sensors that can directly obtain data from the environment. How to efficiently process and collect these data is an open problem. We argue that mobile agent technology could be helpful.

scholarly journals Multiagent System for Mutual Collaboration Classification for Cancer Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Mais Haj Qasem ◽  
Amjad Hudaib ◽  
Nadim Obeid
Keyword(s):  
Cancer Detection ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Distributed Data ◽  
Distributed Clustering ◽  
Classification Tasks ◽  
Communication And Coordination ◽  
Prediction Techniques ◽  
Distributed Classification ◽  
And Behavior

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

Research on Distributed Storage Technology Based on Mass Data

2014 ◽  
Vol 687-691 ◽  
pp. 2710-2713
Author(s):  
Jing Yang
Keyword(s):  
Data Storage ◽  
Distributed Storage ◽  
Rapid Development ◽  
Distributed Data ◽  
Distributed Environment ◽  
Environment Analysis ◽  
Distributed Data Storage ◽  
Storage Technology ◽  
Mass Data ◽  
Storage Performance

With the rapid development of computer technology and network technology, mass data store distributed and management pattern already received accepted extensively. Thus it can be seen malpractice obviously, data storage structure, storage environment are different and other problems such as data handing. The paper go into how improve data storage performance in the distributed environment, analysis the data storage technology at present and data storage performance in the distributed environment, summarize the claim of distributed storage database design, provide the theory in vacation distributed data storage performance standardization.

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

scholarly journals REQUEST BALANCING METHOD FOR INCREASING THEIR PROCESSING EFFICIENCY WITH INFORMATION REPLICATION IN A DISTRIBUTED DATA STORAGE SYSTEM

2021 ◽  
pp. 75-82
Author(s):  
Igor Boyarshin ◽  
Anna Doroshenko ◽  
Pavlo Rehida
Keyword(s):  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
New Method ◽  
Distributed Data ◽  
Processing Efficiency ◽  
Distributed Data Storage ◽  
Shared Data ◽  
Multiple Data ◽  
Data Storage System

The article describes a new method of improving efficiency of the systems that deal with storage and providing access of shared data of many users by utilizing replication. Existing methods of load balancing in data storage systems are described, namely RR and WRR. A new method of request balancing among multiple data storage nodes is proposed, that is able to adjust to input request stream intensity in real time and utilize disk space efficiently while doing so.

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