Network Architecture For Big Data Transmissions

2018 ◽  
pp. 85-148
Author(s):  
MISSING-VALUE MISSING-VALUE
Keyword(s):  
Big Data ◽  
Network Architecture

Digitalization in the system of anti-corruption measures

2020 ◽  
Vol 22 (5) ◽  
pp. 51-55
Author(s):  
OLEG N. KORCHAGIN ◽  
◽  
ANASTASIA V. LYADSKAYA ◽  
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Public Administration ◽  
Network Architecture ◽  
Digital Technologies ◽  
Current State ◽  
Foreign Countries ◽  
Separate Area ◽  
Private Business Sector ◽  
The Given

The article is devoted to the current state of digitalization aimed at solving urgent problems of combating corruption in the field of public administration and private business sector. The work considers the experience of foreign countries and the influence of digital technologies on the fight against corruption. It is noted that the digitalization of public administration is becoming one of the decisive factors for increasing the efficiency of the anti-corruption system and improving management mechanisms. Big Data, if integrated and structured according to the given parameters, allows the implementation of legislative, law enforcement, control and supervisory and law enforcement activities reliably and transparently. Big Data tools allow us to analyze processes, identify dependencies and predict corruption risks. The author describes the most significant problems that complicate the transfer of offline technologies into the online environment. The paper analyzes promising directions for the development of digital technologies that would lead to solving the arising problems, as well as to implement tasks that previously seemed unreachable. The article also describes current developments in the field of collecting and managing large amounts of data, the “Internet of Things”, modern network architecture, and other advances in the field of IT; the work provides applied examples of their potential use in the field of combating corruption. The study gives reasons that, in the context of combating corruption, digitalization should be allocated in a separate area of activity that is controlled and regulated by the state.

Deep Bayesian network architecture for Big Data mining

2018 ◽  
Vol 31 (2) ◽  
pp. e4418 ◽  
Author(s):  
Hasna Njah ◽  
Salma Jamoussi ◽  
Walid Mahdi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Bayesian Network ◽  
Network Architecture ◽  
Big Data Mining

Research of Big Data Processing Platform

2014 ◽  
Vol 484-485 ◽  
pp. 922-926
Author(s):  
Xiang Ju Liu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Network Architecture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Computing Platform ◽  
Operational Characteristics ◽  
Analysis Platform

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

Efficient Vision-based Smart Meter Reading Network

2017 ◽  
Vol 14 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Ching-Han Chen ◽  
Ching-Yi Chen ◽  
Chih-Hsien Hsia ◽  
Guan-Xin Wu
Keyword(s):  
Big Data ◽  
Network Architecture ◽  
Image Sensor ◽  
Sensor Nodes ◽  
Energy Management System ◽  
Smart Meter ◽  
Web Based ◽  
Gas Consumption ◽  
Embedded Image ◽  
Gas Supply

For building the big data collection infrastructure, a vision-based smart meter-reading network and its gateway is provided for a community gas supply system that uses traditional mechanical meters. In the network architecture, the gas meter readings are captured by embedded image sensor nodes and then transmitted to a newly designed gateway for image recognition and are collected in the embedded database of gateway. The Web-based monitoring system designed using HTML5 is applicable to a mobile device which allows a user to monitor household gas consumption and history and allows a gas company to develop an effective energy management system to analyze community users' energy consumption models using the big data collected in the database.

scholarly journals Abstraction of meso-scale network architecture in granular ensembles using ‘big data analytics’ tools

2018 ◽  
Vol 2 (3) ◽  
pp. 031004 ◽  
Author(s):  
Raj Kishore ◽  
R Krishnan ◽  
Manoranjan Satpathy ◽  
Zohar Nussinov ◽  
Kisor K Sahu
Keyword(s):  
Big Data ◽  
Network Architecture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Scale Network ◽  
Meso Scale

scholarly journals Big Data Collection in Large-Scale Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4474 ◽  
Author(s):  
Asside Djedouboum ◽  
Ado Abba Ari ◽  
Abdelhak Gueroui ◽  
Alidou Mohamadou ◽  
Zibouda Aliouat
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Sensor Networks ◽  
Data Collection ◽  
Network Architecture ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Wireless Sensor ◽  
The Military ◽  
The Impact

Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

scholarly journals BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 517
Author(s):  
Rakib Hossen ◽  
Md Whaiduzzaman ◽  
Mohammed Nasir Uddin ◽  
Md. Jahidul Islam ◽  
Nuruzzaman Faruqui ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Shortest Path ◽  
Network Architecture ◽  
Data Delivery ◽  
Dijkstra Algorithm ◽  
Big Data Processing ◽  
Processing Scheme ◽  
Depth First Search ◽  
Efficient Data

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

Sign in / Sign up

Export Citation Format

Share Document