Protecting lightweight block cipher implementation in mobile big data computing

2016 ◽  
Vol 11 (2) ◽  
pp. 252-264
Author(s):  
Weidong Qiu ◽  
Bozhong Liu ◽  
Can Ge ◽  
Lingzhi Xu ◽  
Xiaoming Tang ◽  
...  
Keyword(s):  
Big Data ◽  
Block Cipher ◽  
Mobile Big Data ◽  
Lightweight Block Cipher ◽  
Big Data Computing

scholarly journals The Direction of Lightweight Ciphers in Mobile Big Data Computing

2015 ◽  
Vol 72 ◽  
pp. 469-476 ◽  
Author(s):  
Alya Geogiana Buja ◽  
Shekh Faisal Abdul Latip
Keyword(s):  
Big Data ◽  
Mobile Big Data ◽  
Big Data Computing

Mobile Big Data Query Based on Double R-Tree and Double Indexing

2013 ◽  
Vol 756-759 ◽  
pp. 916-921
Author(s):  
Ye Liang
Keyword(s):  
Big Data ◽  
Dynamic Condition ◽  
Index Structure ◽  
Limited Area ◽  
Universal Method ◽  
Mobile Objects ◽  
Data Query ◽  
Mobile Big Data ◽  
Structure Query ◽  
Window Query

The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data, which is also called big data, but also to analyze and query from it. In order to put forward the universal method to response mobile big data query, queries are separated and grouped according to kinds of query for massive mobile objects in the space. The indexing method for grouping the mobile objects with Grid (GG TPR-tree) has great efficiency to manage a massive capacity of mobile objects within a limited area, but it only could meet a part of requirements for mobile big data query if the GG TPR-tree was used solely. This thesis offers solutions to simple immediate query, simple continuous query, active window query, and continuous window query, dynamic condition query and other query requests by employing DTDI index structure. The experiments prove that with the support of DTDI index structure, query of massive mobile objects has higher precision and better query performance.

Eight-sided fortress: a lightweight block cipher

2014 ◽  
Vol 21 (1) ◽  
pp. 104-128
Author(s):  
Xuan LIU ◽  
Wen-ying ZHANG ◽  
Xiang-zhong LIU ◽  
Feng LIU
Keyword(s):  
Block Cipher ◽  
Lightweight Block Cipher

The Big Picture: Big Data Computing

CHANCE ◽  
2013 ◽  
Vol 26 (2) ◽  
pp. 28-32 ◽  
Author(s):  
Nicole Lazar
Keyword(s):  
Big Data ◽  
Big Picture ◽  
Big Data Computing

scholarly journals Strategies to Implement Edge Computing in a P2P Pervasive Grid

2018 ◽  
Vol 11 (1) ◽  
pp. 1-15
Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Edge Computing ◽  
Computing Platform ◽  
Geophysical Application ◽  
Two Factors ◽  
Computing Platforms ◽  
Distributed Computing Platform ◽  
Big Data Computing ◽  
Analyze Data

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

scholarly journals Infrastructure and Energy Conservation in Big Data Computing: A Survey

2019 ◽  
Vol 2 ◽  
pp. 73-82
Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Michał P. Karpowicz
Keyword(s):  
Big Data ◽  
High Performance ◽  
Physical Sciences ◽  
Communication Management ◽  
Data Intensive ◽  
Efficient Data ◽  
Software Platforms ◽  
Allocation Algorithms ◽  
And Storage ◽  
Big Data Computing

Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of efficient HPC infrastructures. This paper addresses the vital problem of energy-efficient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

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