scholarly journals HEAP: An Efficient and Fault-Tolerant Authentication and Key Exchange Protocol for Hadoop-Assisted Big Data Platform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75342-75382 ◽  
Author(s):  
Durbadal Chattaraj ◽  
Monalisa Sarma ◽  
Ashok Kumar Das ◽  
Neeraj Kumar ◽  
Joel J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Big Data ◽  
Fault Tolerant ◽  
Key Exchange ◽  
Key Exchange Protocol ◽  
Data Platform

Key Exchange Protocol Supporting Mobility and Multihoming

2006 ◽  
Vol 1 (2) ◽  
pp. 52-70
Author(s):  
Mohammed A. Tawfiq ◽  
◽  
Sufyan T. Faraj Al-janabi ◽  
Abdul-Karim A. R. Kadhim ◽  
◽  
...  
Keyword(s):  
Key Exchange ◽  
Key Exchange Protocol

Efficient group key exchange protocol with strong security

2010 ◽  
Vol 30 (7) ◽  
pp. 1805-1808
Author(s):  
Shao-feng DENG ◽  
Fan DENG ◽  
Yi-fa LI
Keyword(s):  
Key Exchange ◽  
Key Exchange Protocol ◽  
Group Key ◽  
Group Key Exchange

RIPIC BASED KEY EXCHANGE PROTOCOL

2020 ◽  
Vol 9 (12) ◽  
pp. 11169-11177
Author(s):  
A. J. Meshram ◽  
C. Meshram ◽  
S. D. Bagde ◽  
R. R. Meshram
Keyword(s):  
Key Exchange ◽  
Key Exchange Protocol

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

A monitoring framework for transparency and fairness in big data platform

2021 ◽  
Author(s):  
Karima Aslaoui Mokhtari ◽  
Salima Benbernou ◽  
Mourad Ouziri ◽  
Hakim Lahmar ◽  
Muhammad Younas
Keyword(s):  
Big Data ◽  
Data Platform ◽  
Monitoring Framework
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