Encrypted Query Data Processing in Internet Of Things(IoTs) CryptDB and Trusted DB

2018 ◽  
Vol 6 (8) ◽  
pp. 735-741
Author(s):  
G. Ambika ◽  
P. Srivaramangai
Keyword(s):  
Internet Of Things ◽  
Data Processing

Potentialities of data processing in internet of things applications

2021 ◽  
Vol 14 (5) ◽  
pp. 486
Author(s):  
Ivan Santana ◽  
Cristian Duran Faundez ◽  
Jorge Portal ◽  
Reyneris De_la_Paz ◽  
Arturo Cardenas_Rivero
Keyword(s):  
Internet Of Things ◽  
Data Processing

scholarly journals Construction of a comprehensive observation platform for natural resource elements based on Internet of Things and Open Data Processing Service technologies

资源科学 ◽  
2020 ◽  
Vol 42 (10) ◽  
pp. 1965-1974
Author(s):  
Yi SUN ◽  
Mengyang FANG ◽  
Jianning HE ◽  
Jiufen LIU ◽  
Siyuan ZHANG ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
Natural Resource ◽  
Open Data ◽  
Resource Elements ◽  
Processing Service

scholarly journals Performance Assay of Big IoT Data Analytics Framework

2019 ◽  
Vol 8 (4) ◽  
pp. 8593-8596
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
High Velocity ◽  
Data Analytics ◽  
Real Life ◽  
Global Environment ◽  
Stream Data ◽  
Processing Level ◽  
Stream Analytics ◽  
Apache Storm

Evaluation of Internet of Things (IoT) technologies in real life has scaled the enumeration of data in huge volumes and that too with high velocity, and thus a new issue has come into picture that is of management & analytics of this BIG IOT STREAM data. In order to optimize the performance of the IoT Machines and services provided by the vendors, industry is giving high priority to analyze this big IoT Stream Data for surviving in the competitive global environment. Thses analysis are done through number of applications using various Data Analytics Framework, which require obtaining the valuable information intelligently from a large amount of real-time produced data. This paper, discusses the challenges and issues faced by distributed stream analytics frameworks at the data processing level and tries to recommend a possible a Scalable Framework to adapt with the volume and velocity of Big IoT Stream Data. Experiments focus on evaluating the performance of three Distributed Stream Analytics Here Analytics frameworks, namely Apache Spark, Splunk and Apache Storm are being evaluated over large steam IoT data on latency & throughput as parameters in respect to concurrency. The outcome of the paper is to find the best possible existing framework and recommend a possible scalable framework.

scholarly journals Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Min Yu ◽  
Rongrong Cui
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Design System ◽  
Design Effect ◽  
Constraint Matrix ◽  
Clothing Design ◽  
Culture Design

In order to improve the design effect of minority clothing, according to the needs of minority clothing design, this paper uses data mining and Internet of Things technologies to construct an intelligent ethnic clothing design system and builds an intelligent clothing design system that meets customer needs based on the idea of human-computer interaction. In data processing, this paper uses the constraint spectrum clustering algorithm to take the Laplacian matrix and the constraint matrix as input and finally outputs a clustering indicator vector to improve the data processing effect of minority clothing design. Finally, this paper verifies the performance of the system designed in this paper through experiments. From the experimental research, it can be known that the minority clothing design system based on the Internet of Things and data mining constructed in this paper has a certain effect and can effectively improve the minority clothing design effect.

scholarly journals Threats and Vulnerabilities to IoT End Devices Architecture and suggested remedies

2020 ◽  
Vol 8 (6) ◽  
pp. 5712-5718
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Data Processing ◽  
Trusted Computing ◽  
External World ◽  
Security Threats ◽  
Iot Applications ◽  
Trusted Computing Base ◽  
Chain Of Trust ◽  
New Challenges

Due to decentralization of Internet of Things(IoT) applications and anything, anytime, anywhere connectivity has increased burden of data processing and decision making at IoT end devices. This overhead initiated new bugs and vulnerabilities thus security threats are emerging and presenting new challenges on these end devices. IoT End Devices rely on Trusted Execution Environments (TEEs) by implementing Root of trust (RoT) as soon as power is on thus forming Chain of trust (CoT) to ensure authenticity, integrity and confidentiality of every bit and byte of Trusted Computing Base (TCB) but due to un-trusted external world connectivity and security flaws such as Spectre and meltdown vulnerabilities present in the TCB of TEE has made CoT unstable and whole TEE are being misutilized. This paper suggests remedial solutions for the threats arising due to bugs and vulnerabilities present in the different components of TCB so as to ensure the stable CoT resulting into robust TEE.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

Sign in / Sign up

Export Citation Format

Share Document