Request-based, secured and energy-efficient (RBSEE) architecture for handling IoT big data

2018 ◽  
Vol 45 (2) ◽  
pp. 227-238 ◽  
Author(s):  
Mohd Abdul Ahad ◽  
Ranjit Biswas

The technological advancements in the field of computing are giving rise to the generation of gigantic volumes of data which are beyond the handling capabilities of the conventionally available tools, techniques and systems. These types of data are known as big data. Moreover with the emergence of Internet of Things (IoT), these types of data have increased in multiple folds in 7Vs (volume, variety, veracity, value, variability, velocity and visualisation). There are several techniques prevalent in today’s time for handling these types of huge data. Hadoop is one such open source framework which has emerged as a de facto technology for handling such huge datasets. In an IoT ecosystem, real-time handling of requests is an imperative requirement; however, Hadoop has certain limitations while handling these types of requests. In this article, we present an energy-efficient architecture for effective, secured and real-time handling of IoT big data. The proposed approach adopts atrain distributed system (ADS) to construct the core architecture. This study uses software-defined networking (SDN) framework for energy-efficient and optimal routing of data and requests from source to destination, and vice versa. Furthermore, to ensure secured handling of IoT big data, the proposed approach uses ‘Twofish’ cryptographic technique for encrypting the information captured by the sensors. Finally, the concept of ‘request-type’ identifying unit has been proposed. Instead of handling all the requests in an identical way, the proposed approach works by characterising the requests on the basis of certain criteria and parameters, which are identified here.

2015 ◽  
Vol 319 ◽  
pp. 92-112 ◽  
Author(s):  
Dawei Sun ◽  
Guangyan Zhang ◽  
Songlin Yang ◽  
Weimin Zheng ◽  
Samee U. Khan ◽  
...  

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.


Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra

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.


2019 ◽  
Vol 76 (7) ◽  
pp. 5175-5191 ◽  
Author(s):  
Li Tu ◽  
Shuai Liu ◽  
Yan Wang ◽  
Chi Zhang ◽  
Ping Li

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