Real-time data reduction at the network edge of Internet-of-Things systems

2015 ◽  
Author(s):  
Apostolos Papageorgiou ◽  
Bin Cheng ◽  
Erno Kovacs
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data

Machine Learning for Real-time Data Reduction in Cloud of Things

2020 ◽  
Author(s):  
Atheer Alahmed ◽  
Amal Alrasheedi ◽  
Maha Alharbi ◽  
Norah Alrebdi ◽  
Marwan Aleasa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data

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.

scholarly journals An adaptive framework for real-time data reduction in AMI

2019 ◽  
Vol 31 (3) ◽  
pp. 392-402 ◽  
Author(s):  
Marwa F. Mohamed ◽  
Abd El-Rahman Shabayek ◽  
Mahmoud El-Gayyar ◽  
Hamed Nassar
Keyword(s):  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data ◽  
Adaptive Framework

A Block Chain Approach for Privacy Preserving and Data Security in Internet of Things

2020 ◽  
Vol 17 (9) ◽  
pp. 3979-3982
Author(s):  
N. Harish Kumar ◽  
G. Deepak
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Information Storage ◽  
Entire System ◽  
Time Data ◽  
Real Time Data ◽  
Block Chain

Internet of Things has been increasing its usage and recognition in vast sectors like Defence, Business, Industries, and Hospitals. The data disruption is strictly unacceptable in a number of these sectors because it could end up in serious Loss or Damages to the entire system. As of now, IOT is using a central cloud storage system for information storage and transactions. However, some examples already verified that Central cloud storage information might be hacked and changed by the specialists. This paper presents an IoT system having localized block chain storage which works on real time data and manipulates with narrowness of data interruption and modification and its recovery.

scholarly journals Internet of Things based Customer Relationship Management – A Research Perspective

2018 ◽  
Vol 7 (2.7) ◽  
pp. 444 ◽  
Author(s):  
Samir Yerpude ◽  
Dr Tarun Kumar Singhal
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Mobile Internet ◽  
Customer Relationship ◽  
Time Data ◽  
Crm System ◽  
Real Time Data ◽  
The Impact

Objectives: To study the impact of Internet of things (IoT) on the Customer Relationship Management process and evaluate the benefits in terms of customer satisfaction and customer retention. Methods: An extensive literature review was conducting wherein the constructs of CRM and IoT are studied. Various preliminary information on IoT and CRM system along with the components of Digital enablers have been evaluated. References from research papers, journals, Internet sites, statistical data sites and books were used to collate the relevant content on the subject. The study of all the relevant scenarios where there is a possible impact of IoT origin real time data on CRM was undertaken. Findings: Customer demands are continuously evolving and it is very relevant for all the organizations to align and keep pace with the change. Organizations need to be customer centric and agile to the changing market scenarios. Evaluation of the trends in mobile internet vs desktop internet was also conducted to validate the findings. Application: The usage of real time data emerging out of the IoT landscape has become a reality with the data transmitted over the Internet and consumed by the CRM system. It improves the control on the customer relationship function helping the organizations to operate within healthy and sustained profit  

scholarly journals Design of smart agriculture based on big data and Internet of things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091706 ◽  
Author(s):  
Chunling Li ◽  
Ben Niu
Keyword(s):  
Data Mining ◽  
Information Processing ◽  
Internet Of Things ◽  
Real Time ◽  
Data Communication ◽  
Large Data ◽  
Time Data ◽  
Real Time Data ◽  
Smart Agriculture ◽  
Internet Of Things Technology

With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

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