scholarly journals Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications

2021 ◽  
Vol 7 ◽  
pp. e500
Author(s):  
Mina Younan ◽  
Essam H. Houssein ◽  
Mohamed Elhoseny ◽  
Abd El-mageid Ali
Keyword(s):  
Time Series ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
The Internet ◽  
Fusion Model ◽  
Time Data ◽  
Iot Applications ◽  
Real Time Data ◽  
Smart Things

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.

Real-time data analytics in healthcare using the Internet of Things

2020 ◽  
pp. 37-50
Author(s):  
Satya Narayan Sahu ◽  
Maheswata Moharana ◽  
Purna Chandra Prusti ◽  
Shanta Chakrabarty ◽  
Fahmida Khan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
The Internet ◽  
Time Data ◽  
Real Time Data ◽  
The Internet Of Things

scholarly journals Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

2020 ◽  
Vol 12 (12) ◽  
pp. 224
Author(s):  
Irene Niyonambaza ◽  
Marco Zennaro ◽  
Alfred Uwitonze
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Real Time ◽  
Short Term Memory ◽  
Structure Design ◽  
Predictive Maintenance ◽  
Mechanical Equipment ◽  
Time Data ◽  
Real Time Data ◽  
High Level

The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.

Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

2021 ◽  
Author(s):  
Saurabh Shukla ◽  
Mohd. Fadzil Hassan ◽  
Duc Chung Tran ◽  
Rehan Akbar ◽  
Irving Vitra Paputungan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Literature Review ◽  
Real Time ◽  
Systematic Literature Review ◽  
Data Transmission ◽  
Time Data ◽  
Real Time Data

Secure Real-time Data Access Using Two-Factor Authentication Scheme for the Internet of Drones

2021 ◽  
Author(s):  
Haqi Khalid ◽  
Shaiful Jahari Hashim ◽  
Sharifah Mumtazah Syed Ahamed ◽  
Fazirulhisyam Hashim ◽  
Muhammad Akmal Chaudhary
Keyword(s):  
Real Time ◽  
Data Access ◽  
Authentication Scheme ◽  
The Internet ◽  
Time Data ◽  
Real Time Data

How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

2021 ◽  
Vol 13 (4) ◽  
pp. 64-74
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Semantic Web ◽  
Industry 4.0 ◽  
The Internet ◽  
Time Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Real Time Data ◽  
The Internet Of Things

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

scholarly journals Analisa Real-Time Data log honeypot menggunakan Algoritma K-Means pada serangan Distributed Denial of Service

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 541
Author(s):  
Denni Septian Hermawan ◽  
Syaifuddin Syaifuddin ◽  
Diah Risqiwati
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Time Data ◽  
Local Networks ◽  
Real Time Data ◽  
Internet Networks ◽  
Data Log

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

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.

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