Utilization of the Internet of Things for Real-time Data Collection and Storage of Big Data as it Relates to Improved Demand Response

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.

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Trends and Technologies in Big Data Processing

Advances in Computational Intelligence and Robotics - Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ◽

10.4018/978-1-7998-3038-2.ch002 ◽

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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Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain

Applied Sciences ◽

10.3390/app9235159 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5159 ◽

Cited By ~ 8

Author(s):

Shichang Xuan ◽

Yibo Zhang ◽

Hao Tang ◽

Ilyong Chung ◽

Wei Wang ◽

...

Keyword(s):

Cloud Computing ◽

Big Data ◽

Internet Of Things ◽

Data Sharing ◽

Privacy Protection ◽

High Performance ◽

The Internet ◽

Blockchain Technology ◽

And Storage ◽

The Internet Of Things

With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system.

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IoT4Emergency

ACM SIGSOFT Software Engineering Notes ◽

10.1145/3437479.3437489 ◽

2021 ◽

Vol 46 (1) ◽

pp. 33-36

Author(s):

Julie Dugdale ◽

Mahyar T. Moghaddam ◽

Henry Muccini

Keyword(s):

Internet Of Things ◽

Emergency Management ◽

Real Time ◽

International Workshop ◽

The Internet ◽

Time Data ◽

Time Requirements ◽

Safety Systems ◽

Crises Management ◽

The Internet Of Things

The increasing natural and man-induced disasters such as res, earthquakes, oods, hurricanes, overcrowding, or pandemic viruses endanger human lives. Hence, designing infrastructures to handle those possible crises has become an ever-increasing need. The Internet of Things (IoT) has changed our approach to safety systems by connecting sensors and providing real-time data to managers, rescuers, and endangered people. IoT systems can monitor and react to progressive disasters, people's movements and their behavioral patterns. The community faces challenges in using IoT for crises management: i) how to take advantage of technological advancements and deal with IoT resources installation issues? ii) what environmental contexts should be considered while designing IoT-based emergency handling systems? iii) how should system design comply with various levels of real-time requirements? This paper reports on the results of the First International Workshop on Internet of Things for Emergency Management (IoT4Emergency 2020), which speci cally focuses on challenges and envisioned solutions in using smart connected systems to handle disasters.

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

PeerJ Computer Science ◽

10.7717/peerj-cs.500 ◽

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.

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Real-Time Data Collection and Processing of Utility Customer’s Power Usage for Improved Demand Response Control

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII - Lecture Notes in Computer Science ◽

10.1007/978-3-662-53416-8_4 ◽

2016 ◽

pp. 48-63

Author(s):

Shawyun Sariri ◽

Volker Schwarzer ◽

Dominik P. H. Kalisch ◽

Michael Angelo ◽

Reza Ghorbani

Keyword(s):

Data Collection ◽

Real Time ◽

Demand Response ◽

Time Data ◽

Response Control ◽

Real Time Data

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Challenges and Solutions of Big Data and IoT

Advances in Data Mining and Database Management - Handbook of Research on Big Data and the IoT ◽

10.4018/978-1-5225-7432-3.ch015 ◽

2019 ◽

pp. 264-272

Author(s):

Jayashree K. ◽

Abirami R. ◽

Rajeswari P.

Keyword(s):

Decision Making ◽

Big Data ◽

Data Collection ◽

Internet Of Things ◽

Data Processing ◽

The Internet ◽

Huge Amount ◽

Collection Data ◽

Different Sources ◽

The Internet Of Things

The successful development of big data and the internet of things (IoT) is increasing and influencing all areas of technologies and businesses. The rapid increase of more devices that are connected to IoT from which enormous amount of data are consumed indicates the way how big data is related with IoT. Since huge amount of data are obtained from different sources, analysis of these data involves much of processing at each and every level to extract knowledge for decision making process. To manage big data in a continuous network that keeps expanding leads to few issues related to data collection, data processing, analytics, and security. To address these issues, certain solution using bigdata approach in IoT are examined. Combining these two areas provides several opportunities developing new systems and identify advanced techniques to solve challenges on big data and IoT.

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Big Data Analytics and Its Applications in IoT

Advances in Web Technologies and Engineering - Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing ◽

10.4018/978-1-7998-3111-2.ch009 ◽

2021 ◽

pp. 146-158

Author(s):

Shaila S. G. ◽

Bhuvana D. S. ◽

Monish L.

Keyword(s):

Big Data ◽

Internet Of Things ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

The Internet ◽

Iot Applications ◽

Maximum Benefit ◽

The Internet Of Things ◽

Achieve Maximum Benefit

Big data and the internet of things (IoT) are two major ruling domains in today's world. It is observed that there are 2.5 quintillion bytes of data created each day. Big data defines a very huge amount of data in terms of both structured and unstructured formats. Business intelligence and other application domains that have high information density use big data analytics to make predictions and better decisions to improve the business. Big data analytics is used to analyze a high range of data at a time. In general, big data and IoT were built on different technologies; however, over a period of time, both of them are interlinked to build a better world. Companies are not able to achieve maximum benefit, just because the data produced by the applications are not utilized and analyzed effectively as there is a shortage of big data analysts. For real-time IoT applications, synchronization among hardware, programming, and interfacing is needed to the greater extent. The chapter discusses about IoT and big data, relation between them, importance of big data analytics in IoT applications.

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