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

2019 ◽  
Vol 9 (23) ◽  
pp. 5159 ◽  
Author(s):  
Shichang Xuan ◽  
Yibo Zhang ◽  
Hao Tang ◽  
Ilyong Chung ◽  
Wei Wang ◽  
...  

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.

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1231 ◽  
Author(s):  
Davide Pedrini ◽  
Mauro Migliardi ◽  
Carlo Ferrari ◽  
Alessio Merlo

Recently blockchain technology has been advocated as a solution fitting many different problems in several applicative fields; among these fields there is the Internet of Things (IoT) too. In this paper we show the most significant properties of a blockchain, how they suite the use case of a cryptocurrency and how they map onto the needs of IoT systems. We claim that a blockchain does not provide a significant advantage with respect to other database technologies in a field such as Internet of Things where computational power comes at a premium, energy is often scarce and storage scalability is a major challenge.


Author(s):  
Reema Abdulraziq ◽  
Muneer Bani Yassein ◽  
Shadi Aljawarneh

Big data refers to the huge amount of data that is being used in commercial, industrial and economic environments. There are three types of big data; structured, unstructured and semi-structured data. When it comes to discussions on big data, three major aspects that can be considered as its main dimensions are the volume, velocity, and variety of the data. This data is collected, analysed and checked for use by the end users. Cloud computing and the Internet of Things (IoT) are used to enable this huge amount of collected data to be stored and connected to the Internet. The time and the cost are reduced by means of these technologies, and in addition, they are able to accommodate this large amount of data regardless of its size. This chapter focuses on how big data, with the emergence of cloud computing and the Internet of Things (IOT), can be used via several applications and technologies.


2022 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Ernesto D. R. Santibanez Gonzalez

Studies concerning Big Data patents have been published; however, research investigating Big Data projects is scarce. Therefore, the objective of this study was to conduct an exploratory analysis of a patent database to collect information about the characteristics of registered patents related to Big Data projects. We searched for patents related to Big Data projects in the Espacenet database on January 10, 2021, and identified 109 records.. The textual analysis detected three word classes interpreted as (i) a direction to cloud computing, (ii) optimization of solutions, and (iii) storage and data sharing structures. Our results also revealed emerging technologies such as Blockchain and the Internet of Things, which are utilized in Big Data project solutions. This observation demonstrates the importance that has been given to solutions that facilitate decision-making in an increasingly data-driven context. As a contribution, we understand that this study endorses a group of researchers that has been dedicated to academic research on patent documents.


The initiation of the Internet of Things is the fundamental stimulus behind the current mechanical surprise. Web of things is the unavoidable aggregation of web related devices that assemble, look at and change the immense measure of colossal data at an incomparable rate. By creating and passing on sensible preprocessing frameworks, this goliath measurement of data can be transformed into performance information. The all-new IoT tremendous information development expects changes to be passed on to the present advances. The significance of preprocessing methodologies in the IoT enormous information situation has been discussed in this document and in addition to early IoT examines huge information preprocessing frameworks. Finally, a bright fresh parallel preprocessing system for IoT Big data has been suggested to transform the tough data into treasurable information so that enormous data examination of IoT performance can obtain complete recognition of this increasing growth


Author(s):  
Pasumpon Pandian A

The edge computing that is an efficient alternative of the cloud computing, for handling of the tasks that are time sensitive, has become has become very popular among a vast range of IOT based application especially in the industrial sides. The huge amount of information flow and the services requisition from the IOT has made the traditional cloud computing incompatible on the time of big data flow. So the paper proposes an enhanced edge model for the by incorporating the artificial intelligence along with the integration of caching to the edge for handling of the big data flow in the applications of the internet of things. The performance evaluation of the same in the network simulator 2 for enormous flow of task that are time sensitive , evinces that the proposed method has a minimized delay compared the traditional cloud computing models.


Author(s):  
Anna Smyshlyaeva ◽  
Kseniya Reznikova ◽  
Denis Savchenko

With the advent of the Industry 4.0 concept, the approach to production automation has fundamentally changed. The manufacturing industry is based on such modern technologies as the Internet of Things, Big Data, cloud computing, artificial intelligence and cyber-physical systems. These technologies have proven themselves not only in industry, but also in various other branches of life. In this paper, the authors consider the concept of cyber-physical systems – systems based on the interaction of physical processes with computational ones. The article presents a conceptual model of cyber-physical systems that displays its elements and their interaction. In cyber-physical systems, it represents five levels: physical, network, data storage, processing and analytics level, application level. Cyber-physical systems carry out their work using a basic set of technologies: the Internet of things, big data and cloud computing. Additional technologies are used depending on the purpose of the system. At the physical level, data is collected from physical devices. With the help of the Internet of Things at the network level, data is transferred to a data warehouse for further processing or processed almost immediately thanks to cloud computing. The amount of data in cyber-physical systems is enormous, so it is necessary to use big data technology and effective methods for processing and analyzing this data. The main feature of this technological complex is real-time operation. Despite the improvement in the quality of production and human life, cyber-physical systems have a number of disadvantages. The authors highlight the main problems of cyber-physical systems and promising areas of research for their development. Having solved the listed problems, cyber-physical systems will reach a qualitatively new level of utility. The paper also provides examples of the implementation of concepts such as a smart city, smart grid, smart manufacturing, smart house. These concepts are based on the principle of cyber-physical systems.


2020 ◽  
Author(s):  
Kateryna Nikitenko ◽  
◽  
Hanna Zhosan ◽  

Consulting agency PricewaterhouseCoopers has identified eight key technologies for the digital economy: the Internet of Things and artificial intelligence - the foundation for a new generation of digital resources; robotics, drones and 3D printers - devices that help transfer the capabilities of a computer to the material world; virtual reality is also augmented - technologies that combine the physical and digital worlds; blockchain and cloud computing - a new approach to basic business accounting operations. Analysis of recent research and publications. The topic of big data is still controversial, although it is being studied by specialists in various fields (economics, information technology, politics, and others). The influence of big data on social processes and business organization, in particular, was studied by Bill Franks, Victor Mayer-Schonberger, Kenneth Kukier, Eric Siegel, John Foreman and others. Publicistic materials prevail among domestic sources, but there is a lack of fundamental works in this direction. Purpose of the article. explore the essence and practical application of revolutionary digital technologies - cloud computing, big data and the Internet of things. The article identifies the advantages and disadvantages of cloud technologies, forms a model of cloud services, discusses technologies for working with big data, analyzes the results of a Tech Pro Research survey on the use of big data, and builds a technological ecosystem of the Internet of Things. Digitization is already a reality, and the penetration of the Internet and digital technologies into traditional industries has become one of the main trends in recent years and is taking place on a global scale, which allows us to talk about the digital transformation of all sectors of the economy, social life and the formation of a new economic structure - the digital economy. The practical use of digital economy technologies is a modern trend in the socio-economic life of a modern state, actively influences consumer behavior, manifests itself in mobility and the desire of companies for continuous improvement.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Zhang ◽  
Yu Huo ◽  
Qiang Ge ◽  
Yuxiang Ma ◽  
Qiqi Liu ◽  
...  

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.


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