scholarly journals Music Intelligent Push Play and Data Analysis System Based on 5G Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng Chen ◽  
Tien-Shou Huang ◽  
Jui-Chan Huang ◽  
Chi-Hung Shih ◽  
Yun Du
Keyword(s):  
Data Analysis ◽  
Internet Of Things ◽  
Rapid Development ◽  
Data Management System ◽  
The Internet ◽  
Dissimilarity Matrix ◽  
Network Information ◽  
Analysis System ◽  
Data Analysis System ◽  
The Internet Of Things

With the rapid development of information science today, multifunctional and intelligent applications have gradually become the focus of attention. In the data management system, the first consideration is the reliability of the data source, followed by the intelligent processing after the data are collected. Due to the upgrade of the Internet to the Internet of Things, the way of network information transmission has also become a problem that people need to think about. The transmission mode of network information services will be converted from the passive transmission of information by traditional servers to the form of actively pushing information. The application of intelligent push technology in the field of the Internet of Things is a prominent and important direction in the development of the Internet of Things. This article mainly introduces the research on the intelligent music push and data analysis system based on the 5G Internet of Things, with the intention of providing some ideas and directions for the research of the music intelligent push and play and data analysis system. This paper proposes a research method for music intelligent push playback and data analysis system based on 5G Internet of Things, including current intelligent push related technologies, music evaluation matrix, user dissimilarity matrix, and music feature similarity calculation. The experimental results in this paper show that with the increase in the number of users, the accuracy of the recommended results of the system under the Hadoop framework gradually stabilizes, eventually reaching 91.2%.

scholarly journals Data Analysis as a Service: An Infrastructure for Storing and Analyzing the Internet of Things

2015 ◽  
pp. 161-169 ◽  
Author(s):  
Martin Lehmann ◽  
Andreas Biørn-Hansen ◽  
Gheorghita Ghinea ◽  
Tor-Morten Grønli ◽  
Muhammad Younas
Keyword(s):  
Data Analysis ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things

Machine learning algorithms for smart data analysis in the Internet of things: an overview

2021 ◽  
pp. 307-327
Author(s):  
Mohammed H. Alsharif ◽  
Anabi Hilary Kelechi ◽  
Imran Khan ◽  
Mahmoud A. Albreem ◽  
Abu Jahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Smart Data ◽  
The Internet Of Things

Data Analysis Using the Internet: the World Wide Web Scanning Probe Microscopy Data Analysis System

The Analyst ◽  
1997 ◽  
Vol 122 (10) ◽  
pp. 1001-1006 ◽  
Author(s):  
Philip M. Williams ◽  
Martyn C. Davies ◽  
Clive J. Roberts ◽  
Saul J. B. Tendler
Keyword(s):  
Data Analysis ◽  
World Wide Web ◽  
Scanning Probe Microscopy ◽  
World Wide ◽  
Scanning Probe ◽  
The Internet ◽  
The World ◽  
Analysis System ◽  
Microscopy Data ◽  
Data Analysis System

Detection of False Data Injection Attack in the Internet of Things

2012 ◽  
Vol 452-453 ◽  
pp. 932-936
Author(s):  
Xiang Dong Hu ◽  
Peng Qin Yu
Keyword(s):  
Internet Of Things ◽  
Rapid Development ◽  
Sensor Nodes ◽  
The Internet ◽  
Malicious Nodes ◽  
High Detection ◽  
Detection Ratio ◽  
False Data Injection Attack ◽  
The Internet Of Things ◽  
Ubiquitous Network

With the rapid development of ubiquitous network and its applications, the key technologies of the Internet of things are actively researched all over the world. The Internet of things has tremendous attraction for adversaries, and it is easily attacked due to poor resource and non-perfect distribution of sensor nodes, then false data maybe be injected into network. Security is one of the most important demands for applications in the Internet of things, an algorithm of malicious nodes detection is proposed to protect the network from destruction based on weighted confidence filter, namely, the cluster heads take charge of collecting messages from nodes and computing their average of confidence in cluster-based network, then they aggregate data from nodes with higher confidence than average and ignore the others, they update confidence of each node by comparing the aggregation value and the received data, and regard it as the weight of exactness of message from node. A sensor node is judged to be a malicious one if its weight is lower than the set threshold. The simulation results show that the algorithm can detect malicious nodes with high detection ratio, low false alarm ratio and outstanding scalability.

scholarly journals Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Rapid Development ◽  
The Internet ◽  
Space Partitioning ◽  
Data Space ◽  
Index Building ◽  
Representative Skyline ◽  
The Internet Of Things ◽  
Grid Based

A rapid development in wireless communication and radio frequency technology has enabled the Internet of Things (IoT) to enter every aspect of our life. However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-kquery processing can be applied to facilitate this task. The top-kqueries retrievektuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-kqueries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-kqueries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. Experimental results show that our method improves the index building time compared to the existing state-of-the-art methods.

scholarly journals Perovskite indoor photovoltaics: opportunity and challenges

2021 ◽  
Vol 12 (36) ◽  
pp. 11936-11954
Author(s):  
Kai-Li Wang ◽  
Yu-Hang Zhou ◽  
Yan-Hui Lou ◽  
Zhao-Kui Wang
Keyword(s):  
Internet Of Things ◽  
Rapid Development ◽  
The Internet ◽  
The Internet Of Things

With the rapid development of the Internet of Things (IoTs), photovoltaics (PVs) has a vast market supply gap of billion dollars.

scholarly journals Upgrade Path and Strategy of Logistics Industry Cluster Based on the Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Du
Keyword(s):  
Internet Of Things ◽  
Evaluation Method ◽  
Service Industry ◽  
Rapid Development ◽  
The Internet ◽  
Industry Cluster ◽  
Analytic Hierarchy ◽  
Logistics Industry ◽  
Expert Evaluation Method ◽  
The Internet Of Things

As the economy grows rapidly and IoT technology advances rapidly, the logistics industry as a service industry is growing rapidly around the world. The logistics industry, meanwhile, is the one that can best play the role of IoT. The rapid development of the logistics industry has brought great competition challenges to the logistics industry. To solve the competitive problems of the logistics industry cluster, this article introduces the research on the upgrade path and strategy of the logistics industry cluster based on the Internet of Things and uses the analytic hierarchy process, investigation method, and expert evaluation method to build the IoT technology information model and logistics cost. According to the established optimization model, the following are proposed: analyzing the problems existing in the logistics industry cluster, giving an upgrade path from the four aspects of manufacturing, technology, structure, and service, and giving specific strategic suggestions from the aspects of talents and enterprises. The accuracy rate of current analysis is as high as 90%, and the implementation rate of upgrade paths and strategy recommendations is as high as 95%.

scholarly journals IoT-Fog-Cloud system architecture for big data analysis and cybersecurity systems: a review of fog computing, the introduction to the Internet of Things audit

2020 ◽  
Author(s):  
D. R. Kolisnyk ◽  
◽  
K. S. Misevych ◽  
S. V. Kovalenko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
System Architecture ◽  
Fog Computing ◽  
Big Data Analysis ◽  
Future Research ◽  
The Internet ◽  
Security Issues ◽  
The Internet Of Things

The article considers the issues of system architecture IoT-Fog-Cloud, considers the interaction between the three levels of IoT, Fog and Cloud for the effective implementation of programs for big data analysis and cybersecurity. The article also discusses security issues, solutions and directions for future research in the field of the Internet of Things and nebulous computing.

scholarly journals About ensuring security in the field of the internet of things

2021 ◽  
Vol 6 (3) ◽  
pp. 33-39
Author(s):  
Oleg O. Viushchenko ◽  
◽  
Maria A. Maslova ◽  
Keyword(s):  
Internet Of Things ◽  
Social Life ◽  
Rapid Development ◽  
Business Environment ◽  
The Internet ◽  
Widespread Adoption ◽  
The World ◽  
Huge Impact ◽  
The Internet Of Things

The rapid development of the Internet of Things (IoT) and its capabilities in terms of services have made it one of the fastest-growing technologies that have a huge impact on both social life and the business environment of a person. The widespread adoption of connected devices in the IoT has created a huge demand for reliable security in response to the growing demand of billions of connected devices and services around the world. But at the same time, the number of threats continues to grow every day, and attacks are increasing both in number and complexity. The number of attackers is also growing, and the tools they use are constantly being improved and becoming more effective. Therefore, it is necessary to constantly protect against threats and vulnerabilities for IoT. In this article, we will analyze the development of IoT, consider existing threats, attacks on IoT, as well as methods of protecting devices from threats and vulnerabilities for IoT.

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