Sensor Based Big Data Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 868

Special Issue Editor


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Guest Editor
Independent Researcher, Pedaso, 63827 Marche, Italy
Interests: monitoring systems; sensors; wireless sensors network; data analysis; numerical simulations; structural health monitoring systems; energy monitoring systems; renewable energies; machine learning; net-zero-energy buildings; artificial intelligence; smart things; smart buildings; smart cities
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Special Issue Information

Dear Colleagues,

Sensor data analysis, together with the design of novel and reliable modelling and simulation techniques, allow for the development of a smart monitoring system that can be applied across a large range of applications, such as buildings, infrastructure, smart cities, precision agriculture, industrial components, and autonomous mobility, etc., for resilience, decision-making, proficient implementation of operational control, maintenance strategies, and sustainability in various sectors.

Sensor data analysis includes cutting-edge sensor technologies, data acquisition systems, data analysis techniques, machine learning (ML), and artificial intelligence (AI) solutions. Despite recent successful examples, more research is needed to collect, store, and manage big datasets from robust sensor networks and data acquisition systems, as well as to create reliable and efficient algorithms for data analysis and trustful numerical simulations. This Special Issue “Sensor Based Big Data Analysis” focuses on recent advances in data analysis methods for smart monitoring systems and their applications and is calling for high-impact submissions in the following areas:

  • Data analysis methods;
  • Machine learning methods;
  • Modelling of sensor networks (wired and wireless);
  • Real-time data collection with dynamic inputs;
  • Time synchronization;
  • Simulation techniques;
  • Embedded machine learning methods and applications;
  • Novel applications of smart monitoring systems from all areas;
  • All other related areas.

Dr. Daniela Isidori
Guest Editor

Manuscript Submission Information

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Keywords

  • data analysis
  • sensor networks
  • machine learning
  • artificial intelligence
  • wireless sensors
  • numerical model
  • simulation techniques
  • monitoring system

Published Papers (2 papers)

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Research

22 pages, 8354 KiB  
Article
Driving Reality vs. Simulator: Data Distinctions
by Natalia Piaseczna, Rafał Doniec, Szymon Sieciński, Klaudia Barańska, Marek Jędrychowski and Marcin Grzegorzek
Electronics 2024, 13(14), 2708; https://doi.org/10.3390/electronics13142708 - 10 Jul 2024
Viewed by 273
Abstract
As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the [...] Read more.
As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology. Full article
(This article belongs to the Special Issue Sensor Based Big Data Analysis)
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18 pages, 1206 KiB  
Article
A Reference Paper Collection System Using Web Scraping
by Inzali Naing, Soe Thandar Aung, Khaing Hsu Wai and Nobuo Funabiki
Electronics 2024, 13(14), 2700; https://doi.org/10.3390/electronics13142700 - 10 Jul 2024
Viewed by 229
Abstract
Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly [...] Read more.
Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly by the user. Even if they are accessible, their effectiveness needs to be checked manually. In this paper, we propose a reference paper collection system using web scraping to automate paper collections from websites. This system can collect or monitor data from the Internet, which is considered as the environment, using Selenium, a popular web scraping software, as the sensor; this examines the similarity against the search target by comparing the keywords using the Bert model. The Bert model is a deep learning model for natural language processing (NLP) that can understand context by analyzing the relationships between words in a sentence bidirectionally. The Python Flask is adopted at the web application server, where Angular is used for data presentations. For the evaluation, we measured the performance, investigated the accuracy, and asked members of our laboratory to use the proposed method and provide their feedback. Their results confirm the method’s effectiveness. Full article
(This article belongs to the Special Issue Sensor Based Big Data Analysis)
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