Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Applied Data Science and Intelligence
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Applied Data Science and Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 15669

Special Issue Editors

Prof. Dr. Ansar Yasar

E-Mail Website
Guest Editor
Transportation Research Institute (IMOB), Hasselt University, 3500 Hasselt, Belgium
Interests: ITS; vehicular networks; agent-based modeling; modeling and simulation; transportation behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nafaa Jabeur

E-Mail Website
Guest Editor
Department of Computer Science, German University of Technology in Oman (GUtech), P.O. Box 1816, Athaibah, Muscat PC 130, Oman
Interests: wireless sensor networks; spatial data warehouses; cyber physical systems, internet of things; smart cities; multiagent systems; social networks; spatial data representation, processing, modeling, and visualization; web and mobile catography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michele Melchiori

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Brescia, Via Branze, 38 25123 Brescia, Italy
Interests: data science; smart cities; blockchain

Special Issue Information

Dear Colleagues,

Thanks to the continuous advances in miniaturization, mobile and pervasive computing, as well as networking technologies, huge amounts of data are being rapidly collected about any kind of event and object of interest. However, the existing processing and storage infrastructures are not always capable of handling the high flow of incoming data and generating the required content and services within optimized timeframes. In order to deal with these shortcomings, extensive research and development efforts are deploying emergent technologies (including Big Data, cloud computing, machine learning, Internet of Things, and blockchain) to create appropriate models, processes, storage spaces, and mechanisms for optimized and secure management of data.

This Special Issue solicits innovative contributions from academia and industry on the use of intelligent solutions to support the important field of applied data science. More specifically, solutions based, for example, on multi-agent systems, machine learning, deep learning, and bio-inspired approaches are particularly needed to support data gathering, cleaning, aggregation, storage, processing, querying, visualization, and analytics. They are also needed to enable smooth communications and collaborations between software and hardware entities within a wide range of applications, particularly those relying on IoT and cyberphysical systems (CPSs).

Prof. Dr. Ansar Yasar
Prof. Dr. Nafaa Jabeur
Prof. Dr. Michele Melchiori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Theories and models for data acquisition, storage, processing, and visualization
  • Intelligent data analytics
  • Data science as a service (DSaaS)
  • Intelligence as a service (IaaS)
  • Intelligent digital transformation
  • Intelligent data science for IoT and/or CPS
  • Intelligent edge and fog computing
  • Embedded data science Embedded intelligence Data science for secure IoT

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

20 pages, 4464 KiB  
Article
Hierarchical Analysis Process for Belief Management in Internet of Drones
by Hana Gharrad, Nafaâ Jabeur and Ansar Ul-Haque Yasar
Sensors 2022, 22(16), 6146; https://doi.org/10.3390/s22166146 - 17 Aug 2022
Viewed by 1262
Abstract
Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad [...] Read more.
Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad of innovative devices are being extensively deployed to collaboratively recognize and track events, objects, and activities of interest. A wide range of IoT-based approaches have focused on representing and managing shared information through formal operators for group awareness. However, despite their proven results, these approaches are still refrained by the inaccuracy of information being shared between the collaborating distributed entities. In order to address this issue, we propose in this paper a new belief-management-based model for a collaborative Internet of Drones (IoD). The proposed model allows drones to decide the most appropriate operators to apply in order to manage the uncertainty of perceived or received information in different situations. This model uses Hierarchical Analysis Process (AHP) with Subjective Logic (SL) to represent and combine opinions of different sources. We focus on purely collaborative drone networks where the group awareness will also be provided as service to collaborating entities. Full article
(This article belongs to the Special Issue Applied Data Science and Intelligence)
►▼ Show Figures

Figure 1

28 pages, 3327 KiB  
Article
Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation
by Francisco Javier Nieto, Unai Aguilera and Diego López-de-Ipiña
Sensors 2021, 21(18), 6063; https://doi.org/10.3390/s21186063 - 10 Sep 2021
Cited by 4 | Viewed by 2875
Abstract
Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several [...] Read more.
Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster. Full article
(This article belongs to the Special Issue Applied Data Science and Intelligence)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

17 pages, 2939 KiB  
Review
Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review
by Manzura Jorayeva, Akhan Akbulut, Cagatay Catal and Alok Mishra
Sensors 2022, 22(7), 2551; https://doi.org/10.3390/s22072551 - 26 Mar 2022
Cited by 19 | Viewed by 6882
Abstract
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction [...] Read more.
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field. Full article
(This article belongs to the Special Issue Applied Data Science and Intelligence)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

16 pages, 2083 KiB  
Systematic Review
Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry
by Xiang Cheng, Jun Kit Chaw, Kam Meng Goh, Tin Tin Ting, Shafrida Sahrani, Mohammad Nazir Ahmad, Rabiah Abdul Kadir and Mei Choo Ang
Sensors 2022, 22(17), 6321; https://doi.org/10.3390/s22176321 - 23 Aug 2022
Cited by 12 | Viewed by 3023
Abstract
The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data [...] Read more.
The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement. Full article
(This article belongs to the Special Issue Applied Data Science and Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop