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Special Issue "Geo-Distributed Big Data Analytics in Sensor Networks"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Gianvito Pio
E-Mail Website
Guest Editor
Department of Computer Science, University of Bari Aldo Moro (Italy)
Interests: Blockchain technologies; Data Mining and Knowledge Discovery; Bioinformatics; Social Network Analysis; Sensor networks; Clustering/Co-Clustering and Multi-relational Data Mining; Big Data Analytics
Dr. Roberto Corizzo
E-Mail Website
Guest Editor
Department of Computer Science, American University (Washington D.C.), USA
Interests: Geo-distributed Data Analysis; Predictive Modeling; Forecasting; Big Data Analytics; High-Performance Computing; Data Mining; Feature Extraction; Anomaly Detection; Clustering
Prof. Michelangelo Ceci
E-Mail Website
Guest Editor
Department of Computer Science, University of Bari Aldo Moro (Italy)
Interests: Machine learning; Data Mining and Knowledge Discovery; Web mining; Spatio-Temporal Data Mining; Big Data Analytics

Special Issue Information

Dear Colleagues,

The rising availability of geodistributed data streams from sensor networks presents great opportunities for the adoption of data analytics methods to extract valuable knowledge in a variety of real-world domains and applications. However, this type of data presents multiple challenges, including: i) missing values, due to possible sensor faults and communication issues; ii) outliers and anomalies, due to measurement errors; iii) temporal and spatial correlation and autocorrelation phenomena; iv) different (and asynchronous) time granularities. Specifically, the spatial proximity of geodistributed nodes may require the adoption of specific techniques (e.g., feature extraction and embedding methods, graph-based modeling techniques) to leverage the spatial autocorrelation induced by their proximity and to obtain high-quality models, for both descriptive and predictive tasks. Finally, data generated in sensor networks present a time-evolving nature, which requires methods and models that are capable of detecting and handling concept drift phenomena, and to dynamically adapt to changes in the observed data distribution.

This Special Issue will publish original research, reviews, and applications of methods for Geo-Distributed Big Data Analytics in Sensor Networks. The Special Issue areas of interest include but are not limited to the following topics:

  • Clustering and summarization for streams of sensor data;
  • Spatiotemporal data mining and knowledge discovery from streams of sensor data;
  • Preprocessing techniques for streams of sensor data;
  • Predictive modeling and forecasting on time series data;
  • Anomaly detection and data repair methods for streams of sensor data;
  • Adaptive models and algorithms for evolving data streams;
  • Edge computing methods;
  • High-performance computing for big data analytics in sensor networks;
  • Active learning approaches for sensor data;
  • Metalearning and transfer learning approaches for streams of sensor data;
  • Change point detection and concept drift detection in sensor data;
  • Feature selection for high-dimensional data domains;
  • Feature extraction for complex data streams;
  • Deep learning architectures and methods for sensor data analysis;
  • Explainable AI techniques for sensor data;
  • Data fusion approaches for feature-rich data analytics;
  • Data monitoring and tracking in sensor networks;
  • Correlation extraction and modeling with sensor data;
  • Smart cities and smart grid applications;
  • Cybersecurity applications, including intrusion detection in network data;
  • Blockchain technologies for sensor network data.

Prof. Michelangelo Ceci
Dr. Gianvito Pio
Dr. Roberto Corizzo
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 papers will be 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 2200 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

  • Data mining and knowledge discovery
  • Spatiotemporal data mining
  • Big data analytics
  • Stream data mining
  • Time series analysis
  • Predictive modeling
  • Sensor networks
  • Blockchain

Published Papers (2 papers)

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Research

Open AccessArticle
Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks
Sensors 2021, 21(9), 3196; https://doi.org/10.3390/s21093196 - 04 May 2021
Viewed by 309
Abstract
Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic [...] Read more.
Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution. Full article
(This article belongs to the Special Issue Geo-Distributed Big Data Analytics in Sensor Networks)
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Open AccessCommunication
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks
Sensors 2021, 21(9), 3030; https://doi.org/10.3390/s21093030 - 26 Apr 2021
Viewed by 328
Abstract
Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some [...] Read more.
Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors. Full article
(This article belongs to the Special Issue Geo-Distributed Big Data Analytics in Sensor Networks)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Towards detecting pneumonia progression in COVID-19 patients by monitoring sleep disturbance using data streams of non-invasive sensor networks
Authors: Ace Dimitrievski; Eftim Zdravevski; Petre Lameski; María Villasana; Ivan Miguel Pires; Nuno M. Garcia; Francisco Flórez-Revuelta; Vladimir Trajkovik
Affiliation: Faculty of Computer Science and Engineering, Ss.Cyril and Methodius University, Skopje, Macedonia
Abstract: COVID-19 caused pneumonia is a severe health risk that sometimes leads to fatal outcomes. Due to medical care systems’ constraints, technology solutions should be applied to diagnose, monitor, and alert the disease progress for patients receiving care at home. Some sleep disturbances such as obstructive sleep apnea syndrome can increase the risk for COVID-19 patients. This paper proposes an approach to evaluate the patients’ sleep quality, aiming to detect sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe the non-invasive sensor network used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model for detecting possible COVID-19 related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

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