sensors-logo

Journal Browser

Journal Browser

Special Issue "Sensor Networks: Physical and Social Sensing in the IoT"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. Suparna De
Website
Guest Editor
Department of Digital Technologies, West Downs Campus, University of Winchester, Winchester, SO22 5HT, UK
Interests: Internet of Things; data analytics; social computing; semantics
Special Issues and Collections in MDPI journals
Prof. Dr. Klaus Moessner
Website
Guest Editor
Technische Universität Chemnitz, Str. der Nationen 62, 09111 Chemnitz, Germany
Interests: context and Situation awareness; sensor and actuator network; wireless communication systems

Special Issue Information

Dear Colleagues,

Advances made in the Internet of Things and other disruptive technological trends such as Big Data analytics and edge computing are contributing enabling solutions to the numerous challenges affecting modern communities. With Gartner reporting 14.2 billion devices in 2019 and, according to some reports, a projected 75 billion IoT devices that will be deployed by 2025 in areas like environment monitoring, smart agriculture, smart healthcare or smart cites, one could think that most issues are already resolved. However, there remain practical challenges in large-scale and rapid deployment of sensors for diverse applications, such as siting optimization methods and participant recruitment and incentive mechanisms. On a higher level, the deluge of data sources that drive the IoT phenomenon grows every day with the rise of smartphone-enabled citizen sensing data on social networks and personal health devices, as well as with increasing connectedness, be it in the transport, logistics, utilities, or manufacturing domains, this range and complexity of the available data calls for even more advanced data processing, mining and fusion methods than those already applied.

Dr. Suparna De
Prof. Dr. Klaus Moessner
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 2000 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

  • Rapid deployment of sensor networks
  • Context-awareness for smart IoT environments
  • Cloud virtual sensors in IoT
  • Virtual sensors modelling using neural networks and/or deep learning
  • Crowd sensing and related issues for IoT applications
  • Techniques for spatio-temporal big data analysis of IoT datasets
  • Large-scale IoT data fusion techniques with computational intelligence
  • Multi-sensor fusion approaches
  • Pattern derivation through visualization
  • Correlation between physical and social data streams
  • Applications of IoT sensor networks, including analytics and visualization
  • Applications based on heterogeneous data in IoT networks, such as eHealth, traffic, infrastructures, environment monitoring, etc.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
Sensors 2020, 20(20), 5895; https://doi.org/10.3390/s20205895 - 18 Oct 2020
Abstract
The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. [...] Read more.
The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks. Full article
(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)
Show Figures

Figure 1

Back to TopTop