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Electronics
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Sensor Network Technologies and Applications with Wireless Sensor Devices
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Sensor Network Technologies and Applications with Wireless Sensor Devices

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 11333

Special Issue Editors

Dr. JungYoon Kim

E-Mail Website1 Website2
Guest Editor
Department of Game Media, College of Future Industry, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea
Interests: AR and VR; game design; game therapy; application design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. SangHyun Seo

E-Mail Website1 Website2
Guest Editor
School of Computer Art, Chung-Ang University, (17546) 4726, Seodongdae-ro, Daedeok-myon, Anseong-si, Gyeonggi-do, Korea
Interests: non-photorealistic rendering and animation; color science; image & movie analysis; visual perception-based rendering virtual reality and augmented reality; sensor based physical computing
Special Issues, Collections and Topics in MDPI journals
Dr. Sung Jong Eun

E-Mail Website
Guest Editor
Health IT Research Center, Gachon University Gil Hospital, 24, Namdong-daero 774beon-gil, Namdong-gu, Incheon, Korea
Interests: sensor-based medical image; IoT-based application; imaging engineering; sensor-based artificial intelligence

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) have significantly influenced our way of living, which becomes pervasive, more relevant, and more accessible. Low-power wireless sensor devices have become the focal point for the Internet of Things (IoT), with areas of applications ranging from environmental monitoring to power management, healthcare applications, industrial automations, structural health monitoring, area monitoring, wind generator operation, home security and automation, and threat detection. The appropriate development of these WSN applications is supplemented by the use of reliable data communication from sensors or sensor networks, which requires the use of sensor data fusion, real-time data processing, and advanced signal processing techniques in order to guarantee a reliable system.

The Special Issue on “Sensor Network Technologies and Applications with Wireless Sensor Devices” is focused on bringing together the broad applications of WSNs. As WSNs are deployed in small or very large numbers to bodies, homes, cars, highways, buildings, cities, and infrastructures, as well as for monitoring and control, new challenges and opportunities arise. Further, it combines with wireless sensor devices to approach conversion for the hardware and software area. This Special Issue aims to provide a high-quality and timely avenue for educators, researchers, engineers, and distinguished scholars to share related ideas, discuss the recent developments, and address the challenges pertaining to the applications of WSNs, as well as provide a new way of education method contents with IoT-based devices and applications.

Original and research articles are solicited in all aspects including theoretical studies, practical applications, high-quality and state-of-the-art ideas and results, new technology, and experimental prototypes.

Prof. Dr. JungYoon Kim
Prof. Dr. SangHyun Seo
Dr. Sung Jong Eun
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. Electronics 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 2400 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

  • smart sensors
  • wireless sensor devices
  • IoT devices
  • IoT development and applications
  • IoT educational devices
  • IoT educational contents
  • signal processing, data fusion and deep learning in sensor systems
  • data pre-processing and data normalization
  • sensor fault detection
  • data-driven algorithms
  • wireless sensor networks
  • environmental monitoring and control
  • data-driven algorithms
  • sensor network design for continuous monitoring
  • security and privacy in WSN

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Published Papers (3 papers)

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Research

19 pages, 383 KiB  
Article
IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection
by Laura Vigoya, Diego Fernandez, Victor Carneiro and Francisco J. Nóvoa
Electronics 2021, 10(22), 2857; https://doi.org/10.3390/electronics10222857 - 19 Nov 2021
Cited by 10 | Viewed by 4436
Abstract
With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently [...] Read more.
With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection. Full article
(This article belongs to the Special Issue Sensor Network Technologies and Applications with Wireless Sensor Devices)
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17 pages, 1288 KiB  
Article
Multi-Objective Function-Based Node-Disjoint Multipath Routing for Mobile Ad Hoc Networks
by Bhanumathi Velusamy, Kalaivanan Karunanithy, Damien Sauveron, Raja Naeem Akram and Jaehyuk Cho
Electronics 2021, 10(15), 1781; https://doi.org/10.3390/electronics10151781 - 25 Jul 2021
Cited by 10 | Viewed by 2440
Abstract
The main goal is to find multiple node-disjoint paths that meet the multi-objective optimization problem in terms of energy consumption minimization and network lifetime improvement. Due to the battery-dependent nodes in mobile ad hoc networks, the performance of the network will degrade. Hence, [...] Read more.
The main goal is to find multiple node-disjoint paths that meet the multi-objective optimization problem in terms of energy consumption minimization and network lifetime improvement. Due to the battery-dependent nodes in mobile ad hoc networks, the performance of the network will degrade. Hence, it is necessary to choose multiple optimal node-disjoint paths between source and destination for data transfer. Additionally, it improves the Quality of Service (QoS) of wireless networks. Multi-objective function is used to select a path such that it gives an optimum result based on the energy consumption, hop, and traffic load. From the simulation results, it is proved that the proposed system is achieving less energy consumption and improved network lifetime compared with existing Dynamic Source Routing (DSR), Hopfield Neural Network-based Disjoint Path set Selection (HNNDPS) and Multipath DSR (MDSR). Full article
(This article belongs to the Special Issue Sensor Network Technologies and Applications with Wireless Sensor Devices)
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19 pages, 678 KiB  
Article
Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining
by Chunde Liu, Xianli Su and Chuanwen Li
Electronics 2021, 10(3), 302; https://doi.org/10.3390/electronics10030302 - 27 Jan 2021
Cited by 26 | Viewed by 3394
Abstract
There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly [...] Read more.
There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption. Full article
(This article belongs to the Special Issue Sensor Network Technologies and Applications with Wireless Sensor Devices)
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