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Wireless Sensor Networks Applications: From Theory to Practice
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Wireless Sensor Networks Applications: From Theory to Practice

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 6776

Special Issue Editors

Dr. Mahdi Zareei

E-Mail Website
Guest Editor
School of Engineering and Sciences, Tecnologico de Monterrey, Guadalajara, Mexico
Interests: wireless communication; cognitive radio ad hoc network; wireless sensor network
Special Issues, Collections and Topics in MDPI journals
Dr. Jesús Arturo Pérez-Díaz

E-Mail Website
Guest Editor
School of Engineering and Sciences, Tecnologico de Monterrey, Guadalajara, Mexico
Interests: information security; cyberattack mitigation in SDN security; DDoS preventation

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) have emerged as versatile technology with broad applicability across diverse fields, such as agriculture, healthcare, environmental monitoring, and industrial automation. WSNs are made up of numerous compact sensor nodes that gather and wirelessly transmit data to a centralized base station or gateway.

Even though WSNs offer numerous advantages, several obstacles need to be overcome before they can be extensively implemented in practical applications. These issues encompass energy efficiency, scalability, security, and reliability, among others.

This call for submissions invites original research articles, review articles, and case studies that delve into the theory and practice of WSN applications. The range of this call encompasses, but is not limited to, the following subjects:

  • WSNs architectures, protocols, and standards;
  • Energy-efficient WSNs design and optimization;
  • Scalable WSNs for large-scale deployments;
  • Security and privacy in WSNs;
  • WSNs for environmental monitoring and smart agriculture;
  • WSNs for healthcare and medical applications;
  • WSNs for industrial automation and control;
  • Real-world case studies and applications of WSNs.

We encourage scholars, professionals, and specialists in the field to contribute their original and previously unshared research on the specified subjects. Each submission will be subject to a thorough peer-review procedure, and approved papers will be included in either the conference proceedings or a pertinent journal's special issue.

Dr. Mahdi Zareei
Dr. Jesús Arturo Pérez-Díaz
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. Applied Sciences 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

  • WSNs architectures, protocols, and standards
  • energy-efficient WSNs design and optimization
  • scalable WSNs for large-scale deployments
  • security and privacy in WSNs
  • WSNs for environmental monitoring and smart agriculture
  • WSNs for healthcare and medical applications
  • WSNs for industrial automation and control
  • real-world case studies and applications of WSNs

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

16 pages, 841 KiB  
Article
A Decision Tree-Based Pattern Classification and Regression for a Mobility Support Scheme in Industrial Wireless Sensor Networks
by Cheonyong Kim and Sangdae Kim
Appl. Sci. 2025, 15(3), 1408; https://doi.org/10.3390/app15031408 - 30 Jan 2025
Cited by 1 | Viewed by 570
Abstract
Industrial wireless sensor networks (IWSNs) are exploited to achieve various purposes, including enhancing productivity and reducing cost in a variety of industries, and they require low-delay and high-reliability packet transmission. To achieve these requirements, a network manager is responsible for constructing a graph, [...] Read more.
Industrial wireless sensor networks (IWSNs) are exploited to achieve various purposes, including enhancing productivity and reducing cost in a variety of industries, and they require low-delay and high-reliability packet transmission. To achieve these requirements, a network manager is responsible for constructing a graph, allocating resources, and determining the transmission cycle and path of each node in advance. However, this approach is inadequate for exploiting mobile devices that constantly change network topology because frequent graph reconstruction and resource reallocation are required. In other words, despite the increasing reliance on mobile devices in a variety of industries, existing schemes cannot adequately respond to path failures due to device movement and subsequent packet loss during recovery. For example, real-time tracking of mobile vehicles in mining operations is crucial for safety and efficiency, where path failures and packet loss can lead to significant issues. To solve this problem, we propose a mobility support scheme to prevent packet loss caused by device mobility. In the proposed scheme, we first classify mobility patterns based on the decision tree and then apply regression to predict their trajectories. By leveraging this predictive information, the network manager could preemptively adjust graph construction and resource allocation to accommodate topology changes. Performance evaluation results show that the predicted mobility patterns closely match the actual patterns, achieving a high packet delivery ratio compared to conventional schemes, while also enabling efficient resource management. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications: From Theory to Practice)
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23 pages, 6222 KiB  
Article
Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder
by Md. Nazmul Hasan, Sana Ullah Jan and Insoo Koo
Appl. Sci. 2024, 14(17), 7717; https://doi.org/10.3390/app14177717 - 1 Sep 2024
Cited by 1 | Viewed by 3120
Abstract
The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often [...] Read more.
The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (LSTM-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method’s functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications: From Theory to Practice)
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19 pages, 15881 KiB  
Article
Rotating Target Detection Using Commercial 5G Signal
by Penghui Chen, Liuyang Tian, Yujing Bai and Jun Wang
Appl. Sci. 2024, 14(10), 4282; https://doi.org/10.3390/app14104282 - 18 May 2024
Viewed by 1080
Abstract
Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an [...] Read more.
Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of a high-frequency band, high bandwidth, and a large number of base stations, which give it significant advantages for use in passive radar. Therefore, in this paper, we introduce a passive radar target detection method based on 5G signals and design a rotating target speed measurement experiment. In the experiment, this paper validated the method of detecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications: From Theory to Practice)
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15 pages, 3542 KiB  
Article
Optimization of Coverage and Capacity Using Smart Antennae
by Min-Che Ho, Pin-Yu Song, Yi-Shian Chiou, Yueh-Tan Lee and Li-Ling Huang
Appl. Sci. 2023, 13(19), 10897; https://doi.org/10.3390/app131910897 - 30 Sep 2023
Cited by 1 | Viewed by 1316
Abstract
In the rural and geographically remote regions of Taiwan, the high cost of establishing information infrastructure has resulted in significantly lower internet penetration and usage rates compared with urban areas. To address the network demands in such remote mountainous areas, the deployment of [...] Read more.
In the rural and geographically remote regions of Taiwan, the high cost of establishing information infrastructure has resulted in significantly lower internet penetration and usage rates compared with urban areas. To address the network demands in such remote mountainous areas, the deployment of multiple mobile base stations has become essential. However, the wireless implementation of base stations can lead to signal interference issues. This research aims to enhance the signal reception capabilities of end-user devices by utilizing intelligent directional antennas. This study employs five directional smart antennas, each of which can be independently adjusted to be active or inactive. Unlike traditional omnidirectional antennas that cause interference in overlapping coverage areas for end-user devices, our proposed adaptive directional antenna algorithm optimizes energy consumption by selectively activating directional antennas and concurrently reduces signal interference problems for end-user devices. The results of this research offer valuable insights for improving network connectivity and efficiency in remote and underserved areas. Through experimental simulations conducted in an environment with 10 base stations per square kilometer, the utilization of smart antennas, as opposed to omnidirectional antennas, results in a significant improvement of 33.8% in signal coverage. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications: From Theory to Practice)
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