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Applied Sciences
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Advanced Applications of Wireless Sensor Network (WSN)
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Advanced Applications of Wireless Sensor Network (WSN)

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: 31 October 2025 | Viewed by 2633

Special Issue Editors

Dr. Pedro Miguel Sobral

E-Mail Website1 Website2
Guest Editor
1. Faculty of Science and Technology (FCT), University Fernando Pessoa (UFP), Intelligent and Ubiquitous Sensing Systems (ISUS) Group, 4249-004 Porto, Portugal
2. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), University of Porto, 4200-465 Porto, Portugal
Interests: computer network sensor networks ubiquitous computing context sensitive applications; ambient assisted living; IoT, smart spaces, wireless networks
Dr. José Manuel Torres

E-Mail Website1 Website2
Guest Editor
1. Faculty of Science and Technology (FCT), University Fernando Pessoa (UFP), Intelligent and Ubiquitous Sensing Systems (ISUS) Group, 4249-004 Porto, Portugal
2. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), University of Porto, 4200-465 Porto, Portugal
Interests: artificial intelligence; computer vision; machine learning; algorithms; ubicomp and IoT systems
Special Issues, Collections and Topics in MDPI journals
Dr. Rui S. Moreira

E-Mail Website1 Website2
Guest Editor
1. Faculty of Science and Technology (FCT), University Fernando Pessoa (UFP), Intelligent and Ubiquitous Sensing Systems (ISUS) Group, 4249-004 Porto, Portugal
2. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), University of Porto, 4200-465 Porto, Portugal
Interests: applying machine learning together with reflective middleware and software architectures for building and managing intelligent and sensing dynamically adaptable distributed, ubiquitous and IoT systems
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriel Villarrubia González

E-Mail Website
Guest Editor
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; ambient intelligence; wireless sensor networks; bigdata; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) have emerged as a cornerstone technology in the era of pervasive computing, enabling seamless data collection and transmission in an extensive range of applications; this encompasses areas such as environmental monitoring, intelligent healthcare systems and smart cities. The convergence of WSNs with advanced Internet of Things (IoT) computing paradigms such as Edge, Fog and Cloud, has revealed and stimulated unprecedented opportunities for innovation in various domains. This Special Issue (SI) aims to explore the frontiers of research and development in Advanced Applications of Wireless Sensor Networks (WSNs) within the context of IoT smart computing systems.

The integration of WSNs with IoT computing technologies has led to transformative advances in various domains, including, but not limited to, the following:

  • Intelligent sensing and ubiquitous systems
  • Smart cities and sustainable urban planning
  • Environmental monitoring and protection
  • Intelligent home healthcare and telemedicine systems
  • Precision agriculture and sustainable agri-tech solutions
  • Structural health monitoring and infrastructure management
  • Industry automation and process control
  • Disaster management and emergency response

This Special Issue aims to collect high-quality research papers that address various aspects of advanced WSN applications, including the following:

  • Novel architectures and protocols for WSN-enabled IoT systems
  • Edge computing frameworks using WSNs for data collection, processing and analysis
  • Integration of machine learning and artificial intelligence techniques into WSNs and IoT computing systems
  • Applications of WSNs in smart sensing ubiquitous systems
  • Energy-efficient and resource-aware algorithms for WSN deployment and operation
  • Security and privacy considerations and concerns in WSN-enabled IoT ecosystems
  • Case studies and practical implementations of WSN-based solutions in real-world IoT scenarios
  • Advances in scalability, reliability and resilience of WSNs in dynamic and heterogeneous IoT environments
  • Next generation WSN-solutions for primary sector industries enabling sustainable farming, fishing, forestry and mining
  • Future applications of WSNs on smart and sustainable cities
  • Advances in the use of WSNs for smart home assisted living solutions
  • The role of WSNs in the design and implementation of disaster and emergency management systems
  • Advanced combination of WSNs and Artificial Intelligence in the scope of industry and automation scenarios

We invite researchers, academics and practitioners to submit original research papers, review articles and case studies that address the above or akin topics and application areas. Submissions should present significant insights, innovative methodologies, and experimental validations that may contribute to the state of the art in Advanced Applications of Wireless Sensor Networks (WSN) within the context of IoT computing systems and applications.

We look forward to receiving your valuable contributions and fostering meaningful discussions to advance the development and adoption of cutting-edge WSN technologies.

Dr. Pedro Miguel Sobral
Dr. José Manuel Torres
Dr. Rui S. Moreira
Dr. Gabriel Villarrubia González
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

  • wireless sensor networks
  • IoT systems
  • artificial intelligence computing

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Published Papers (2 papers)

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23 pages, 3210 KiB  
Article
False Data Injection Attacks on Reinforcement Learning-Based Charging Coordination in Smart Grids and a Countermeasure
by Amr A. Elshazly, Islam Elgarhy, Ahmed T. Eltoukhy, Mohamed Mahmoud, William Eberle, Maazen Alsabaan and Tariq Alshawi
Appl. Sci. 2024, 14(23), 10874; https://doi.org/10.3390/app142310874 - 24 Nov 2024
Cited by 1 | Viewed by 900
Abstract
Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false [...] Read more.
Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase their chances of charging compared to honest consumers. As a result, honest consumers experience reduced charging levels for their batteries, leading to a degradation in the system’s performance in terms of fairness, consumer satisfaction, and overall reward. These negative effects become more severe as the amount of power allocated for charging decreases and as the number of attackers in the system increases. Since the total available power for charging is limited, some honest consumers with genuinely low SoC values are not selected, creating a significant disparity in battery charging levels between honest and malicious consumers. To counter this serious threat, we develop a deep learning-based FDI attack detector and evaluated it using a real-world dataset. Our experiments show that our detector can identify malicious consumers with high accuracy and low false alarm rates, effectively protecting the RL-based charging coordination system from FDI attacks and mitigating the negative impacts of these attacks. Full article
(This article belongs to the Special Issue Advanced Applications of Wireless Sensor Network (WSN))
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16 pages, 1323 KiB  
Article
Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
by Robin Janssens, Erik Mannens, Rafael Berkvens and Stijn Denis
Appl. Sci. 2024, 14(20), 9386; https://doi.org/10.3390/app14209386 - 15 Oct 2024
Viewed by 1080
Abstract
Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation [...] Read more.
Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment’s state. Full article
(This article belongs to the Special Issue Advanced Applications of Wireless Sensor Network (WSN))
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