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Applied Sciences
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Advances in Wireless Sensor Networks and Communication Technology
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Advances in Wireless Sensor Networks and Communication Technology

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: 20 November 2025 | Viewed by 1959

Special Issue Editors

Dr. Michel Kulhandjian

E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
2. School of EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: wireless communications; adaptive machine learning; adaptive signal processing; error correcting codes; optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Hovannes Kulhandjian

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA
Interests: underwater communications; visible light communications; applied machine learning; wireless communications; wireless networking; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) and communication technology play pivotal roles in modern-day connectivity and data transmission across various domains. With continuous advancements in these fields, there is a pressing need to explore innovative solutions to enhance efficiency, reliability, and security in wireless communication systems. This Special Issue aims to delve into the latest developments and breakthroughs in wireless sensor networks and communication technologies, offering insights into cutting-edge research and addressing emerging challenges. The objective is to foster collaboration among researchers, engineers, and practitioners to propel the field forward and facilitate its application in diverse domains. Topics of interest include, but are not limited to, the following:

  • Integration of Internet of Things (IoT) with wireless sensor networks;
  • Energy-efficient communication protocols for WSNs;
  • Reconfigurable intelligent surfaces;
  • Data fusion algorithm for wireless sensor networks;
  • Advances in wireless communication technologies (e.g., 5G, Wi-Fi 6);
  • Security and privacy issues in wireless sensor networks;
  • Machine learning and artificial intelligence for wireless communication systems;
  • Millimeter wave (mmWave) and terahertz (THz) communications;
  • Wireless communication for autonomous vehicles;
  • Cellular networks (e.g., 4G, 5G, 6G);
  • Localization and tracking techniques in wireless sensor networks;
  • Cognitive radio networks and dynamic spectrum access techniques for WSNs;
  • IoT connectivity and interoperability challenges;
  • Real-time applications and deployment strategies for WSNs;
  • Emerging trends and future directions in wireless communication technology.

By addressing these topics, this Special Issue aims to provide valuable insights and advancements in wireless sensor networks and wireless communication technology, contributing to the growth and innovation in this dynamic field.

Dr. Michel Kulhandjian
Dr. Hovannes Kulhandjian
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
  • wireless communication technology
  • reconfigurable intelligent surfaces
  • data fusion algorithm
  • autonomous vehicles
  • cellular networks
  • cognitive radio networks

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Published Papers (2 papers)

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23 pages, 1378 KiB  
Article
Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11ax
by Roberto Gaona Juárez, Abel García-Barrientos, Jesus Acosta-Elias, Enrique Stevens-Navarro, César G. Galván, Alessio Palavicini and Ernesto Monroy Cruz
Appl. Sci. 2025, 15(5), 2620; https://doi.org/10.3390/app15052620 - 28 Feb 2025
Viewed by 756
Abstract
This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of [...] Read more.
This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of the measurements in this study were performed using a test bench operating under the IEEE 802.11ax standard in the 2.4 GHz band. RSSI is widely used due to its simplicity and availability; however, its accuracy is limited by signal attenuation, electromagnetic interference, and environmental variability. To mitigate these limitations, the present work proposes the implementation of advanced techniques, including weighted averages and positioning algorithms such as Min–Max, Maximum Likelihood, and trilateration, aiming to achieve an accuracy of 2 m in controlled conditions. The design also included a specialized test bench to calculate the coordinates and estimate the location of unknown nodes using anchor node positioning. This approach combines the simplicity of RSSI with optimized algorithms, providing a robust and practical solution for indoor localization. The results validate the system’s effectiveness and highlight its potential for future applications in real-world environments, opening new possibilities for optimizing wireless sensor networks and addressing the current challenges in localization systems. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks and Communication Technology)
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14 pages, 2023 KiB  
Article
Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
by Dongsup Jin, Minho Jang, Ji-Woong Jang and Gyuyeol Kong
Appl. Sci. 2024, 14(23), 11369; https://doi.org/10.3390/app142311369 - 5 Dec 2024
Viewed by 829
Abstract
This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) [...] Read more.
This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) have varying channel availability. Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. Extensive simulations demonstrate that the RL-based algorithm significantly reduces the expected TTR (ETTR) compared to the JS algorithm, particularly in asymmetric scenarios where M-sequence-based approaches are less effective. This suggests that RL-based approaches not only offer robustness in asymmetric environments but also provide a promising alternative in more predictable settings. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks and Communication Technology)
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