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Recent Advancements in Sensor Networks and Communication Technologies
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Recent Advancements in Sensor Networks and Communication Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 6523

Editors

Dr. Apostolos Xenakis

E-Mail Website
Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: wireless sensor networks; networks; wireless communications; cross-layer optimization; quantum communications; cross layer security; IoT
Dr. Costas Chaikalis

E-Mail Website
Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: mobile communications; forward error correction coding; reconfigurable (software radio) architectures; cross-layer architectures; V2V applications
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios Kosmanos

E-Mail Website
Guest Editor
1. Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
2. Department of Electrical & Computer Engineering, University of Thessaly, 38334 Volos, Greece
Interests: security and privacy in wireless communications; vehicular ad-hoc networks (VANETs); estimation techniques for physical layers; error detection and correction techniques in physical layers
Special Issues, Collections and Topics in MDPI journals
Dr. Vasileios Vlachos

E-Mail Website
Guest Editor
Department of Economics, School of Economics and Business Administration, University of Thessaly, 38333 Volos, Greece
Interests: cybersecurity; distributed system; cryptoeconomics; data analytics

Special Issue Information

Dear Colleagues,

Recent advancements in sensor networks and communication technologies have become an part integral to our daily lives, impacting various application domains, including smart health, transportation, precision agriculture, and Industry 4.0 (IIoT, IoT, etc.). The main goal of this Special Issue is to provide researchers with an opportunity to spread their research findings on advances related to the applications and technologies of advanced sensor networks and communication technologies across several research domains, including precision agriculture and smart farming, smart grids, V2X, smart cities, Industry 4.0 and beyond, VANETs, 5G/6G, and more. This Special Issue aims to attract leading scientists, both from academia and industry, to share their insights on cutting-edge wireless networking and communication issues and to disseminate their findings.

The Special Issue will highlight cutting-edge algorithms, cross-layer optimization techniques, architectures and frameworks for next-generation WSNs, networking devices, IoT, fog, and pervasive computing environments. It will also feature AI/ML algorithms for communications, algorithms and techniques, as well as state-of-the-art communication technologies that are vital for our society.

The contribution topics of primary interest include, but are not limited to, the following:

  • Wireless Sensor Networks (WSNs): Architectures, Protocols, and Applications;
  • Ad Hoc, Mesh, and Underwater Sensor Networks;
  • Wireless Traffic Management and Routing Techniques;
  • Cross-Layer Optimization in Wireless and IoT Networks;
  • Wireless Multimedia Communications;
  • IoT Communication Protocols (e.g., ZigBee, BLE, NFC, MQTT, XMPP);
  • Network Performance, Quality of Service (QoS), and Reliability;
  • Energy-Efficient Routing in WSNs, IoT, and Fog Networks;
  • Topology Control in 2D and 3D WSNs, IoT, and Fog Environments;
  • Cognitive Radio Networks and Smart Sensor Systems;
  • Vehicular Ad Hoc Networks (VANETs) and Intelligent Vehicular Systems;
  • Error Correction, Detection, and Estimation Codes;
  • Security Challenges, Threats, and Algorithms in Wireless Networks;
  • Monitoring and Control in Wireless and IoT Systems;
  • Resource Allocation and Management Techniques;
  • Measurements, Data Acquisition, and Wireless Signal Transmission;
  • Wireless Edge and Fog Computing: Architectures and Applications;
  • Wireless Physical Computing Systems;
  • Channel Modelling and Propagation Analysis;
  • Wireless System Architectures and Communication Protocols;
  • 5G and Beyond: Technologies, Standards, and Applications;
  • Signal Processing Algorithms for Wireless Systems;
  • Applications in Precision Agriculture, Smart Cities, Connected Vehicles, and UAVs.

Dr. Apostolos Xenakis
Dr. Costas Chaikalis
Dr. Dimitrios Kosmanos
Dr. Vasileios Vlachos
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • wireless sensor networks
  • IoT
  • cognitive radio networks
  • wireless networks
  • 5G and beyond

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Published Papers (5 papers)

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Research

29 pages, 1910 KB  
Article
Path Loss Prediction in Dense WSN–IoT Networks with Machine Learning Techniques Across Diverse Terrains for Energy-Efficient Connectivity
by George Papastergiou, Apostolos Xenakis, Dimitrios Kosmanos, Costas Chaikalis, Menelaos Panagiotis Papastergiou and Vasileios Priovolos
Electronics 2026, 15(11), 2350; https://doi.org/10.3390/electronics15112350 - 28 May 2026
Viewed by 420
Abstract
Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy [...] Read more.
Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy without explicitly linking statistical error metrics to system-level design parameters, thus limiting their practical interpretability in deployment scenarios. This work presents an extensive comparative evaluation among well-known propagation models versus machine learning regressors, and a lightweight convolutional neural network (CNN) for path loss prediction, using transmitter–receiver distance and carrier frequency as input features. A pairwise communication model is adopted to ensure consistent analysis across heterogeneous environments while preserving physical interpretability of the propagation process. Building upon this evaluation, a unified analytical framework is proposed that correlates path loss (PL) prediction accuracy to system-level metrics relevant to WSN–IoT design. Moreover, in this work we apply the Root Mean Square Error (RMSE) of the best-performing model as an empirical estimate of the shadowing standard deviation, under standard statistical assumptions, thereby allowing its direct use in link budget and fade margin calculations. Extensive experimental results across five heterogeneous wireless link datasets demonstrate that improved prediction accuracy leads to reduced transmission power requirements, lower energy consumption, enhanced communication reliability, and extended node lifetime. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)
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17 pages, 18749 KB  
Communication
A LoRa-Based IoT Framework for Structural Modal Identification with Levenberg–Marquardt Optimization
by Quy Ngoc Vu, Thuy-Binh Nguyen and Toan Thanh Dao
Electronics 2026, 15(11), 2267; https://doi.org/10.3390/electronics15112267 - 23 May 2026
Viewed by 1390
Abstract
Structural health monitoring (SHM) is a critical research topic in civil engineering for assessing the integrity of constructed facilities, yet its widespread deployment is often hindered by the high cost of commercial equipment. This study introduces an accessible, vibration-based SHM system consisting of [...] Read more.
Structural health monitoring (SHM) is a critical research topic in civil engineering for assessing the integrity of constructed facilities, yet its widespread deployment is often hindered by the high cost of commercial equipment. This study introduces an accessible, vibration-based SHM system consisting of a slave unit for data acquisition via an MPU6050 sensor and a master unit for long-range wireless transmission using the LoRa protocol. To overcome the inherent noise levels of inexpensive MEMS sensors, we propose a robust modal identification framework that utilizes the Levenberg–Marquardt optimization method combined with a sliding window strategy to accurately estimate damped natural frequencies. Experimental validation conducted on a steel beam demonstrates the technical viability of this event-triggered IoT architecture. The designed system achieved a relative error of only 6.38% in natural frequency identification compared to a high-precision commercial reference system. Ultimately, this framework provides a technically sound, resource-efficient solution for structural assessment. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)
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20 pages, 2201 KB  
Article
Protecting AODV Protocol from Black Hole Attacks on WSNs
by Akourmis Sana, Fakhri Youssef and Rahmani Moulay Driss
Electronics 2026, 15(6), 1280; https://doi.org/10.3390/electronics15061280 - 18 Mar 2026
Viewed by 609
Abstract
The emergence of wireless sensor network (WSN) technology is accompanied by intrinsic constraints and vulnerabilities that render it susceptible to malicious exploitation by intruders. The primary objective of this article is to address security issues caused by black hole attacks, which disrupt the [...] Read more.
The emergence of wireless sensor network (WSN) technology is accompanied by intrinsic constraints and vulnerabilities that render it susceptible to malicious exploitation by intruders. The primary objective of this article is to address security issues caused by black hole attacks, which disrupt the proper functioning of the network and may result in data leakage and loss. We provide a control mechanism named “IDSHNAODV” to specifically counteract the effects of malicious nodes by controlling and removing the first Route Reply (RREP) coming from the black hole attack. This strategy will be put into practice and compared with the “HNAODV” protocol using the NS2 simulator. Three performance metrics will be used, along with a quantity of malicious black hole nodes. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)
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26 pages, 9465 KB  
Article
A Lightweight DTDMA-Assisted MAC Scheme for Ad Hoc Cognitive Radio IIoT Networks
by Bikash Mazumdar and Sanjib Kumar Deka
Electronics 2026, 15(1), 170; https://doi.org/10.3390/electronics15010170 - 30 Dec 2025
Viewed by 539
Abstract
Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge [...] Read more.
Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)
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24 pages, 748 KB  
Article
Evaluating Filter, Wrapper, and Embedded Feature Selection Approaches for Encrypted Video Traffic Classification
by Arkadiusz Biernacki
Electronics 2025, 14(18), 3587; https://doi.org/10.3390/electronics14183587 - 10 Sep 2025
Cited by 6 | Viewed by 2883
Abstract
Classification of video traffic is crucial for network management, enforcing quality of service, and optimising bandwidth. Feature selection plays a vital role in traffic identification by reducing data volume, enhancing accuracy, and reducing computational cost. This paper presents a comparative study of three [...] Read more.
Classification of video traffic is crucial for network management, enforcing quality of service, and optimising bandwidth. Feature selection plays a vital role in traffic identification by reducing data volume, enhancing accuracy, and reducing computational cost. This paper presents a comparative study of three feature selection approaches applied to video traffic identification: filter, wrapper, and embedded. Real-world traffic traces are collected from three popular video streaming platforms: YouTube, Netflix, and Amazon Prime Video, representing diverse content delivery characteristics. The main contributions of this work are (1) the identification of traffic generated by these streaming services, (2) a comparative evaluation of three feature selection methods, and (3) the application of previously untested algorithms for this task. We evaluate the examined methods using F1-score and computational efficiency. The results demonstrate distinct trade-offs among the approaches: the filter method offers low computational overhead with moderate accuracy, while the wrapper method achieves higher accuracy at the cost of longer processing times. The embedded method provides a balanced compromise by integrating feature selection within model training. This comparative analysis offers insights for designing video traffic identification systems in modern heterogeneous networks. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Networks and Communication Technologies)
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