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: 15 March 2026 | Viewed by 2190

Special Issue Editors


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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
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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

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

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

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Keywords

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

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Published Papers (2 papers)

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Research

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 167
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 1 | Viewed by 1644
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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