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Wireless Sensor Networks and Next-Generation Communication Technologies
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Wireless Sensor Networks and Next-Generation Communication Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 1902

Special Issue Editor

Dr. Li Chen

E-Mail Website
Guest Editor
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China
Interests: 6G wireless networks; integrated sensing and communication; integrated computation and communication

Special Issue Information

Dear Colleagues,

The Special Issue on Wireless Sensor Networks (WSNs) and Next-Generation Communication Technologies explores the convergence of WSNs with advanced communication systems such as 5G/6G, edge computing, and artificial intelligence (AI) to address challenges in the Internet of Things (IoT) era. Key topics include the following:

  1. Architectural Innovations: Integration of low-power WSNs with massive MIMO, millimeter-wave communications, and cognitive radio techniques to enhance scalability and reliability.
  2. Intelligent Data Processing: AI-driven approaches for real-time data fusion, edge/fog computing frameworks, and lightweight machine learning models for sensor networks.
  3. Energy Efficiency: Optimization strategies for power consumption, dynamic resource allocation, and green communication protocols in heterogeneous networks (e.g., WiFi 6, LoRa, NB-IoT).
  4. Security and Privacy: Cryptographic schemes, distributed privacy-preserving mechanisms, and defenses against physical-layer and cyber-attacks in IoT ecosystems.
  5. Cross-Domain Applications: End-to-end solutions for smart cities, industrial IoT, healthcare monitoring, environmental sensing, emphasizing reliability, latency, and interoperability.

This issue invites interdisciplinary research, including theoretical analyses, algorithm design, system prototypes, and deployment case studies, aiming to advance the robustness, intelligence, and sustainability of WSNs in next-generation communication landscapes.

Dr. Li Chen
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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 (WSNs)
  • 5G/6G communication systems
  • internet of things (IoT)
  • edge/fog computing
  • AI-driven data fusion
  • energy-efficient protocols
  • security and privacy
  • heterogeneous network interoperability
  • smart city and industrial IoT
  • distributed network architectures

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

Published Papers (2 papers)

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Research

31 pages, 1954 KB  
Article
HASCom: A Heterogeneous Affective-Semantic Communication Framework for Speech Transmission
by Zhenjia Yu, Taojie Zhu, Md Arman Hossain, Zineb Zbarna and Lei Wang
Sensors 2026, 26(7), 2158; https://doi.org/10.3390/s26072158 - 31 Mar 2026
Viewed by 676
Abstract
Driven by the development of next-generation wireless networks and the widespread adoption of sensing, communication is shifting from traditional bit-level transmission to intelligent, rich interactions within our digital social system. However, existing speech semantic communication frameworks predominantly focus on textual accuracy, neglecting the [...] Read more.
Driven by the development of next-generation wireless networks and the widespread adoption of sensing, communication is shifting from traditional bit-level transmission to intelligent, rich interactions within our digital social system. However, existing speech semantic communication frameworks predominantly focus on textual accuracy, neglecting the critical affective information (e.g., tone and emotion) that is essential for natural human-centric interactions in the real world. To address this limitation, we propose the Heterogeneous Affective Speech Semantic Communication (HASCom) framework, designed for the robust transmission of highly expressive speech over complex wireless channels. Specifically, we design a heterogeneous dual-stream transmission architecture that decouples discrete phoneme-level linguistic content from continuous emotional embeddings. For discrete semantic information, we use reliable digital coding protected by Low-Density Parity-Check (LDPC) to guarantee strict recoverability. Conversely, for emotional features, we employ Deep Joint Source-Channel Coding (JSCC) analog transmission to prevent irreversible quantization errors and the cliff effect. Additionally, we develop a prior-guided diffusion reconstruction module at the receiving end. This module leverages a structural prior network to align the decoded semantics, which then steers the reverse diffusion process conditioned on the recovered affective features. Extensive experiments under both AWGN and Rayleigh fading channels demonstrate that HASCom significantly outperforms state-of-the-art baselines. Specifically, it achieves superior objective semantic similarity and subjective Mean Opinion Score (MOS) at low Signal-to-Noise Ratios (SNRs), while the JSCC transmission modules maintain an ultra-low inference latency of less than 0.1 ms, validating its high efficiency and robustness for practical deployments. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Next-Generation Communication Technologies)
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22 pages, 7184 KB  
Article
Multimodal Optimal Base Station Selection Network for Intelligent Communications
by Haie Dou, Xinyu Zhan, Xinyu Zhang, Taojie Zhu and Lei Wang
Sensors 2025, 25(22), 6895; https://doi.org/10.3390/s25226895 - 12 Nov 2025
Viewed by 730
Abstract
With the rapid development of next-generation wireless communication systems, the increasing density of heterogeneous base stations and the dynamic nature of channel conditions have posed significant challenges to accurate and timely base station selection. Traditional single-modal approaches relying solely on partial channel or [...] Read more.
With the rapid development of next-generation wireless communication systems, the increasing density of heterogeneous base stations and the dynamic nature of channel conditions have posed significant challenges to accurate and timely base station selection. Traditional single-modal approaches relying solely on partial channel or location information often fail to capture the complex semantics of real communication scenarios, leading to suboptimal decision-making. To address these limitations, this paper proposes the Multimodal Optimal Base Station Selection Network (MOBS-Net), which integrates multimodal spatial and temporal information to achieve both optimal base station judgment and proactive prediction. The judgment module employs convolutional neural networks to extract image semantics and a Transformer-based fusion mechanism to combine image, location, and channel features for real-time decision-making. The prediction module leverages multimodal sequential data and a large-scale multimodal model to extract temporal semantics, enabling proactive base station switching under dynamic channel conditions. Extensive experiments demonstrate that MOBS-Net significantly outperforms single-modal baselines, achieving an accuracy of 92.20% for optimal base station judgment and 91.5% for prediction tasks. These results highlight the reliability and effectiveness of MOBS-Net for intelligent base station decision-making in dynamic wireless environments. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Next-Generation Communication Technologies)
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