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Sensor Networks and Communication with AI
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Sensor Networks and Communication with AI

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5224

Special Issue Editors

Dr. Ge Wang

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: AIoT; multi-modality fusion; intelligent decision
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Wang

E-Mail Website
Guest Editor
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: machine learning; deep learning; sensors; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growing development of artificial intelligence, its application in solving practical problems in sensor networks and communication has become a popular topic. Using AI technologies may offer new opportunities to improve the accuracy, efficiency, and usability of sensing, communication, networking, etc., with sensors.

This Special Issue therefore aims to collect original research and review articles on the recent advances, technologies, solutions, and applications, as well as new challenges, in the field of sensor networks and communication with AI.

Potential topics include, but are not limited to, the following:

  • Edge and cloud computing;
  • Embedded and energy-harvesting systems;
  • Backscatter communication and wireless power;
  • Implantable and wearable computing;
  • Millimeter-wave and terahertz communications;
  • Mobile health;
  • Applications of machine learning to mobile/wireless research;
  • Transfer learning and domain adaptation for mobile applications;
  • Large language models (LLMs) and generative AI for mobile and wireless systems;
  • The next generation of mobile networks (5G, 6G and beyond);
  • Mobile web, video, virtual reality, augmented reality and other applications;
  • Novel applications of wireless signals;
  • Vehicular, robotic and drone-based networking;
  • Communication with reconfigurable and intelligent reflective surfaces, meta materials, and flexible surfaces;
  • Sensing with radio, light, sound, and acoustics;
  • Smart cities and smart spaces (e.g., smart factories, smart workspaces, smart agriculture, urban mobility);
  • Ubiquitous computing and mobile human–computer interaction;
  • Underwater networking and sensing;
  • Visible light communications;
  • Wireless localization and tracking;
  • Reducing the carbon footprint of wireless networks and mobile devices.

Dr. Ge Wang
Dr. Fei Wang
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-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

  • edge and cloud computing
  • energy-harvesting systems
  • wearable computing
  • next generation of mobile networks
  • smart cities

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

Published Papers (4 papers)

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Research

22 pages, 5390 KB  
Article
Joint Optimization of Time Slot and Power Allocation in Underwater Acoustic Communication Networks
by Xuan Geng and Yongkang Hu
Sensors 2026, 26(7), 2188; https://doi.org/10.3390/s26072188 - 1 Apr 2026
Viewed by 526
Abstract
This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding [...] Read more.
This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding to time-slot scheduling and power allocation. The sub-objective corresponding to time-slot scheduling is addressed by constructing a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) learning. In this model, the agent learns the time slot allocation policy with the goal of increasing the number of successfully transmitted links while reducing the collision. For the sub-objective corresponding to power allocation, another MDP model is developed, solved by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, in which each underwater transmission node acts as an independent agent. The MADDPG approach enables the system to improve channel capacity under energy limitation, which maximizes the total capacity of successfully transmitted links. In terms of model execution, the DQN adopts a centralized training and time slot allocation, while MADDPG uses a centralized training and distributed execution to select the transmission power by each node. Simulation results show that the proposed joint optimization algorithm demonstrates better performance in the number of successfully transmitted links and channel capacity compared to TDMA, Slotted ALOHA, and other algorithms. Full article
(This article belongs to the Special Issue Sensor Networks and Communication with AI)
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28 pages, 2832 KB  
Article
Unsupervised Neural Beamforming for Uplink MU-SIMO in 3GPP-Compliant Wireless Channels
by Cemil Vahapoglu, Timothy J. O’Shea, Wan Liu, Tamoghna Roy and Sennur Ulukus
Sensors 2026, 26(2), 366; https://doi.org/10.3390/s26020366 - 6 Jan 2026
Viewed by 724
Abstract
Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and [...] Read more.
Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article
(This article belongs to the Special Issue Sensor Networks and Communication with AI)
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21 pages, 447 KB  
Article
Enhancing Intrusion Detection for IoT and Sensor Networks Through Semantic Analysis and Self-Supervised Embeddings
by Yanshen Liu and Yinfeng Guo
Sensors 2025, 25(22), 7074; https://doi.org/10.3390/s25227074 - 20 Nov 2025
Cited by 1 | Viewed by 1694
Abstract
As cyber threats continue to grow in complexity and sophistication, the need for advanced network and sensor security solutions has never been more urgent. Traditional intrusion detection methods struggle to keep pace with the sheer volume of network traffic and the evolving nature [...] Read more.
As cyber threats continue to grow in complexity and sophistication, the need for advanced network and sensor security solutions has never been more urgent. Traditional intrusion detection methods struggle to keep pace with the sheer volume of network traffic and the evolving nature of attacks. In this paper, we propose a novel machine learning-driven Intrusion Detection System (IDS) that improves intrusion detection through a comprehensive analysis of multidimensional data. Transcending traditional feature extraction methods, the system introduces geospatial context features and self-supervised semantic features that provide rich contextual information for enhanced threat identification. The system’s performance is validated on a carefully curated dataset from China Mobile, containing over 100 K records, achieving an impressive 98.5% accuracy rate in detecting intrusions. The results highlight the effectiveness of ensemble learning methods and underscore the system’s potential for real-world deployment, offering a significant advancement in the development of intelligent cybersecurity tools that can adapt to the ever-changing landscape of cyber threats. Furthermore, the proposed framework is extensible to IoT and wireless sensor networks (WSNs), where resource constraints and new attack surfaces demand lightweight yet semantically enriched IDS solutions. Full article
(This article belongs to the Special Issue Sensor Networks and Communication with AI)
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22 pages, 3131 KB  
Article
CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination
by Tingting Zhang, Qunhang Fu, Han Ding, Ge Wang and Fei Wang
Sensors 2025, 25(15), 4706; https://doi.org/10.3390/s25154706 - 30 Jul 2025
Cited by 1 | Viewed by 1206
Abstract
In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over [...] Read more.
In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over time. However, Wi-Fi-based indoor action recognition under incremental learning faces two major challenges: catastrophic forgetting of previously learned knowledge and uncontrolled model expansion as new classes are added. To address these issues, we propose CAREC, a class-incremental framework that balances dynamic model expansion with efficient compression. CAREC adopts a multi-branch architecture to incorporate new classes without compromising previously learned features and leverages balanced knowledge distillation to compress the model by 80% while preserving performance. A data replay strategy retains representative samples of old classes, and a super-feature extractor enhances inter-class discrimination. Evaluated on the large-scale XRF55 dataset, CAREC reduces performance degradation by 51.82% over four incremental stages and achieves 67.84% accuracy with only 21.08 M parameters, 20% parameters compared to conventional approaches. Full article
(This article belongs to the Special Issue Sensor Networks and Communication with AI)
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