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Wireless Sensor Network: Latest Advances and Prospects

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 9768

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Dr. Alexandra Bousia

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Department of Business Administration, University of Thessaly, 41500 Larissa, Greece
Interests: business intelligence; artificial intelligence; smart cities; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, there has been a tremendous growth in wireless sensor networks, especially due to the technological advancements and the challenges of Internet of Things (IoT). Wireless IoT devices can be flexibly deployed in the target region to sense and collect relevant information. Due to their flexibility, wireless sensor networks can be used in several applications, including smart cities, smart grid, health, agriculture, telecommunications, etc. Although the area of wireless sensor networks has been thoroughly examined in the literature, there are still a wide range of open issues and challenges regarding wireless sensor network applications. This Special Issue is aimed at addressing issues that are involved in connectivity, heterogeneity of infrastructure, data processing, management, energy waste, security, and privacy. This includes the following:

Wireless sensor networks and their applications;
Novel architectures, protocols, and algorithms for wireless sensor network applications;
Communication platforms and access technologies;
Theoretical modelling and frameworks for wireless sensor network applications;
Case studies, real solutions, designs, and implementations;
Resource allocation techniques;
Measurements and transmission in wireless networks;
Security and privacy frameworks for wireless sensor network applications;
Innovative techniques for wireless sensor network security applications;
Environmental challenges;
Solutions for smart transport and traffic;
Solutions for smart mobility and parking.

Dr. Alexandra Bousia
Guest Editor

Manuscript Submission Information

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Keywords

wireless sensor networks
Internet of Things
network architectures and protocols
big data analysis
efficient communications

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Published Papers (11 papers)

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Research

19 pages, 1127 KB

Open AccessArticle

Movable Wireless Sensor-Enabled Waterway Surveillance with Enhanced Coverage Using Multi-Layer Perceptron and Reinforced Learning

by Minsoo Kim and Hyunbum Kim

Electronics 2025, 14(16), 3295; https://doi.org/10.3390/electronics14163295 - 19 Aug 2025

Viewed by 267

Abstract

Waterway networking environments present unique challenges due to their dynamic nature, including vessel movement, water flow, and varying water quality. These challenges render traditional static surveillance systems inadequate for effective monitoring. This study proposes a novel wireless sensor-enabled surveillance and monitoring framework tailored to waterway conditions, integrating a two-phase approach with a Movement Phase and a Deployment Phase. In the Movement Phase, a Multi-Layer Perceptron (MLP) guides sensors efficiently toward a designated target area, minimizing travel time and computational complexity. Subsequently, the Deployment Phase utilizes reinforcement learning (RL) to arrange sensors within the target area, optimizing coverage while minimizing overlap between sensing regions. By addressing the unique requirements of waterways, the proposed framework ensures both efficient sensor mobility and resource utilization. Experimental evaluations demonstrate the framework’s effectiveness in achieving high coverage and minimal overlap, with comparable performance to traditional clustering algorithms such as K-Means. The results confirm that the proposed approach achieves flexible, scalable, and computationally efficient monitoring tailored to waterway environments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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20 pages, 9514 KB

Open AccessArticle

The Behavior of an IoT Sensor Monitoring System Using a 5G Network and Its Challenges in 6G Networking

by Georgios Gkagkas, Vasiliki Karamerou, Angelos Michalas, Michael Dossis and Dimitrios J. Vergados

Electronics 2025, 14(16), 3167; https://doi.org/10.3390/electronics14163167 - 8 Aug 2025

Viewed by 446

Abstract

The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform an evaluation of the 5G infrastructure for sensor networks in order to quantify the performance in terms of energy efficiency and bandwidth within a testing environment. We used an ESP32 sensor with BLE-connected sensing devices for environmental conditions, and a Raspberry Pi with the Waveshare SIM8200EA-M2 5G module for cellular connectivity. We measured the power usage of each component of the system, in real conditions, as well as the power consumption for different bandwidth usage scenarios, and the end-to-end delay of the system. The results showed that the system is capable of achieving the required delay and bandwidth; however, the energy efficiency of the specific setup leaves room for improvement. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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21 pages, 9379 KB

Open AccessArticle

UDirEar: Heading Direction Tracking with Commercial UWB Earbud by Interaural Distance Calibration

by Minseok Kim, Younho Nam, Jinyou Kim and Young-Joo Suh

Electronics 2025, 14(15), 2940; https://doi.org/10.3390/electronics14152940 - 23 Jul 2025

Viewed by 395

Abstract

Accurate heading direction tracking is essential for immersive VR/AR, spatial audio rendering, and robotic navigation. Existing IMU-based methods suffer from drift and vibration artifacts, vision-based approaches require LoS and raise privacy concerns, and RF techniques often need dedicated infrastructure. We propose UDirEar, a COTS UWB device-based system that estimates user heading using solely high-level UWB information like distance and unit direction. By initializing an EKF with each user’s constant interaural distance, UDirEar compensates for the earbuds’ roto-translational motion without additional sensors. We evaluate UDirEar on a step-motor-driven dummy head against an IMU-only baseline (MAE 30.8°), examining robustness across dummy head–initiator distances, elapsed time, EKF calibration conditions, and NLoS scenarios. UDirEar achieves a mean absolute error of 3.84° and maintains stable performance under all tested conditions. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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19 pages, 684 KB

Open AccessArticle

A Wi-Fi Fingerprinting Indoor Localization Framework Using Feature-Level Augmentation via Variational Graph Auto-Encoder

by Dongdeok Kim, Jae-Hyeon Park and Young-Joo Suh

Electronics 2025, 14(14), 2807; https://doi.org/10.3390/electronics14142807 - 12 Jul 2025

Viewed by 535

Abstract

Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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17 pages, 3041 KB

Open AccessArticle

Error Prediction and Simulation of Strapdown Inertial Navigation System Based on Deep Neural Network

by Jinlai Liu, Tianran Zhang, Lubin Chang and Pinglan Li

Electronics 2025, 14(13), 2622; https://doi.org/10.3390/electronics14132622 - 28 Jun 2025

Viewed by 424

Abstract

In order to address the problem of error accumulation in long-duration autonomous navigation using Strapdown Inertial Navigation Systems (SINS), this paper proposes an error prediction and correction method based on Deep Neural Networks (DNN). A 12-dimensional feature vector is constructed using angular increments, velocity increments, and real-time attitude and velocity states from the inertial navigation system, while a 9-dimensional response vector is composed of attitude, velocity, and position errors. The proposed DNN adopts a feedforward architecture with two hidden layers containing 10 and 5 neurons, respectively, using ReLU activation functions and trained with the Levenberg–Marquardt algorithm. The model is trained and validated on a comprehensive dataset comprising 5 × 10³ seconds of real vehicle motion data collected at 100 Hz sampling frequency, totaling 5 × 10⁵ sample points with a 7:3 train-test split. Experimental results demonstrate that the DNN effectively captures the nonlinear propagation characteristics of inertial errors and significantly outperforms traditional SINS and LSTM-based methods across all dimensions. Compared to pure SINS calculations, the proposed method achieves substantial error reductions: yaw angle errors decrease from 2.42 × 10⁻² to 1.10 × 10⁻⁴ radians, eastward velocity errors reduce from 455 to 4.71 m/s, northward velocity errors decrease from 26.8 to 4.16 m/s, latitude errors reduce from 3.83 × 10⁻³ to 7.45 × 10⁻⁴ radians, and longitude errors reduce dramatically from 3.82 × 10⁻² to 1.5 × 10⁻⁴ radians. The method also demonstrates superior performance over LSTM-based approaches, with yaw errors being an order of magnitude smaller and having significantly better trajectory tracking accuracy. The proposed method exhibits strong robustness even in the absence of external signals, showing high potential for engineering applications in complex or GPS-denied environments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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19 pages, 6328 KB

Open AccessArticle

Seamless Indoor–Outdoor Localization Through Transition Detection

by Jaehyun Yoo

Electronics 2025, 14(13), 2598; https://doi.org/10.3390/electronics14132598 - 27 Jun 2025

Viewed by 376

Abstract

Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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26 pages, 3728 KB

Open AccessArticle

Developing a Novel Muscle Fatigue Index for Wireless sEMG Sensors: Metrics and Regression Models for Real-Time Monitoring

by Dimitrios Miaoulis, Ioannis Stivaros and Stavros Koubias

Electronics 2025, 14(11), 2097; https://doi.org/10.3390/electronics14112097 - 22 May 2025

Cited by 1 | Viewed by 1015

Abstract

Muscle fatigue impacts performance in sports, rehabilitation, and daily activities, with surface electromyography (sEMG) widely used for monitoring. In this study, we developed a wearable sEMG device and conducted experiments to create a dataset for fatigue analysis. The sEMG signals were analyzed through a multi-domain feature extraction pipeline, incorporating time-domain (e.g., RMS, ARV), frequency-domain (e.g., MNF), and hybrid-domain metrics (e.g., MNF/ARV ratio, Instantaneous Mean Amplitude Difference), to identify physiologically meaningful indicators of fatigue. To ensure inter-subject comparability, we applied a dynamic standardization strategy that normalized each feature based on the RMS value of the first active segment, establishing a consistent baseline across participants. Using these standardized features, we explored several fatigue index construction methods—such as weighted sums, t-SNE, and Principal Component Analysis (PCA)—to capture fatigue progression effectively. We then trained and evaluated multiple machine learning models such as LR, SVR, RF, GBM, LSTM, CNN, and kNN to predict fatigue levels, selecting the most effective approach for real-time monitoring. Integrated into a wireless BLE-enabled sensor platform, the system offers seamless body placement, mobility, and efficient data transmission. An initial calibration phase ensures adaptation to individual muscle profiles, enhancing accuracy. By balancing on-device processing with efficient wireless communication, this platform delivers scalable, real-time fatigue monitoring across diverse applications. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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21 pages, 1171 KB

Open AccessArticle

Statistical Analysis of the Sum of Double Random Variables for Security Applications in RIS-Assisted NOMA Networks with a Direct Link

by Sang-Quang Nguyen, Phuong T. Tran, Bui Vu Minh, Tran Trung Duy, Anh-Tu Le, Lubos Rejfek and Lam-Thanh Tu

Electronics 2025, 14(2), 392; https://doi.org/10.3390/electronics14020392 - 20 Jan 2025

Cited by 1 | Viewed by 1284

Abstract

Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS’s phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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16 pages, 652 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Novel Architecture for Virtual Network Twin Deployment

by Amir Hossein Banisadr and Xavier Hesselbach

Electronics 2024, 13(24), 5045; https://doi.org/10.3390/electronics13245045 - 22 Dec 2024

Viewed by 835

Abstract

In this paper, a novel architecture is proposed that enhances network connectivity by combining network virtualization and the digital twin approach. A virtual network twin (VNT) framework is designed to emulate the behavior of the original network within a virtualized environment. This framework provides an enhanced connection experience for users, mirroring the performance of the original network while avoiding the limitations of previous methods such as Telnet, SSH, and VPN. By integrating the network virtualization approach and the concept of digital twins, this framework can improve network visibility, security, and robustness in real-time connectivity through appropriate communication protocols and artificial intelligence (AI) methods. The application of this proposal can significantly impact key areas such as medical applications, autonomous driving, and space communications. This paper introduces the VNT architecture; its core components; requirements; and evaluation metrics, such as the accuracy of the VNT. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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18 pages, 10134 KB

Open AccessArticle

A Novel Sensor Deployment Strategy Based on Probabilistic Perception for Industrial Wireless Sensor Network

by Xiaokai Liu, Fangmin Xu, Lina Ning, Yuhan Lv and Chenglin Zhao

Electronics 2024, 13(24), 4952; https://doi.org/10.3390/electronics13244952 - 16 Dec 2024

Cited by 2 | Viewed by 1136

Abstract

The rapid development of Industrial Internet of Things (IIoT) technology has highlighted the critical role of wireless sensor networks in enabling intelligent production and equipment monitoring. Effective sensor deployment is essential for ensuring communication quality and transmission speed in IIoT environments. This paper presents a novel sensor deployment strategy that integrates four key metrics: deployment cost, energy consumption, network connectivity, and sensing probability. To address the challenges of multi-dimensional optimization, the proposed method normalizes these metrics and assigns appropriate weights based on their relative importance. A major innovation of this approach is the inclusion of larger-scale environmental obstacles, which enhances its adaptability to diverse industrial settings and specific deployment scenarios. Through a comprehensive set of simulation experiments across different scenarios, the proposed particle swarm/genetic hybrid algorithm demonstrates superior performance compared to existing methods, even surpassing 10% in performance. Specifically, it excels in optimizing the newly introduced network performance metric and significantly improves search convergence time, making it a highly efficient and effective solution for sensor network optimization in IIoT applications. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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25 pages, 1778 KB

Open AccessArticle

Efficient User Pairing and Resource Optimization for NOMA-OMA Switching Enabled Dynamic Urban Vehicular Networks

by Aravindh Balaraman, Shigeo Shioda, Yonggang Kim, Yohan Kim and Taewoon Kim

Electronics 2024, 13(23), 4834; https://doi.org/10.3390/electronics13234834 - 7 Dec 2024

Cited by 1 | Viewed by 1352

Abstract

Vehicular communication is revolutionizing transportation by enhancing passenger experience and improving safety through seamless message exchanges with nearby vehicles and roadside units (RSUs). To accommodate the growing number of vehicles in dense urban traffic with limited channel availability, non-orthogonal multiple access (NOMA) is a promising solution due to its ability to improve spectral efficiency by sharing channels among multiple users. However, to completely leverage NOMA on mobile vehicular networks, a chain of operations and resources must be optimized, including vehicle user (VU) and RSU association, channel assignment, and optimal power control. In contrast, traditional orthogonal multiple access (OMA) allocates separate channels to users, simplifying management but falling short in high-density environments. Additionally, enabling NOMA-OMA switching can further enhance the system performance while significantly increasing the complexity of the optimization task. In this study, we propose an optimized framework to jointly utilize the power domain NOMA in a vehicular network, where dynamic NOMA-OMA switching is enabled, by integrating the optimization of vehicle-to-RSU association, channel assignment, NOMA-OMA switching, and transmit power allocation into a single solution. To handle the complexity of these operations, we also propose simplified formulations that make the solution practical for real-time applications. The proposed framework reduces total power consumption by up to

27 %

compared to Util&LB/opt, improves fairness in user association by

18 %

, and operates efficiently with minimal computational overhead. These findings highlight the potential of the proposed framework to enhance communication performance in dynamic, densely populated urban environments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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