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Feature Papers in the 'Sensor Networks' Section 2024
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Feature Papers in the 'Sensor Networks' Section 2024

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4180

Special Issue Editors

Prof. Dr. Yuh-Shyan Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan
Interests: wireless sensor networks; fog computing for sensors; software-defined sensors; sensors with 5G/6G; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Yi

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China
Interests: multi-target tracking; sensor networks; resources management; multi-sensor information fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the “Sensor Networks” Section is now compiling a collection of papers submitted by the Section’s Editorial Board Members (EBMs) and outstanding scholars in this research field. We welcome contributions and recommendations from EBMs.

The Section covers theoretical and experimental problems, especially considering the rise of Internet of things (IoT) applications that allow several devices to connect in a smart way. In general, this Section aims to provide researchers with a platform on which to publish their scientific work that can influence the scientific community as well as the general public.

We would also like to take this opportunity to call on more excellent scholars to join the Sensor Networks Section so that we can work together to further develop this exciting field of research.

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

  • Smart sensor networks;
  • Power consumption/energy-harvesting sensor networks;
  • Energy-autonomous and low-power systems for the IoT;
  • Machine learning on sensors;
  • Cross-layer optimization;
  • Wireless sensor networks;
  • Routing protocols in sensor networks;
  • Embedded networked sensors;
  • Software-defined networks;
  • Underwater sensor networks;
  • Distributed sensor networks;
  • Ad hoc networks;
  • Industrial sensor networks;
  • Sensor network security, privacy, and threat detection;
  • Data calibration and fault tolerance;
  • Sensor network data fusion and data aggregation;
  • Sensor node localization;
  • Medium access control (MAC) protocols for sensor networks;
  • Artificial intelligence in sensor networks;
  • Edge computing in wireless sensor networks;
  • AI/ML for integrated sensing and communication;
  • Applications of sensor networks in area monitoring, healthcare monitoring, habitat monitoring, environmental/Earth sensing, etc.;
  • Advanced and intelligent sensor applications.

Prof. Dr. Yuh-Shyan Chen
Prof. Dr. Wei Yi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • sensor networks
  • wireless sensor networks
  • sensing and communication
  • IoT

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Related Special Issue

Published Papers (7 papers)

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Research

18 pages, 4816 KiB  
Article
Prototyping a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control
by Maria Papaioannou, Georgios Zachos, Georgios Mantas, Emmanouil Panaousis and Jonathan Rodriguez
Sensors 2024, 24(16), 5193; https://doi.org/10.3390/s24165193 - 11 Aug 2024
Viewed by 283
Abstract
As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling [...] Read more.
As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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23 pages, 1335 KiB  
Article
Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems
by Mekhla Sarkar and Prasan Kumar Sahoo
Sensors 2024, 24(15), 5076; https://doi.org/10.3390/s24155076 - 5 Aug 2024
Viewed by 296
Abstract
The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming [...] Read more.
The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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14 pages, 3208 KiB  
Article
Smart Industrial Internet of Things Framework for Composites Manufacturing
by Boon Xian Chai, Maheshi Gunaratne, Mohammad Ravandi, Jinze Wang, Tharun Dharmawickrema, Adriano Di Pietro, Jiong Jin and Dimitrios Georgakopoulos
Sensors 2024, 24(15), 4852; https://doi.org/10.3390/s24154852 - 26 Jul 2024
Viewed by 505
Abstract
Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred [...] Read more.
Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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16 pages, 8759 KiB  
Article
Normal-Incidence Germanium Photodetectors Integrated with Polymer Microlenses for Optical Fiber Communication Applications
by Yu-Hsuan Liu, Chia-Peng Lin, Po-Wei Chen, Chia-Tai Tsao, Chun-Chi Lin, Tsung-Ting Wu, Likarn Wang and Neil Na
Sensors 2024, 24(13), 4221; https://doi.org/10.3390/s24134221 - 29 Jun 2024
Viewed by 535
Abstract
We present a novel photon-acid diffusion method to integrate polymer microlenses (MLs) on a four-channel, high-speed photo-receiver consisting of normal-incidence germanium (Ge) p-i-n photodiodes (PDs) fabricated on a 200 mm Si substrate. For a 29 µm diameter PD capped with a 54 µm [...] Read more.
We present a novel photon-acid diffusion method to integrate polymer microlenses (MLs) on a four-channel, high-speed photo-receiver consisting of normal-incidence germanium (Ge) p-i-n photodiodes (PDs) fabricated on a 200 mm Si substrate. For a 29 µm diameter PD capped with a 54 µm diameter ML, its dark current, responsivity, 3 dB bandwidth (BW), and effective aperture size at −3 V bias and 850 nm wavelength are measured to be 138 nA, 0.6 A/W, 21.4 GHz, and 54 µm, respectively. The enlarged aperture size significantly decouples the tradeoff between aperture size and BW and enhances the optical fiber misalignment tolerance from ±5 µm to ±15 µm to ease the module packaging precision. The sensitivity of the photo-receiver is measured to be −9.2 dBm at 25.78 Gb/s with a bit error rate of 10−12 using non-return-to-zero (NRZ) transmission. Reliability tests are performed, and the results show that the fabricated Ge PDs integrated with polymer MLs pass the GR-468 reliability assurance standard. The demonstrated photo-receiver, a first of its kind to the best of our knowledge, features decent performance, high yield, high throughput, low cost, and compatibility with complementary metal-oxide-semiconductor (CMOS) fabrication processes, and may be further applied to 400 Gb/s pulse-amplitude modulation four-level (PAM4) communication. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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16 pages, 667 KiB  
Article
BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms
by Yongxin Fan, Qian Tang, Yangming Guo and Yifei Wei
Sensors 2024, 24(12), 3962; https://doi.org/10.3390/s24123962 - 19 Jun 2024
Viewed by 643
Abstract
This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed [...] Read more.
This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model’s understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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15 pages, 3882 KiB  
Article
A Dual-Stream Cross AGFormer-GPT Network for Traffic Flow Prediction Based on Large-Scale Road Sensor Data
by Yu Sun, Yajing Shi, Kaining Jia, Zhiyuan Zhang and Li Qin
Sensors 2024, 24(12), 3905; https://doi.org/10.3390/s24123905 - 17 Jun 2024
Viewed by 487
Abstract
Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data [...] Read more.
Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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20 pages, 1613 KiB  
Article
Energy-Efficient Edge and Cloud Image Classification with Multi-Reservoir Echo State Network and Data Processing Units
by E. J. López-Ortiz, M. Perea-Trigo, L. M. Soria-Morillo, J. A. Álvarez-García and J. J. Vegas-Olmos
Sensors 2024, 24(11), 3640; https://doi.org/10.3390/s24113640 - 4 Jun 2024
Viewed by 480
Abstract
In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning [...] Read more.
In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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