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Feature Papers in the 'Sensor Networks' Section 2023

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 17323

Special Issue Editors


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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

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Guest Editor
Departament of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: design and testing of IoT-based electronic systems; smart remote control of facilities; electronic systems for automation and automotive; energy harvesting systems for sensors nodes; wearable devices for health monitoring; new materials and advanced sensors
Special Issues, Collections and Topics in MDPI journals

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 Section “Sensor Networks” 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 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:

  • Smart sensor networks;
  • Power consumption/energy harvesting sensor networks;
  • Energy-autonomous and low-power systems for 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;
  • Integrated sensing and communication;
  • Applications of sensor networks on area monitoring, health care monitoring, habitat monitoring, environmental/Earth sensing, etc.

Prof. Dr. Yuh-Shyan Chen
Dr. Paolo Visconti
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.

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Published Papers (10 papers)

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Research

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18 pages, 1548 KiB  
Article
Insights into Object Semantics: Leveraging Transformer Networks for Advanced Image Captioning
by Deema Abdal Hafeth and Stefanos Kollias
Sensors 2024, 24(6), 1796; https://doi.org/10.3390/s24061796 - 11 Mar 2024
Viewed by 478
Abstract
Image captioning is a technique used to generate descriptive captions for images. Typically, it involves employing a Convolutional Neural Network (CNN) as the encoder to extract visual features, and a decoder model, often based on Recurrent Neural Networks (RNNs), to generate the captions. [...] Read more.
Image captioning is a technique used to generate descriptive captions for images. Typically, it involves employing a Convolutional Neural Network (CNN) as the encoder to extract visual features, and a decoder model, often based on Recurrent Neural Networks (RNNs), to generate the captions. Recently, the encoder–decoder architecture has witnessed the widespread adoption of the self-attention mechanism. However, this approach faces certain challenges that require further research. One such challenge is that the extracted visual features do not fully exploit the available image information, primarily due to the absence of semantic concepts. This limitation restricts the ability to fully comprehend the content depicted in the image. To address this issue, we present a new image-Transformer-based model boosted with image object semantic representation. Our model incorporates semantic representation in encoder attention, enhancing visual features by integrating instance-level concepts. Additionally, we employ Transformer as the decoder in the language generation module. By doing so, we achieve improved performance in generating accurate and diverse captions. We evaluated the performance of our model on the MS-COCO and novel MACE datasets. The results illustrate that our model aligns with state-of-the-art approaches in terms of caption generation. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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18 pages, 9451 KiB  
Article
Lessons Learnt from Monitoring the Etna Volcano Using an IoT Sensor Network through a Period of Intense Eruptive Activity
by Laurent Royer, Luca Terray, Maxime Rubéo-Lisa, Julien Sudre, Pierre-Jean Gauthier, Alexandre Claude, Salvatore Giammanco, Emilio Pecora, Paolo Principato and Vincent Breton
Sensors 2024, 24(5), 1577; https://doi.org/10.3390/s24051577 - 29 Feb 2024
Viewed by 531
Abstract
This paper describes the successes and failures after 4 years of continuous operation of a network of sensors, communicating nodes, and gateways deployed on the Etna Volcano in Sicily since 2019, including a period of Etna intense volcanic activity that occurred in 2021 [...] Read more.
This paper describes the successes and failures after 4 years of continuous operation of a network of sensors, communicating nodes, and gateways deployed on the Etna Volcano in Sicily since 2019, including a period of Etna intense volcanic activity that occurred in 2021 and resulted in over 60 paroxysms. It documents how the installation of gateways at medium altitude allowed for data collection from sensors up to the summit craters. Most of the sensors left on the volcanic edifice during winters and during this period of intense volcanic activity were destroyed, but the whole gateway infrastructure remained fully operational, allowing for a very fruitful new field campaign two years later, in August 2023. Our experience has shown that the best strategy for IoT deployment on very active and/or high-altitude volcanoes like Etna is to permanently install gateways in areas where they are protected both from meteorological and volcanic hazards, that is mainly at the foot of the volcanic edifice, and to deploy temporary sensors and communicating nodes in the more exposed areas during field trips or in the summer season. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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17 pages, 309 KiB  
Article
Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network
by Thibaut Vandervelden, Diana Deac, Roald Van Glabbeek, Ruben De Smet, An Braeken and Kris Steenhaut
Sensors 2024, 24(1), 73; https://doi.org/10.3390/s24010073 - 22 Dec 2023
Cited by 1 | Viewed by 776
Abstract
The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In [...] Read more.
The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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18 pages, 805 KiB  
Article
Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and Communication Systems Using Deep Reinforcement Learning Algorithm
by Liu Yang, Yifei Wei and Xiaojun Wang
Sensors 2023, 23(24), 9896; https://doi.org/10.3390/s23249896 - 18 Dec 2023
Viewed by 886
Abstract
This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of [...] Read more.
This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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16 pages, 2895 KiB  
Article
Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning
by Muhammad Hashir Syed, Maninderpal Singh and Joseph Camp
Sensors 2023, 23(23), 9524; https://doi.org/10.3390/s23239524 - 30 Nov 2023
Viewed by 697
Abstract
UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of [...] Read more.
UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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25 pages, 4648 KiB  
Article
Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning
by Cedric De Cock, Emmeric Tanghe, Wout Joseph and David Plets
Sensors 2023, 23(19), 8289; https://doi.org/10.3390/s23198289 - 07 Oct 2023
Cited by 1 | Viewed by 936
Abstract
Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the [...] Read more.
Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body–tag–anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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21 pages, 3367 KiB  
Article
Human Micro-Expressions in Multimodal Social Behavioral Biometrics
by Zaman Wahid, A. S. M. Hossain Bari and Marina Gavrilova
Sensors 2023, 23(19), 8197; https://doi.org/10.3390/s23198197 - 30 Sep 2023
Viewed by 795
Abstract
The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric [...] Read more.
The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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20 pages, 1857 KiB  
Article
Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications
by Ales Povalac, Jan Kral, Holger Arthaber, Ondrej Kolar and Marek Novak
Sensors 2023, 23(17), 7333; https://doi.org/10.3390/s23177333 - 22 Aug 2023
Cited by 4 | Viewed by 2187
Abstract
In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies [...] Read more.
In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark’s LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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25 pages, 4451 KiB  
Article
A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons
by Nikolaos Giamarelos, Myron Papadimitrakis, Marios Stogiannos, Elias N. Zois, Nikolaos-Antonios I. Livanos and Alex Alexandridis
Sensors 2023, 23(12), 5436; https://doi.org/10.3390/s23125436 - 08 Jun 2023
Cited by 3 | Viewed by 1557
Abstract
The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network [...] Read more.
The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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Review

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26 pages, 1198 KiB  
Review
Advancements in Forest Fire Prevention: A Comprehensive Survey
by Francesco Carta, Chiara Zidda, Martina Putzu, Daniele Loru, Matteo Anedda and Daniele Giusto
Sensors 2023, 23(14), 6635; https://doi.org/10.3390/s23146635 - 24 Jul 2023
Cited by 7 | Viewed by 7535
Abstract
Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems [...] Read more.
Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2023)
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