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Algorithms, Systems and Applications of Smart Sensor Networks
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Algorithms, Systems and Applications of Smart Sensor Networks

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

Deadline for manuscript submissions: closed (7 February 2024) | Viewed by 20490

Special Issue Editor

Dr. Raquel Caballero-Aguila

E-Mail Website
Guest Editor
Departamento de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
Interests: stochastic dynamical systems; random signal estimation; fusion estimation algorithms; discrete-time stochastic systems with network-induced uncertainties
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, networked systems have become a fertile and significant field of research, due to their large variety of applications in data acquisition and processing. As a result, significant accomplishments have been made in the design of new system models and algorithms, as well as in the development of new applications for sensor networks. This Special Issue aims to gather the most recent advances and approaches related to these topics, within the broad field of the fundamentals and applications of multi-sensor networked systems. Contributions from both theoretical and application sides are welcome, also accepting survey/tutorial manuscripts.

Potential topics include (but are not limited to):

  • Network configuration;
  • System design and mathematical models in sensor networks;
  • Processing of sensor data;
  • Signal and image processing;
  • Signal estimation over sensor networks;
  • Sensor network algorithms;
  • Multisensor information fusion and applications;
  • Sensor network applications.

Dr. Raquel Caballero-Aguila
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 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
  • network configuration
  • system design
  • signal processing
  • image processing
  • sensor network algorithms
  • multisensor information fusion
  • sensor network applications

Published Papers (16 papers)

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Research

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21 pages, 2104 KiB  
Article
Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces
by Alberto Martín-Martín, Rubén Padial-Allué, Encarnación Castillo, Luis Parrilla, Ignacio Parellada-Serrano, Alejandro Morán and Antonio García
Sensors 2024, 24(3), 899; https://doi.org/10.3390/s24030899 - 30 Jan 2024
Viewed by 923
Abstract
Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging [...] Read more.
Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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28 pages, 26972 KiB  
Article
Structural Health Monitoring of Chemical Storage Tanks with Application of PZT Sensors
by Michal Dziendzikowski, Paulina Kozera, Kamil Kowalczyk, Kamil Dydek, Milena Kurkowska, Zuzanna D. Krawczyk, Szczepan Gorbacz and Anna Boczkowska
Sensors 2023, 23(19), 8252; https://doi.org/10.3390/s23198252 - 05 Oct 2023
Viewed by 1229
Abstract
Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical [...] Read more.
Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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22 pages, 5623 KiB  
Article
Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction
by Yu-Hsuan Tseng and Chih-Yu Wen
Sensors 2023, 23(18), 7802; https://doi.org/10.3390/s23187802 - 11 Sep 2023
Cited by 1 | Viewed by 1434
Abstract
This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing [...] Read more.
This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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17 pages, 23732 KiB  
Article
A Study on the Effect of Temperature Variations on FPGA-Based Multi-Channel Time-to-Digital Converters
by Awwad H. Alshehry, Saleh M. Alshahry, Abdullah K. Alhazmi and Vamsy P. Chodavarapu
Sensors 2023, 23(18), 7672; https://doi.org/10.3390/s23187672 - 05 Sep 2023
Cited by 1 | Viewed by 1109
Abstract
We describe a study on the effect of temperature variations on multi-channel time-to-digital converters (TDCs). The objective is to study the impact of ambient thermal variations on the performance of field-programmable gate array (FPGA)-based tapped delay line (TDL) TDC systems while simultaneously meeting [...] Read more.
We describe a study on the effect of temperature variations on multi-channel time-to-digital converters (TDCs). The objective is to study the impact of ambient thermal variations on the performance of field-programmable gate array (FPGA)-based tapped delay line (TDL) TDC systems while simultaneously meeting the requirements of high-precision time measurement, low-cost implementation, small size, and low power consumption. For our study, we chose two devices, Artix-7 and ProASIC3L, manufactured by Xilinx and Microsemi, respectively. The radiation-tolerant ProASIC3L device offers better stability in terms of thermal sensitivity and power consumption compared to the Artix-7. To assess the performance of the TDCs under varying thermal conditions, a laboratory thermal chamber was utilized to maintain ambient temperatures ranging from −75 to 80 °C. This analysis ensured a comprehensive evaluation of the TDCs’ performance across a wide operational range. By utilizing the Artix-7 and ProASIC3L devices, we achieved root mean square (RMS) resolution of 24.7 and 554.59 picoseconds, respectively. Total on-chip power of 0.968 W was achieved using Artix-7, while 1.997 mW of power consumption was achieved using the ProASIC3L device. We worked to determine the temperature sensitivity for both FPGA devices, which could help in the design and optimization of FPGA-based TDCs for many applications. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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24 pages, 3766 KiB  
Article
An Enhanced Food Digestion Algorithm for Mobile Sensor Localization
by Shu-Chuan Chu, Zhi-Yuan Shao, Ning Zhong, Geng-Geng Liu and Jeng-Shyang Pan
Sensors 2023, 23(17), 7508; https://doi.org/10.3390/s23177508 - 29 Aug 2023
Viewed by 776
Abstract
Mobile sensors can extend the range of monitoring and overcome static sensors’ limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm [...] Read more.
Mobile sensors can extend the range of monitoring and overcome static sensors’ limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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16 pages, 1707 KiB  
Article
A Size, Weight, Power, and Cost-Efficient 32-Channel Time to Digital Converter Using a Novel Wave Union Method
by Saleh M. Alshahry, Awwad H. Alshehry, Abdullah K. Alhazmi and Vamsy P. Chodavarapu
Sensors 2023, 23(14), 6621; https://doi.org/10.3390/s23146621 - 23 Jul 2023
Cited by 1 | Viewed by 1236
Abstract
We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a [...] Read more.
We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a low-cost Field-Programmable Gate Array (FPGA), Artix-7, from Xilinx. Compared to prior works, our high-precision multi-channel TDC has the lowest SWaP-C requirements. We demonstrate an average time precision of less than 3 ps and a Root Mean Square resolution of about 1.81 ps. We propose a novel Wave Union type A architecture where only the first multiplexer is used to generate the wave union pulse train at the arrival of the start signal to minimize the required computational processing. In addition, an auto-calibration algorithm is proposed to help improve the TDC performance by improving the TDC Differential Non-Linearity and Integral Non-Linearity. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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24 pages, 1941 KiB  
Article
Integer Arithmetic Algorithm for Fundamental Frequency Identification of Oceanic Currents
by Juan Montiel-Caminos, Nieves G. Hernandez-Gonzalez, Javier Sosa and Juan A. Montiel-Nelson
Sensors 2023, 23(14), 6549; https://doi.org/10.3390/s23146549 - 20 Jul 2023
Cited by 1 | Viewed by 709
Abstract
Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other [...] Read more.
Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other hand, industrial and literature current meters work as raw data loggers, and most of the calculations to determine the fundamental frequencies are performed offline on a desktop computer or in the cloud. Belonging to the edge computing research area, this paper presents an algorithm to extract the fundamental frequencies of water currents in an underwater sensor network deployed in offshore aquaculture infrastructures. The target sensor node is based on a commercial ultra-low-power microcontroller. The proposed fundamental frequency identification algorithm only requires the use of an integer arithmetic unit. Our approach exploits the mathematical properties of the finite impulse response (FIR) filtering in the integer domain. The design and implementation of the presented algorithm are discussed in detail in terms of FIR tuning/coefficient selection, memory usage and variable domain for its mathematical formulation aimed at reducing the computational effort required. The approach is validated using a shallow water current model and real-world raw data from an offshore aquaculture infrastructure. The extracted frequencies have a maximum error below a 4%. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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16 pages, 2724 KiB  
Article
Q-Learning-Based Pending Zone Adjustment for Proximity Classification
by Jung-Hyok Kwon, Sol-Bee Lee and Eui-Jik Kim
Sensors 2023, 23(9), 4352; https://doi.org/10.3390/s23094352 - 28 Apr 2023
Viewed by 845
Abstract
This paper presents a Q-learning-based pending zone adjustment for received signal strength indicator (RSSI)-based proximity classification (QPZA). QPZA aims to improve the accuracy of RSSI-based proximity classification by adaptively adjusting the size of the pending zone, taking into account changes in the surrounding [...] Read more.
This paper presents a Q-learning-based pending zone adjustment for received signal strength indicator (RSSI)-based proximity classification (QPZA). QPZA aims to improve the accuracy of RSSI-based proximity classification by adaptively adjusting the size of the pending zone, taking into account changes in the surrounding environment. The pending zone refers to an area in which the previous result of proximity classification is maintained and is expressed as a near boundary and a far boundary. QPZA uses Q-learning to expand the size of the pending zone when the noise level increases and reduce it otherwise. Specifically, it calculates the noise level using the estimation error of a device deployed at a specific location. Then, QPZA adjusts the near boundary and far boundary separately by inputting the noise level into the near and far boundary adjusters, consisting of the Q-learning agent and reward calculator. The Q-learning agent determines the next boundary using the Q-table, and the reward calculator calculates the reward using the noise level. QPZA updates the Q-table of the Q-learning agent using the reward. To evaluate the performance of QPZA, we conducted an experimental implementation and compared the accuracy of QPZA with that of the existing approach. The results showed that QPZA achieves 11.69% higher accuracy compared to the existing approach, on average. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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20 pages, 936 KiB  
Article
An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts
by Rosa M. Fernández-Alcalá, José D. Jiménez-López, Nicolas Le Bihan and Clive Cheong Took
Sensors 2023, 23(8), 4047; https://doi.org/10.3390/s23084047 - 17 Apr 2023
Viewed by 850
Abstract
This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and [...] Read more.
This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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23 pages, 14661 KiB  
Article
A Non-Equal Time Interval Incremental Motion Prediction Method for Maritime Autonomous Surface Ships
by Zhijie Zhou, Haixiang Xu, Hui Feng and Wenjuan Li
Sensors 2023, 23(5), 2852; https://doi.org/10.3390/s23052852 - 06 Mar 2023
Cited by 1 | Viewed by 1250
Abstract
Recent technological advancements facilitate the autonomous navigation of maritime surface ships. The accurate data given by a range of various sensors serve as the primary assurance of a voyage’s safety. Nevertheless, as sensors have various sample rates, they cannot obtain information at the [...] Read more.
Recent technological advancements facilitate the autonomous navigation of maritime surface ships. The accurate data given by a range of various sensors serve as the primary assurance of a voyage’s safety. Nevertheless, as sensors have various sample rates, they cannot obtain information at the same time. Fusion decreases the accuracy and reliability of perceptual data if different sensor sample rates are not taken into account. Hence, it is helpful to increase the quality of the fusion information to precisely anticipate the motion status of ships at the sampling time of each sensor. This paper proposes a non-equal time interval incremental prediction method. In this method, the high dimensionality of the estimated state and nonlinearity of the kinematic equation are taken into consideration. First, the cubature Kalman filter is employed to estimate a ship’s motion at equal intervals based on the ship’s kinematic equation. Next, a ship motion state predictor based on a long short-term memory network structure is created, using the increment and time interval of the historical estimation sequence as the network input and the increment of the motion state at the projected time as the network output. The suggested technique can lessen the effect of the speed difference between the test set and the training set on the prediction accuracy compared with the traditional long short-term memory prediction method. Finally, comparison experiments are carried out to validate the precision and effectiveness of the proposed approach. The experimental results show that the root-mean-square error coefficient of the prediction error is decreased on average by roughly 78% for various modes and speeds when compared with the conventional non-incremental long short-term memory prediction approach. Additionally, the proposed prediction technology and the traditional approach have virtually the same algorithm times, which may fulfill the real engineering requirements. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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24 pages, 8259 KiB  
Article
A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks
by Shujie Yu, Jianping Zhu and Chunfeng Lv
Sensors 2023, 23(2), 782; https://doi.org/10.3390/s23020782 - 10 Jan 2023
Cited by 8 | Viewed by 1709
Abstract
Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum [...] Read more.
Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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19 pages, 1410 KiB  
Article
Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks
by Yimin Ma and Shuli Sun
Sensors 2023, 23(1), 335; https://doi.org/10.3390/s23010335 - 28 Dec 2022
Cited by 8 | Viewed by 1197
Abstract
In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack [...] Read more.
In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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21 pages, 4521 KiB  
Article
Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems
by Ivo Silva, Cristiano Pendão and Adriano Moreira
Sensors 2022, 22(22), 8585; https://doi.org/10.3390/s22228585 - 08 Nov 2022
Cited by 2 | Viewed by 1365
Abstract
Indoor positioning and navigation have been attracting interest from the research community for quite some time. Nowadays, new fields, such as the Internet of Things, Industry 4.0, and augmented reality, are increasing the demand for indoor positioning solutions capable of delivering specific positioning [...] Read more.
Indoor positioning and navigation have been attracting interest from the research community for quite some time. Nowadays, new fields, such as the Internet of Things, Industry 4.0, and augmented reality, are increasing the demand for indoor positioning solutions capable of delivering specific positioning performances not only in simulation but also in the real world; hence, validation in real-world environments is essential. However, collecting real-world data is a time-consuming and costly endeavor, and many research teams lack the resources to perform experiments across different environments, which are required for high-quality validation. Publicly available datasets are a solution that provides the necessary resources to perform this type of validation and to promote research work reproducibility. Unfortunately, for different reasons, and despite some initiatives promoting data sharing, the number and diversity of datasets available are still very limited. In this paper, we introduce and describe a new public dataset which has the unique characteristic of being collected over a long period (2+ years), and it can be used for different Wi-Fi-based positioning studies. In addition, we also describe the solution (Wireless Sensor Network (WSN) + mobile unit) developed to collect this dataset, allowing researchers to replicate the method and collect similar datasets in other spaces. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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21 pages, 626 KiB  
Article
Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses
by Raquel Caballero-Águila, Jun Hu and Josefa Linares-Pérez
Sensors 2022, 22(21), 8505; https://doi.org/10.3390/s22218505 - 04 Nov 2022
Cited by 4 | Viewed by 990
Abstract
Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of [...] Read more.
Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of the estimators substantially. Thus, the development of estimation algorithms accounting for these random phenomena has received a lot of research attention. In this paper, the centralized fusion linear estimation problem is discussed under the assumption that the sensor measurements are affected by random parameter matrices, perturbed by time-correlated additive noises, exposed to random deception attacks and subject to random packet dropouts during transmission. A covariance-based methodology and two compensation strategies based on measurement prediction are used to design recursive filtering and fixed-point smoothing algorithms. The measurement differencing method—typically used to deal with the measurement noise time-correlation—is unsuccessful for these kinds of systems with packet losses because some sensor measurements are randomly lost and, consequently, cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of the measurement noises and the innovation technique. The two proposed compensation scenarios are contrasted through a simulation example, in which the effect of the different uncertainties on the estimation accuracy is also evaluated. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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17 pages, 2231 KiB  
Article
Globally Optimal Distributed Fusion Filter for Descriptor Systems with Time-Correlated Measurement Noises
by Jing Ma and Liling Xu
Sensors 2022, 22(19), 7469; https://doi.org/10.3390/s22197469 - 02 Oct 2022
Cited by 1 | Viewed by 1307
Abstract
This paper concerns the distributed fusion filtering problem for descriptor systems with time-correlated measurement noises. The original descriptor is transformed into two reduced-order subsystems (ROSs) based on singular value decomposition. For the first ROS, a new measurement is obtained using measurement difference technology. [...] Read more.
This paper concerns the distributed fusion filtering problem for descriptor systems with time-correlated measurement noises. The original descriptor is transformed into two reduced-order subsystems (ROSs) based on singular value decomposition. For the first ROS, a new measurement is obtained using measurement difference technology. Each sensor produces a local filter based on the fusion predictor from the fusion center and its own new measurement and then sends it to the fusion center. In the fusion center, based on local filters, a distributed fusion filter with feedback (DFFWF) in the linear minimum variance (LMV) sense is proposed by applying an innovative approach. The DFFWF for the second ROS is also obtained based on the DFFWF for the first ROS. Then, the DFFWF for the original descriptor is obtained. The proposed DFFWF can achieve the same estimation accuracy as the centralized fusion filter (CFF) under the condition that all local filter gain matrices are of full column rank. Its optimality is strictly proved. Moreover, it has robustness and reliability due to the parallel processing of local filters. Two simulation examples demonstrate the effectiveness of the developed fusion algorithm. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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Review

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24 pages, 5452 KiB  
Review
IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic
by Jamal Al-Nabulsi, Nidal Turab, Hamza Abu Owida, Bassam Al-Naami, Roberto De Fazio and Paolo Visconti
Sensors 2023, 23(16), 7193; https://doi.org/10.3390/s23167193 - 15 Aug 2023
Cited by 1 | Viewed by 2053
Abstract
A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection [...] Read more.
A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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