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J. Sens. Actuator Netw., Volume 13, Issue 4 (August 2024) – 6 articles

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46 pages, 5284 KiB

Open AccessReview

Recent Studies on Smart Textile-Based Wearable Sweat Sensors for Medical Monitoring: A Systematic Review

by Asma Akter, Md Mehedi Hasan Apu, Yedukondala Rao Veeranki, Turki Nabieh Baroud and Hugo F. Posada-Quintero

J. Sens. Actuator Netw. 2024, 13(4), 40; https://doi.org/10.3390/jsan13040040 - 11 Jul 2024

Viewed by 350

Abstract

Smart textile-based wearable sweat sensors have recently received a lot of attention due to their potential for use in personal medical monitoring. They have a variety of desirable qualities, including low cost, easy implementation, stretchability, flexibility, and light weight. Wearable sweat sensors are a potential approach for personalized medical devices because of these features. Moreover, real-time textile-based sweat sensors can easily monitor health by analyzing the sweat produced by the human body. We reviewed the most recent advancements in wearable sweat sensors from the fabrication, materials, and disease detection and monitoring perspectives. To integrate real-time biosensors with electronics and introduce advancements to the field of wearable technology, key chemical constituents of sweat, sweat collection technologies, and concerns of textile substrates are elaborated. Perspectives for building wearable biosensing systems based on sweat are reviewed, as well as the methods and difficulties involved in enhancing wearable sweat-sensing performance. Full article

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26 pages, 2233 KiB

Open AccessReview

Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management

by Fernando Fuentes-Peñailillo, Karen Gutter, Ricardo Vega and Gilda Carrasco Silva

J. Sens. Actuator Netw. 2024, 13(4), 39; https://doi.org/10.3390/jsan13040039 - 8 Jul 2024

Viewed by 675

Abstract

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. Full article

(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)

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20 pages, 1146 KiB

Open AccessArticle

Distributed Consensus Multi-Distribution Filter for Heavy-Tailed Noise

by Guan-Nan Chang, Wen-Xing Fu, Tao Cui, Ling-Yun Song and Peng Dong

J. Sens. Actuator Netw. 2024, 13(4), 38; https://doi.org/10.3390/jsan13040038 - 28 Jun 2024

Viewed by 306

Abstract

Distributed state estimation is one of the critical technologies in the field of target tracking, where the process noise and measurement noise may have a heavy-tailed distribution. Traditionally, heavy-tailed distributions like the student-t distribution are employed, but our observation reveals that Gaussian noise predominates in many instances, with occasional outliers. This sporadic reliance on heavy-tailed distributions can degrade performances or necessitate frequent parameter adjustments. To overcome this, we introduce a novel distributed consensus multi-distribution state estimation method that combines Gaussian and student-t filters. Our approach establishes a system model using both Gaussian and student-t distributions. We derive a multi-distribution filter for a single sensor, assigning probabilities to Gaussian and student-t noise models. Parallel estimation under both distributions, utilizing Gaussian and student-t filters, allows us to calculate the likelihood of each distribution. The fusion of these results yields a mixed-state estimation and corresponding error matrix. Recognizing the increasing degrees of freedom in the student-t distribution over time, we provide an effective approximation. An information consensus strategy for multi-distribution filters is introduced, achieving global estimation through consensus on fused local filter results via interaction with neighboring nodes. This methodology is extended to the distributed case, and the recursive process of the distributed multi-distribution consensus state estimation method is presented. Simulation results demonstrate that the estimation accuracy of the proposed algorithm improved by at least 20% compared to that of the traditional algorithm in scenarios involving both Gaussian and heavy-tailed distributions. Full article

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19 pages, 7416 KiB

Open AccessArticle

Beta Maximum Power Extraction Operation-Based Model Predictive Current Control for Linear Induction Motors

by Mohamed. A. Ghalib, Samir A. Hamad, Mahmoud F. Elmorshedy, Dhafer Almakhles and Hazem Hassan Ali

J. Sens. Actuator Netw. 2024, 13(4), 37; https://doi.org/10.3390/jsan13040037 - 28 Jun 2024

Viewed by 365

Abstract

There is an increasing interest in achieving global climate change mitigation targets that target environmental protection. Therefore, electric vehicles (as linear metros) were developed to avoid greenhouse gas emissions, which negatively impact the climate. Hence, this paper proposes a finite set-model predictive-based current control (FS-MPCC) strategy of linear induction motor (LIM) for linear metro drives fed by solar cells with a beta maximum power extraction (B-MPE) control approach to achieve lower thrust ripples and eliminate a selection of weighting factors, the main limitation of conventional model predictive-based thrust control (which can be time consuming and challenging). The B-MPE control approach ensures that the solar cells operate at their maximum power output, maximizing the energy harvested from the sun. Considering a single cost function of primary current errors between the predicted values and their references in αβ-axes, the proposed method eliminates the need for weighting factor selection, thus simplifying the control process. A comparison between the conventional and the presented control method is conducted using MATLAB/Simulink under different scenarios. Comprehensive simulation results of the presented system on a 3 kW LIM prototype revealed that the introduced system based on FS-MPCC surpasses the conventional technique, resulting in a maximum power extraction from solar cells and a suppression of the thrust ripples as well as an avoidance of weighting factor tuning, leading to fewer computational steps. Full article

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18 pages, 1222 KiB

Open AccessArticle

A Nature-Inspired Partial Distance-Based Clustering Algorithm

by Mohammed El Habib Kahla, Mounir Beggas, Abdelkader Laouid and Mohammad Hammoudeh

J. Sens. Actuator Netw. 2024, 13(4), 36; https://doi.org/10.3390/jsan13040036 - 21 Jun 2024

Viewed by 473

Abstract

In the rapidly advancing landscape of digital technologies, clustering plays a critical role in the domains of artificial intelligence and big data. Clustering is essential for extracting meaningful insights and patterns from large, intricate datasets. Despite the efficacy of traditional clustering techniques in handling diverse data types and sizes, they encounter challenges posed by the increasing volume and dimensionality of data, as well as the complex structures inherent in high-dimensional spaces. This research recognizes the constraints of conventional clustering methods, including sensitivity to initial centroids, dependence on prior knowledge of cluster counts, and scalability issues, particularly in large datasets and Internet of Things implementations. In response to these challenges, we propose a K-level clustering algorithm inspired by the collective behavior of fish locomotion. K-level introduces a novel clustering approach based on greedy merging driven by distances in stages. This iterative process efficiently establishes hierarchical structures without the need for exhaustive computations. K-level gives users enhanced control over computational complexity, enabling them to specify the number of clusters merged simultaneously. This flexibility ensures accurate and efficient hierarchical clustering across diverse data types, offering a scalable solution for processing extensive datasets within a reasonable timeframe. The internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, are utilized to evaluate the K-level algorithm across various types of datasets. Additionally, comparisons are made with rivals in the literature, including UPGMA, CLINK, UPGMC, SLINK, and K-means. The experiments and analyses show that the proposed algorithm overcomes many of the limitations of existing clustering methods, presenting scalable and adaptable clustering in the dynamic landscape of evolving data challenges. Full article

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26 pages, 10517 KiB

Open AccessArticle

Estimation of Vehicle Traffic Parameters Using an Optical Distance Sensor for Use in Smart City Road Infrastructure

by Rafał Burdzik, Ireneusz Celiński, Minvydas Ragulskis, Vinayak Ranjan and Jonas Matijošius

J. Sens. Actuator Netw. 2024, 13(4), 35; https://doi.org/10.3390/jsan13040035 - 21 Jun 2024

Viewed by 392

Abstract

In recent decades, the dynamics of road vehicle traffic have significantly evolved, compelling traffic engineers to develop innovative traffic monitoring solutions, especially for dense road networks. Traditional methods for measuring traffic volume along road sections may no longer suffice for modern traffic control systems. This is particularly true for induction loops, a widely used method since the last century. In contrast, measuring techniques using microwaves or visible light offer better accuracy but are often hindered by the high cost of sensors. This paper presents new techniques for measuring traffic flow and other parameters that adapt to changing traffic dynamics using low-cost optical distance sensors. Our study demonstrates that the integration of multiple monitoring approaches enhances measurement accuracy, contingent on the dynamics and specific characteristics of the traffic. The results indicate that cheap optical distance sensors are particularly well suited for use in smart city road networks. Full article

(This article belongs to the Section Network Services and Applications)

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