<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Sensors | Sensor Networks section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/sensors/sections/sensor-networks</link>
        <description>RSS Feed for Sensor Networks section in the Frontiers in Sensors journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-13T13:02:54.672+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2026.1794293</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2026.1794293</link>
        <title><![CDATA[Correction: MobileNetV2-based classification of premium tea leaves for optimized production]]></title>
        <pubdate>2026-03-05T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Indrarini Dyah Irawati</author><author>Anyelia Adianggiali</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2026.1730414</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2026.1730414</link>
        <title><![CDATA[Voting mechanism for trustworthy localization in wireless sensor networks]]></title>
        <pubdate>2026-01-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Slavisa Tomic</author><author>Marko Beko</author><author>Dejan Vukobratovic</author><author>Srdjan Krco</author>
        <description><![CDATA[This work aspires to provide a trustworthy solution for target localization in adverse environments, where malicious nodes, capable of manipulating distance measurements (i.e., performing spoofing attacks), are present, thus hindering accurate localization. Besides localization, its other goal is to identify (detect) which of the nodes participating in the process are malicious. This problem becomes extremely important with the forthcoming expansion of IoT and smart cities applications, that depend on accurate localization, and the presence of malicious attackers can represent serious security threats if not taken into consideration. This is the case with most existing localization systems which makes them highly vulnerable to spoofing attacks. In addition, existing methods that are intended for adversarial settings consider very specific settings or require additional knowledge about the system model, making them only partially secure. Therefore, this work proposes a novel voting scheme based on clustering and weighted central mass to securely solve the localization problem and detect attackers. The proposed solution has two main phases: 1) Choosing a cluster of suitable points of interest by taking advantage of the problem geometry to assigning votes in order to localize the target, and 2) Attacker detection by exploiting the location estimate and basic statistics. The proposed method is assessed in terms of localization accuracy, success in attacker detection, and computational complexity in different settings. Computer simulations and real-world experiments corroborate the effectiveness of the proposed scheme compared to state-of-the-art methods, showing that it can accomplish an error reduction of 30 % and is capable of achieving almost perfect attacker detection rate when the ratio between attacker intensity and noise standard deviation is significant.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2025.1625488</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2025.1625488</link>
        <title><![CDATA[MobileNetV2-based classification of premium tea leaves for optimized production]]></title>
        <pubdate>2025-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Indrarini Dyah Irawati</author><author>Anyelia Adianggiali</author>
        <description><![CDATA[The agricultural sector in Indonesia is one of the sectors producing a variety of food crops including tea plants. Tea (Camellia sinensis) is one of the plants that is widely consumed by the world community. In particular, black tea is one type of tea that is in great demand in Indonesia. PT Perkebunan Nusantara (PTPN) VIII Kebun Rancabali is one of the companies that take part in producing black tea plants and produces around 30 tons of black tea per day. In its production, black tea plants go through various stages to be processed into quality tea powder. It is necessary to know in advance the quality of the black tea leaves themselves before entering the processing stage to produce quality tea products. Therefore, in this research, a system for quality classification on black tea plants using Convolutional Neural Network (CNN) based on MobileNetV2 architecture was created. Based on the test scenario, the use of Adam optimizer with learning rate 0.001 achieved the highest accuracy of 97% and RMSprop optimizer achieved 96% accuracy. This research uses a dataset of 2000 images, so the accuracy results obtained are expected to reflect more reliable model performance and better generalization capabilities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2024.1375034</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2024.1375034</link>
        <title><![CDATA[A semi-supervised anomaly detection strategy for drunk driving detection: a feasibility study]]></title>
        <pubdate>2024-06-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fouzi Harrou</author><author>K. Ramakrishna Kini</author><author>Muddu Madakyaru</author><author>Ying Sun</author>
        <description><![CDATA[Drunk driving poses a significant threat to road safety, necessitating effective detection methods to enhance preventive measures and ensure the well-being of road users. Recognizing the critical importance of identifying drunk driving incidents for public safety, this paper introduces an effective semi-supervised anomaly detection strategy. The proposed strategy integrates three key elements: Independent Component Analysis (ICA), Kantorovitch distance (KD), and double Exponentially Weighted Moving Average (DEWMA). ICA is used to handle non-gaussian and multivariate data, while KD is used to measure the dissimilarity between normal and abnormal events based on ICA features. The DEWMA is applied to KD charting statistics to detect changes in data and uses a nonparametric threshold to improve sensitivity. The primary advantage of this approach is its ability to perform anomaly detection without requiring labeled data. The study also used XGBoost for the later calculation of the SHAP (SHapley Additive exPlanations) values to identify the most important variables for detecting drunk driving behavior. The approach was evaluated using publicly available data from gas and temperature sensors, as well as digital cameras. The results showed that the proposed approach achieved an F1-score of 98% in detecting the driver’s drunk status, outperforming conventional PCA-based and ICA-based methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2022.1020202</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2022.1020202</link>
        <title><![CDATA[Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review]]></title>
        <pubdate>2022-11-03T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Chanthujan Chandrakumar</author><author>Raj Prasanna</author><author>Max Stephens</author><author>Marion Lara Tan</author>
        <description><![CDATA[Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motion-based EEW algorithms for generating EEW.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2022.998928</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2022.998928</link>
        <title><![CDATA[A review of agroforestry, precision agriculture, and precision livestock farming—The case for a data-driven agroforestry strategy]]></title>
        <pubdate>2022-09-23T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Elisa S. Ramil Brick</author><author>John Holland</author><author>Dimitris E. Anagnostou</author><author>Keith Brown</author><author>Marc P. Y. Desmulliez</author>
        <description><![CDATA[Agroforestry can be defined as an agroecosystem whereby soil is used holistically and synergistically by various stakeholders including farmers, livestock, and plants. As such, agroforestry offers numerous benefits that include conservation of biodiversity, regulation of pests and diseases, increased quality of soil, air and water, efficient cycling of nutrients, and resilience to climate change. Review of published studies in agroforestry shows however that research in this area could benefit from increased real-time, spatial and temporal measurements. This situation is to be contrasted with that of precision agriculture in monocultures and precision livestock farming where progress made in sensor systems has attracted considerable research interest. It is advocated in this review article that wireless sensor networks could also significantly impact agroforestry through the monitoring of the local real-time interactions that occur between the various components constituting agroforestry systems. This review article proposes therefore the new field of data-driven agroforestry which lies at the intersection of precision agriculture, precision livestock farming, permaculture, and agroforestry. Data-driven agroforestry has the potential to not only help farmers harness the interactions between the different components of an agroforestry system to their advantage but also shine light on fundamental interactions between soil, plants, trees, and livestock while offering a sustainable agricultural method beneficial to all agroforestry stakeholders.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2022.896299</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2022.896299</link>
        <title><![CDATA[Fault-Aware Adversary Attack Analyses and Enhancement for RRAM-Based Neuromorphic Accelerator]]></title>
        <pubdate>2022-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liuting Shang</author><author>Sungyong Jung</author><author>Fengjun Li</author><author>Chenyun Pan</author>
        <description><![CDATA[Neural networks have been widely deployed in sensor networks and IoT systems due to the advance in lightweight design and edge computing as well as emerging energy-efficient neuromorphic accelerators. However, adversary attack has raised a major threat against neural networks, which can be further enhanced by leveraging the natural hard faults in the neuromorphic accelerator that is based on resistive random access memory (RRAM). In this paper, we perform a comprehensive fault-aware attack analysis method for RRAM-based accelerators by considering five attack models based on a wide range of device- and circuit-level nonideal properties. The research on nonideal properties takes into account detailed hardware situations and provides a more accurate perspective on security. Compared to the existing adversary attack strategy that only leverages the natural fault, we propose an initiative attack based on two soft fault injection methods, which do not require a high-precision laboratory environment. In addition, an optimized fault-aware adversary algorithm is also proposed to enhance the attack effectiveness. The simulation results of an MNIST dataset on a classic convolutional neural network have shown that the proposed fault-aware adversary attack models and algorithms achieve a significant improvement in the attacking image classification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2022.850056</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2022.850056</link>
        <title><![CDATA[Hardware Security in Sensor and its Networks]]></title>
        <pubdate>2022-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammad Mezanur Rahman Monjur</author><author>Joseph Heacock</author><author>Joshua Calzadillas</author><author>MD Shaad Mahmud</author><author>John Roth</author><author>Kunal Mankodiya</author><author>Edward Sazonov</author><author>Qiaoyan Yu</author>
        <description><![CDATA[Sensor networks and IoT systems have been widely deployed in monitoring and controlling system. With its increasing utilization, the functionality and performance of sensor networks and their applications are not the only design aims; security issues in sensor networks attract more and more attentions. Security threats in sensor and its networks could be originated from various sectors: users in cyber space, security-weak protocols, obsolete network infrastructure, low-end physical devices, and global supply chain. In this work, we take one of the emerging applications, advanced manufacturing, as an example to analyze the security challenges in the sensor network. Presentable attacks—hardware Trojan attack, man-in-the-middle attack, jamming attack and replay attack—are examined in the context of sensing nodes deployed in a long-range wide-area network (LoRaWAN) for advanced manufacturing. Moreover, we analyze the challenges of detecting those attacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2021.751748</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2021.751748</link>
        <title><![CDATA[Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms]]></title>
        <pubdate>2022-01-11T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Fayez Gebali</author><author>Mohammad Mamun</author>
        <description><![CDATA[Physically unclonable functions (PUFs) are now an essential component for strengthening the security of Internet of Things (IoT) edge devices. These devices are an important component in many infrastructure systems such as telehealth, commerce, industry, etc. Traditionally these devices are the weakest link in the security of the system since they have limited storage, processing, and energy resources. Furthermore they are located in unsecured environments and could easily be the target of tampering and various types of attacks. We review in this work the structure of most salient types of PUF systems such as static RAM static random access memory (SRAM), ring oscillator (RO), arbiter PUFs, coating PUFs and dynamic RAM dynamic random access memory (DRAM). We discuss statistical models for the five most common types of PUFs and identify the main parameters defining their performance. We review some of the most recent algorithms that can be used to provide stable authentication and secret key generation without having to use helper data or secure sketch algorithms. Finally we provide results showing the performance of these devices and how they depend on the authentication algorithm used and the main system parameters.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsens.2021.700967</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsens.2021.700967</link>
        <title><![CDATA[Specialty Grand Challenge: Sensor Networks]]></title>
        <pubdate>2021-06-01T00:00:00Z</pubdate>
        <category>Specialty Grand Challenge</category>
        <author>Guangjie Han</author>
        <description></description>
      </item>
      </channel>
    </rss>