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An Efficient and Scalable Internet of Things Framework for Smart Farming 高效、可扩展的智能农业物联网框架
IF 2.2
IEEE Sensors Letters Pub Date : 2024-10-09 DOI: 10.1109/LSENS.2024.3476940
Imad Jawhar;Samar Sindian;Sara Shreif;Mahmoud Ezzdine;Bilal Hammoud
{"title":"An Efficient and Scalable Internet of Things Framework for Smart Farming","authors":"Imad Jawhar;Samar Sindian;Sara Shreif;Mahmoud Ezzdine;Bilal Hammoud","doi":"10.1109/LSENS.2024.3476940","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3476940","url":null,"abstract":"Internet of Things (IoT) advancements have provided significant benefits to the agriculture sector in rationing water usage and monitoring the growth of vegetation. This article presents an efficient and scalable IoT framework for smart farming. It is based on a wireless sensor actuator network (WSAN) that logs the farm's environmental parameters into a network control center for processing and monitoring. Furthermore, a new addressing scheme for the WSAN nodes is proposed, which features the scalability of the proposed solution. To test and evaluate the architecture's performance, simulations are conducted to measure water consumption and time to network failure. Results confirm the efficiency and the reliability of the proposed scalable network as a proof of concept of the proposed work.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Portable and Flexible On-Road Sensing System for Traffic Monitoring 用于交通监控的便携式灵活路面传感系统
IF 2.2
IEEE Sensors Letters Pub Date : 2024-10-02 DOI: 10.1109/LSENS.2024.3473306
Naveen Kumar Gajingam;Sourav Karmakar;Aftab M. Hussain
{"title":"A Portable and Flexible On-Road Sensing System for Traffic Monitoring","authors":"Naveen Kumar Gajingam;Sourav Karmakar;Aftab M. Hussain","doi":"10.1109/LSENS.2024.3473306","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3473306","url":null,"abstract":"With an increasing number of vehicles on the road every day, intelligent traffic monitoring and control is essential. This entails development of cost-effective, scalable, and easy-to-install monitoring systems. In this letter, a versatile piezoresistance-based cost-effective on-road sensor system is presented to estimate vehicle speed and vehicle wheelbase length. The system consists of a velostat thin film sensing element placed on the road, with read out circuits and control electronics located at the sidewalk. The system measures the speed of a vehicle with 90.4% accuracy, and the length of the wheelbase with 94.3% accuracy. The wheelbase length can be used to classify the vehicle type. Our experiments show that the system is reliable, as the sensor output returns to the initial values after each vehicle passes. The utilization of flexible piezoresistive sensors makes this system convenient to deploy in different applications where basic traffic activity monitoring is required with speed, count, and classification estimation of vehicles.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning-Based Low-Cost Colorimetric Sensor for pH and Free-Chlorine Measurement 基于机器学习的低成本比色传感器用于 pH 值和游离氯测量
IF 2.2
IEEE Sensors Letters Pub Date : 2024-10-02 DOI: 10.1109/LSENS.2024.3473530
Chetanya Goyal;Shreya Malkurthi;Kirthi Vignan Reddy Yellakonda;Aftab M. Hussain
{"title":"Machine Learning-Based Low-Cost Colorimetric Sensor for pH and Free-Chlorine Measurement","authors":"Chetanya Goyal;Shreya Malkurthi;Kirthi Vignan Reddy Yellakonda;Aftab M. Hussain","doi":"10.1109/LSENS.2024.3473530","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3473530","url":null,"abstract":"Free-chlorine concentration monitoring is of importance in public and industrial water supplies. Current colorimetric methods, which include test strips, spectrophotometric kits, etc. either lack precision or are expensive and labor intensive. In this study, we present a fully automated, cost-effective method of measurement of free chlorine concentration in real -time. The setup includes an automatic powder dispenser, an automatic liquid dispenser, a sample chamber, and an LED-light-dependent resistor sensor pair. The liquid sample is mixed with a coloring reagent and its color is measured using the sensor pair. Different regression algorithms were trained on the sensor data and tuned to predict the corresponding free-chlorine concentration with maximum accuracy. The system eliminates the need for color matching, reduces the time taken per test, and can be used to predict concentrations of multiple analytes, including ammonia-nitrogen, dissolved oxygen, etc., by adding corresponding colorimetry agents. This allows for a fully automated, real-time water testing system.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancing General Sensor Data Synthesis by Integrating LLMs and Domain-Specific Generative Models 通过整合 LLM 和特定领域生成模型推进通用传感器数据合成
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-30 DOI: 10.1109/LSENS.2024.3470748
Xiaomao Zhou;Qingmin Jia;Yujiao Hu
{"title":"Advancing General Sensor Data Synthesis by Integrating LLMs and Domain-Specific Generative Models","authors":"Xiaomao Zhou;Qingmin Jia;Yujiao Hu","doi":"10.1109/LSENS.2024.3470748","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3470748","url":null,"abstract":"Synthetic data has become essential in machine learning and data science, addressing real-world data limitations such as scarcity, privacy, and cost. While existing generative models are effective in synthesizing various sensor data, they struggle with performance and generalization. This letter introduces a large language model (LLM)-driven framework that leverages LLMs and domain-specific generative models (DGMs) for general sensor data synthesis. Specifically, our method employs LLMs as the core to analyze data generation tasks, decompose complex tasks into manageable subtasks, and delegate each to the most suitable DGM, thereby automatically constructing customized data generation pipelines. Meanwhile, the integration of reinforcement learning (RL) is promising to enhance the framework's ability to optimally utilize DGMs, resulting in data generation with superior quality and control flexibility. Experimental results demonstrate the effectiveness of LLMs in understanding diverse tasks and in facilitating general sensor data synthesis through collaborative interactions with diverse DGMs.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
$mu$WSense: A Self-Sustainable Microwave-Powered Battery-Less Wireless Sensor Node for Temperature and Humidity Monitoring $mu$WSense：用于温湿度监测的可自我维持的微波供电无电池无线传感器节点
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-25 DOI: 10.1109/LSENS.2024.3468808
Vikas Kumar Malav;Ashwani Sharma
{"title":"$mu$WSense: A Self-Sustainable Microwave-Powered Battery-Less Wireless Sensor Node for Temperature and Humidity Monitoring","authors":"Vikas Kumar Malav;Ashwani Sharma","doi":"10.1109/LSENS.2024.3468808","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3468808","url":null,"abstract":"To realize a green Internet of Things (IoT) sensor network, batteryless wireless sensor nodes (WSNs) are required. This self-sustainability is achieved via energy harvesting from conventional renewable sources, such as solar and wind, which rely on the weather and are highly expensive. Alternatively, the microwave-based wireless power transfer technique is demonstrated previously, however, only for sensing operations without including the IoT. In this letter, a \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000Wave-powered WSN (\u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000Wsense) hardware is demonstrated to realize true batteryless IoT sensing applications. The \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000Wsense consists of a rectenna array (\u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000wave receiver), power management unit, and BLE module. The \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000 wave receiver is designed at 5.2 GHz to power \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000Wsense at a maximum measured transfer range of 2 m with a maximum real-time sensing interval of 75 s. The minimum harvested power \u0000<inline-formula><tex-math>$-16.59$</tex-math></inline-formula>\u0000 dBm is sufficient to operate the \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000Wsense.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiclass Object Classification Using Ultra-Low Resolution Time-of-Flight Sensors 利用超低分辨率飞行时间传感器进行多类物体分类
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-24 DOI: 10.1109/LSENS.2024.3467165
Andrea Fasolino;Paola Vitolo;Rosalba Liguori;Luigi Di Benedetto;Alfredo Rubino;Danilo Pau;Gian Domenico Licciardo
{"title":"Multiclass Object Classification Using Ultra-Low Resolution Time-of-Flight Sensors","authors":"Andrea Fasolino;Paola Vitolo;Rosalba Liguori;Luigi Di Benedetto;Alfredo Rubino;Danilo Pau;Gian Domenico Licciardo","doi":"10.1109/LSENS.2024.3467165","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3467165","url":null,"abstract":"Time-of-Flight (ToF) sensors are generally used in combination with red–blue–green sensors in image processing for adding the 3-D to 2-D scenes. Because of their low lateral resolution and contrast, they are scarcely used in object detection or classification. In this work, we demonstrate that ultra-low resolution (URL) ToF sensors with 8×8 pixels can be successfully used as stand-alone sensors for multiclass object detection even if combined with machine learning (ML) models, which can be implemented in a very compact and low-power custom circuit. Specifically, addressing an STMicroelectronics VL53L8CX 8×8 pixel ToF sensor, the designed ToF+ML system is capable to classify up to 10 classes with an overall mean accuracy of 90.21%. The resulting hardware architecture, prototyped on an AMD Xilinx Artix-7 field programmable gate array (FPGA), achieves an energy per inference consumption of 65.6 nJ and a power consumption of 1.095 \u0000<inline-formula><tex-math>$mu text{W}$</tex-math></inline-formula>\u0000 at the maximum output data rate of the sensor. These values are lower than the typical energy and power consumption of the sensor, enabling real-time postprocessing of depth images with significantly better performance than the state-of-the-art in the literature.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Physical Sensor Driven Approach for Optimizing Positive Airway Pressure Therapy 优化气道正压疗法的物理传感器驱动方法
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-20 DOI: 10.1109/LSENS.2024.3464673
Delpha J;Priyanka Kokil;Subramaniyan S;Jayanthi T
{"title":"Physical Sensor Driven Approach for Optimizing Positive Airway Pressure Therapy","authors":"Delpha J;Priyanka Kokil;Subramaniyan S;Jayanthi T","doi":"10.1109/LSENS.2024.3464673","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3464673","url":null,"abstract":"Obstructive sleep apnea (OSA) is a sleep disorder for which continuous positive airway pressure (CPAP) therapy is an effective treatment. In this study, a novel method to control the pressure in the positive airway pressure (PAP) device is proposed, which, in return, reduces the need to keep the subject at high pressure throughout extended period of their sleep without hindering the efficacy of the therapy. A standard CPAP/Bi-PAP (bilevel positive airway pressure) titration study is compared and verified with the predicted pressure values. Also, the relationship and correlation between weight, age, \u0000<inline-formula><tex-math>$SpO_{2}$</tex-math></inline-formula>\u0000, oxygen desaturation index, and the maximum pressure required for PAP therapy are also analyzed. Thus, it is affirmed that the PAP therapy compliance can be improved by sustaining the essential pressure and avoiding extended high-pressure intervals during therapy, unless they are absolutely required.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IEEE Sensors Letters Publication Information IEEE 传感器快报》出版信息
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-19 DOI: 10.1109/LSENS.2024.3463231
{"title":"IEEE Sensors Letters Publication Information","authors":"","doi":"10.1109/LSENS.2024.3463231","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3463231","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IEEE Sensors Letters Subject Categories for Article Numbering Information 用于文章编号信息的 IEEE 传感器快报主题类别
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-19 DOI: 10.1109/LSENS.2024.3463235
{"title":"IEEE Sensors Letters Subject Categories for Article Numbering Information","authors":"","doi":"10.1109/LSENS.2024.3463235","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3463235","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points 利用相邻时频点之间的角度相关性进行单源点检测以估算 DOA
IF 2.2
IEEE Sensors Letters Pub Date : 2024-09-19 DOI: 10.1109/LSENS.2024.3464515
Lu Li;Maoshen Jia;Dingding Yao
{"title":"Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points","authors":"Lu Li;Maoshen Jia;Dingding Yao","doi":"10.1109/LSENS.2024.3464515","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3464515","url":null,"abstract":"This letter proposes multisource direction-of-arrival (DOA) estimation using the correlation between angles of adjacent time– frequency (TF) points for a first-order ambisonics sensor array. For a TF point in the recorded signal, we define the adjacent TF points whose angles are close to that of this point as angle correlation points (ACPs) and then explore the relation between the probability that this point is a single-source point (SSP) and the number of ACPs. We found that there is a positive correlation between the number of ACPs and the probability that a point is an SSP. Hence, SSP detection is proposed using the angle correlation between adjacent TF points. In addition, 2-D weight kernel density estimation is designed to estimate the probability density of angles of detected SSPs. Finally, peak search is adopted for DOA estimation. Experiments in simulated and real environments show that the DOA estimation performance of the proposed method exceeds that of some existing methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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