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.
{"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}
Pub Date : 2024-10-02DOI: 10.1109/LSENS.2024.3473306
Naveen Kumar Gajingam;Sourav Karmakar;Aftab M. Hussain
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.
{"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}
Pub Date : 2024-10-02DOI: 10.1109/LSENS.2024.3473530
Chetanya Goyal;Shreya Malkurthi;Kirthi Vignan Reddy Yellakonda;Aftab M. Hussain
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.
游离氯浓度监测对公共和工业供水非常重要。目前的比色法(包括试纸、分光光度法试剂盒等)要么精度不够,要么成本高昂且劳动强度大。在这项研究中,我们提出了一种全自动、经济高效的实时测量游离氯浓度的方法。该装置包括一个自动粉末分配器、一个自动液体分配器、一个样品室和一对 LED 光敏电阻传感器。液体样品与着色试剂混合后,使用传感器对测量其颜色。根据传感器数据训练不同的回归算法,并对其进行调整,以最准确地预测相应的游离氯浓度。该系统无需配色,减少了每次测试所需的时间,并可通过添加相应的比色剂来预测多种分析物的浓度,包括氨氮、溶解氧等。这就实现了全自动实时水检测系统。
{"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}
Pub Date : 2024-09-30DOI: 10.1109/LSENS.2024.3470748
Xiaomao Zhou;Qingmin Jia;Yujiao Hu
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.
{"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}
Pub Date : 2024-09-25DOI: 10.1109/LSENS.2024.3468808
Vikas Kumar Malav;Ashwani Sharma
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 $mu$