Pub Date : 2025-02-08DOI: 10.1016/j.measen.2025.101816
Roland Nagy , István Szalai
Vibrations in road vehicles related to road surface damage have a number of harmful consequences for the health of the occupants and for the components of the vehicle. To mitigate these effects and support timely pavement repairs, continuous road condition monitoring is essential. Vibration-based measurement systems have gained prominence in recent years, but their accuracy can be significantly compromised by vehicle maneuvers, particularly on urban or curvy roads. Despite this, the influence of aggressive maneuvers has largely been overlooked in previous studies. In this paper, we address this gap by presenting a comprehensive investigation into the impact of abrupt maneuvers on vibration-based road quality measurement. We introduce a novel, computationally efficient soft-sensor algorithm that detects and isolates aggressive maneuvers using sensor data from existing road quality measurement systems, classifying them into four categories. This algorithm combines rule-based methods with machine learning, offering enhanced accuracy and lower computational costs compared to alternative approaches. In this way, the overall maneuver classification achieves an accuracy of 93%. By applying the introduced approach to identify and correct the influence of maneuvers, we achieved a 7% increase in accuracy of pavement quality classification in a suburban environment and a 10% increase in an urban environment. The proposed solution can be easily integrated into current vibration-based road quality measurement frameworks, enhancing their performance while maintaining scalability and low operational cost.
{"title":"Vehicle maneuver recognition and correction algorithm for road quality measurement system optimization","authors":"Roland Nagy , István Szalai","doi":"10.1016/j.measen.2025.101816","DOIUrl":"10.1016/j.measen.2025.101816","url":null,"abstract":"<div><div>Vibrations in road vehicles related to road surface damage have a number of harmful consequences for the health of the occupants and for the components of the vehicle. To mitigate these effects and support timely pavement repairs, continuous road condition monitoring is essential. Vibration-based measurement systems have gained prominence in recent years, but their accuracy can be significantly compromised by vehicle maneuvers, particularly on urban or curvy roads. Despite this, the influence of aggressive maneuvers has largely been overlooked in previous studies. In this paper, we address this gap by presenting a comprehensive investigation into the impact of abrupt maneuvers on vibration-based road quality measurement. We introduce a novel, computationally efficient soft-sensor algorithm that detects and isolates aggressive maneuvers using sensor data from existing road quality measurement systems, classifying them into four categories. This algorithm combines rule-based methods with machine learning, offering enhanced accuracy and lower computational costs compared to alternative approaches. In this way, the overall maneuver classification achieves an accuracy of 93%. By applying the introduced approach to identify and correct the influence of maneuvers, we achieved a 7% increase in accuracy of pavement quality classification in a suburban environment and a 10% increase in an urban environment. The proposed solution can be easily integrated into current vibration-based road quality measurement frameworks, enhancing their performance while maintaining scalability and low operational cost.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101816"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376889","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}
Pub Date : 2025-02-05DOI: 10.1016/j.measen.2025.101817
S.Z. Islami rad , R. Gholipour Peyvandi
Radiation sources are used for measurement and control of industrial processes, determining the height of materials inside the vessel, analyzing the composition and structure of materials, and detecting defects in industrial processes due to the complexity of the production process. In petrochemical industries, the height of urea in a vessel can be measured using the nuclear level gauging method, which is a non-destructive technique. Therefore, the energy of the gamma emitting source, the design, and arrangement of the source geometry (including the point or rod sources), and the detector material (NaI (Tl) crystal or plastic scintillator) are crucial parameters. In this research, a nuclear level gauge, including the source, detector, and reactor containing urea and gases at high temperatures and pressures, was simulated by MCNPX Monte Carlo code and the results were compared and validated with experimental values. Then, the detector's response was evaluated and optimized based on the different arrangements of the radioactive source and its distances, as well as the type and geometry of the detector, and the best arrangement was selected. The comparison of the simulation results and the resulting analysis indicated that using point sources at specific distances (three points) instead of rod sources is a viable alternative due to its simpler structure, higher accuracy and stability, and lower production cost compared to the high cost of rod sources. Additionally, for accurate level and height gauging, rod detectors should be replaced with point detectors.
{"title":"A simple and inexpensive design of radioactive source for accurate level and height gauging in petrochemical industries","authors":"S.Z. Islami rad , R. Gholipour Peyvandi","doi":"10.1016/j.measen.2025.101817","DOIUrl":"10.1016/j.measen.2025.101817","url":null,"abstract":"<div><div>Radiation sources are used for measurement and control of industrial processes, determining the height of materials inside the vessel, analyzing the composition and structure of materials, and detecting defects in industrial processes due to the complexity of the production process. In petrochemical industries, the height of urea in a vessel can be measured using the nuclear level gauging method, which is a non-destructive technique. Therefore, the energy of the gamma emitting source, the design, and arrangement of the source geometry (including the point or rod sources), and the detector material (NaI (Tl) crystal or plastic scintillator) are crucial parameters. In this research, a nuclear level gauge, including the source, detector, and reactor containing urea and gases at high temperatures and pressures, was simulated by MCNPX Monte Carlo code and the results were compared and validated with experimental values. Then, the detector's response was evaluated and optimized based on the different arrangements of the radioactive source and its distances, as well as the type and geometry of the detector, and the best arrangement was selected. The comparison of the simulation results and the resulting analysis indicated that using point sources at specific distances (three points) instead of rod sources is a viable alternative due to its simpler structure, higher accuracy and stability, and lower production cost compared to the high cost of rod sources. Additionally, for accurate level and height gauging, rod detectors should be replaced with point detectors.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101817"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386257","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}
Pub Date : 2025-02-01DOI: 10.1016/j.measen.2024.101803
Yin Lu, Yuntian Huang, Hao Guo
This work aims to address the current sensing issue in a three-phase bridge inverter circuit and discuss a motor control method based on single-sensor phase current reconstruction. By collecting the motor's current signals and utilizing signal processing techniques such as Fourier transform and wavelet transform, information about the three-phase currents is extracted from the data of a single sensor. Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. Software tools such as MATLAB and LabVIEW are used for data processing and analysis in the implementation process. An experimental platform is set up to verify the accuracy and real-time performance of the reconstruction method. The experimental results indicate that employing the Mixed Space Vector Pulse Width Modulation (MSVPWM) control strategy reduces the reconstruction error from the original e = 3.5 % to e = 3.1 %. The current transition is smooth throughout the vector plane, and even in unobservable regions, the phase current can be accurately reconstructed. The motor control method based on single-sensor phase current reconstruction exhibits high accuracy and real-time performance, meeting practical requirements for motor control. In conclusion, this work provides technical support and a theoretical basis for the precise control of motors.
{"title":"Motor control method using single-sensor phase current reconstruction","authors":"Yin Lu, Yuntian Huang, Hao Guo","doi":"10.1016/j.measen.2024.101803","DOIUrl":"10.1016/j.measen.2024.101803","url":null,"abstract":"<div><div>This work aims to address the current sensing issue in a three-phase bridge inverter circuit and discuss a motor control method based on single-sensor phase current reconstruction. By collecting the motor's current signals and utilizing signal processing techniques such as Fourier transform and wavelet transform, information about the three-phase currents is extracted from the data of a single sensor. Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. Software tools such as MATLAB and LabVIEW are used for data processing and analysis in the implementation process. An experimental platform is set up to verify the accuracy and real-time performance of the reconstruction method. The experimental results indicate that employing the Mixed Space Vector Pulse Width Modulation (MSVPWM) control strategy reduces the reconstruction error from the original e = 3.5 % to e = 3.1 %. The current transition is smooth throughout the vector plane, and even in unobservable regions, the phase current can be accurately reconstructed. The motor control method based on single-sensor phase current reconstruction exhibits high accuracy and real-time performance, meeting practical requirements for motor control. In conclusion, this work provides technical support and a theoretical basis for the precise control of motors.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101803"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145246","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}
Pub Date : 2025-02-01DOI: 10.1016/j.measen.2024.101404
Johannes Braun, Faouzi Derbel
This article proposes a wireless sensor network (WSN) for fire alarm systems that leverages network coding to enhance system reliability and security. The proposed WSN is designed to comply with European standards, with a particular focus on German standards, and enables the deployment of sensor nodes that automatically construct a robust and reliable network. The self-healing, redundant, decentralized routed network ensures uninterrupted functionality in case of a communication failure. The network coding technique is utilized to manage the surge in data traffic during hazard situations, reducing the overall number of telegrams while maintaining data integrity and reliability. By implementing network coding, the proposed WSN reduces energy consumption and enhances the efficiency and reliability of fire alarm systems, thereby contributing to greater safety in emergency situations. To further leverage the advantages of network coding, a new decentralized routing technique is introduced. This technique operates locally on nodes through the use of virtual cluster heads and an innovative Weighted Composite Value (WCV) Table, optimizing routing decisions for improved performance and scalability. This article provides a comprehensive exploration of the benefits of network coding in WSNs for fire alarm systems and presents a real-world implementation, demonstrating its potential for improving the performance of these systems.
{"title":"Wireless sensor network for fire detection with network coding to improve security and reliability","authors":"Johannes Braun, Faouzi Derbel","doi":"10.1016/j.measen.2024.101404","DOIUrl":"10.1016/j.measen.2024.101404","url":null,"abstract":"<div><div>This article proposes a wireless sensor network (WSN) for fire alarm systems that leverages network coding to enhance system reliability and security. The proposed WSN is designed to comply with European standards, with a particular focus on German standards, and enables the deployment of sensor nodes that automatically construct a robust and reliable network. The self-healing, redundant, decentralized routed network ensures uninterrupted functionality in case of a communication failure. The network coding technique is utilized to manage the surge in data traffic during hazard situations, reducing the overall number of telegrams while maintaining data integrity and reliability. By implementing network coding, the proposed WSN reduces energy consumption and enhances the efficiency and reliability of fire alarm systems, thereby contributing to greater safety in emergency situations. To further leverage the advantages of network coding, a new decentralized routing technique is introduced. This technique operates locally on nodes through the use of virtual cluster heads and an innovative Weighted Composite Value (WCV) Table, optimizing routing decisions for improved performance and scalability. This article provides a comprehensive exploration of the benefits of network coding in WSNs for fire alarm systems and presents a real-world implementation, demonstrating its potential for improving the performance of these systems.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101404"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143859","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}
The Internet of Things (IoT) is a network that interconnects many everyday objects, including computers, televisions, washing machines, and even whole urban areas. These devices has the capability to collect and disseminate information because to their integration of electronics, software, sensors, and connectivity to a network. The Internet of Things enables the remote sensing, identification, and control of physical things via the utilisation of existing network infrastructure. By using this function, it becomes feasible to integrate elements of the physical world into computerised systems, resulting in enhanced levels of efficiency, precision, and financial profitability. The Internet of Things (IoT) encompasses a diverse array of applications. The Internet of Things (IoT) may be used in several sectors such as healthcare, smart cities, smart homes, transportation, logistics, agriculture, and smart traffic management. The quantity of Internet of Things (IoT) devices is increasing rapidly and exponentially. The surge in numbers is accompanied by a significant escalation in security vulnerabilities. This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. The system aims to accurately categorise and forecast attacks on IoT devices. This approach utilises the publicly accessible NSL KDD dataset as its input dataset. During the data collecting process for NSL-KDD, all symbolic qualities are transformed into their corresponding numerical representations. Conversely, all numerical features are translated back into symbolic form at the conclusion of the procedure. Principal component analysis is employed to achieve the objective of attribute extraction. After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. The accuracy of the Intrusion Detection System (IDS) based on Particle Swarm Optimisation (PSO) is 98.5 percent. The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.
{"title":"PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things","authors":"Mutkule Prasad Raghunath , Shyam Deshmukh , Poonam Chaudhari , Sunil L. Bangare , Kishori Kasat , Mohan Awasthy , Batyrkhan Omarov , Rajesh R. Waghulde","doi":"10.1016/j.measen.2024.101806","DOIUrl":"10.1016/j.measen.2024.101806","url":null,"abstract":"<div><div>The Internet of Things (IoT) is a network that interconnects many everyday objects, including computers, televisions, washing machines, and even whole urban areas. These devices has the capability to collect and disseminate information because to their integration of electronics, software, sensors, and connectivity to a network. The Internet of Things enables the remote sensing, identification, and control of physical things via the utilisation of existing network infrastructure. By using this function, it becomes feasible to integrate elements of the physical world into computerised systems, resulting in enhanced levels of efficiency, precision, and financial profitability. The Internet of Things (IoT) encompasses a diverse array of applications. The Internet of Things (IoT) may be used in several sectors such as healthcare, smart cities, smart homes, transportation, logistics, agriculture, and smart traffic management. The quantity of Internet of Things (IoT) devices is increasing rapidly and exponentially. The surge in numbers is accompanied by a significant escalation in security vulnerabilities. This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. The system aims to accurately categorise and forecast attacks on IoT devices. This approach utilises the publicly accessible NSL KDD dataset as its input dataset. During the data collecting process for NSL-KDD, all symbolic qualities are transformed into their corresponding numerical representations. Conversely, all numerical features are translated back into symbolic form at the conclusion of the procedure. Principal component analysis is employed to achieve the objective of attribute extraction. After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. The accuracy of the Intrusion Detection System (IDS) based on Particle Swarm Optimisation (PSO) is 98.5 percent. The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101806"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145243","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}
—Wireless telecommunication systems are essential in transferring data through fog cloud servers. However, the fog cloud servers suffer from unreliable and non-secure routing and limited power resources when mobile users are mobile. This paper introduces a scalable framework (SFRRDC) for reliable wireless routing and secure data transfer. We aim to address security and reliability issues by combining pheromone termite (PT) characteristics and ant colony optimization (ACO) algorithms for high-performance, more secure, highly reliable data transfer among the fog cloud servers. For that, the proposed SFRRDC supports a user authentication mining (UAM) algorithm to secure the confidentiality of the users. Based on testing results, it is confirmed that the proposed SFRRDC provides a better solution for confidentiality and fast routing for a wireless telecommunication system. The results show that the proposed SFRRDC framework's energy consumption outperforms competing frameworks' energy consumption with a better-achieved latency. For example, the suggested SFRRDC method uses 748.5 J during 72 h of operation, competing frameworks use more energy, and DDFQFR uses 976.22 J. It also shows that the proposed SFRRDC is immune to malicious attacks. The results show that with 3600 Fog cloud users, the proposed HEE protocol reduces the number of attacks to only 48 compared to 58, 72, and 77415 expected malicious attacks when using the ICDRP-F-SDVN, ACO, and DDFQFR frameworks, respectively.
{"title":"A scalable framework for secure and reliable wireless-based fog cloud communication","authors":"Kymbat Khairosheva , Abdul Razaque , Gulnara Bektemyssova , Joon Yoo","doi":"10.1016/j.measen.2024.101408","DOIUrl":"10.1016/j.measen.2024.101408","url":null,"abstract":"<div><div>—Wireless telecommunication systems are essential in transferring data through fog cloud servers. However, the fog cloud servers suffer from unreliable and non-secure routing and limited power resources when mobile users are mobile. This paper introduces a scalable framework (SFRRDC) for reliable wireless routing and secure data transfer. We aim to address security and reliability issues by combining pheromone termite (PT) characteristics and ant colony optimization (ACO) algorithms for high-performance, more secure, highly reliable data transfer among the fog cloud servers. For that, the proposed SFRRDC supports a user authentication mining (UAM) algorithm to secure the confidentiality of the users. Based on testing results, it is confirmed that the proposed SFRRDC provides a better solution for confidentiality and fast routing for a wireless telecommunication system. The results show that the proposed SFRRDC framework's energy consumption outperforms competing frameworks' energy consumption with a better-achieved latency. For example, the suggested SFRRDC method uses 748.5 J during 72 h of operation, competing frameworks use more energy, and DDFQFR uses 976.22 J. It also shows that the proposed SFRRDC is immune to malicious attacks. The results show that with 3600 Fog cloud users, the proposed HEE protocol reduces the number of attacks to only 48 compared to 58, 72, and 77415 expected malicious attacks when using the ICDRP-F-SDVN, ACO, and DDFQFR frameworks, respectively.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101408"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143860","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}
‘Based on Minutiae and Neural Networks,’ this paper introduces a robust fingerprint identification system that significantly enhances the accuracy of matching fingerprints, especially those altered due to various reasons such as scars or mutilations. Utilizing a combination of minutiae-based matching and neural network algorithms, the system is designed to overcome the limitations of traditional methods, which often fail under less-than-ideal conditions. The system's core lies in its ability to train an artificial neural network to learn an improved similarity function for minutiae matching. This capability has been extensively validated through a series of rigorous experiments, demonstrating its superiority over existing systems. Implemented in MATLAB, the system performs remarkably on benchmark datasets like FVC2004 DB1 and NIST SD27, achieving state-of-the-art results. This paper not only presents a detailed methodology involving image enhancement, minutiae extraction, and advanced matching techniques but also sets a new standard in fingerprint identification technology, particularly in handling altered fingerprints effectively.
{"title":"Enhancing fingerprint identification using Fuzzy-ANN minutiae matching","authors":"S.P. Singh , Dinesh Kumar Nishad , Saifullah Khalid","doi":"10.1016/j.measen.2025.101809","DOIUrl":"10.1016/j.measen.2025.101809","url":null,"abstract":"<div><div>‘Based on Minutiae and Neural Networks,’ this paper introduces a robust fingerprint identification system that significantly enhances the accuracy of matching fingerprints, especially those altered due to various reasons such as scars or mutilations. Utilizing a combination of minutiae-based matching and neural network algorithms, the system is designed to overcome the limitations of traditional methods, which often fail under less-than-ideal conditions. The system's core lies in its ability to train an artificial neural network to learn an improved similarity function for minutiae matching. This capability has been extensively validated through a series of rigorous experiments, demonstrating its superiority over existing systems. Implemented in MATLAB, the system performs remarkably on benchmark datasets like FVC2004 DB1 and NIST SD27, achieving state-of-the-art results. This paper not only presents a detailed methodology involving image enhancement, minutiae extraction, and advanced matching techniques but also sets a new standard in fingerprint identification technology, particularly in handling altered fingerprints effectively.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101809"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145244","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}
Pub Date : 2025-02-01DOI: 10.1016/j.measen.2024.101409
Allah Ditta , Muhammad Maroof Ahmed , Tehseen Mazhar , Tariq Shahzad , Yazan Alahmed , Habib Hamam
This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management.
{"title":"Number plate recognition smart parking management system using IoT","authors":"Allah Ditta , Muhammad Maroof Ahmed , Tehseen Mazhar , Tariq Shahzad , Yazan Alahmed , Habib Hamam","doi":"10.1016/j.measen.2024.101409","DOIUrl":"10.1016/j.measen.2024.101409","url":null,"abstract":"<div><div>This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101409"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143857","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}
Pub Date : 2025-02-01DOI: 10.1016/j.measen.2025.101807
N. Manjunathan, N. Gomathi
The use of Internet of Things (IoT) devices has been growing rapidly recently. As technology improves, products for older people are developed in the health industry. Applications for virtual and remote interactions with patients are somewhat too simple to use. If IoT technology is used well, it may be possible to treat physically erratic individuals without having to see a doctor often. As a result of this research, a prototype of an Internet of Things–based remote health monitoring system for senior patients has been developed. The suggested technique enables the care to better manage and keep an eye on the well-being of older patients. The system will design and implement efficient contact with the patient's families. This model has a number of sensors, including sensors for arthritis, body temperature, skin response, and pulse. Each sensor is paired with a system of proposals for analysis and validation. The data feasibility of the data obtained from the IoT sensors of the proposed system efficacy is being explored. The information obtained from the sensors and the extracted data is sent to cloud storage via distributed storage. In the performance studies, the efficacy of the proposed system is evaluated based on the data retrieved and used against certain health metrics like heartbeat and temperature sensors. IoT combined with wellness wearables may eliminate the need to visit a doctor for urgent health conditions. To ensure data accuracy & system scaling, Internet of Things devices are employed in the proposed system, & the power consumption and battery life are analysed.
{"title":"A comparative analysis of the health monitoring process using deep learning methods for brain tumour","authors":"N. Manjunathan, N. Gomathi","doi":"10.1016/j.measen.2025.101807","DOIUrl":"10.1016/j.measen.2025.101807","url":null,"abstract":"<div><div>The use of Internet of Things (IoT) devices has been growing rapidly recently. As technology improves, products for older people are developed in the health industry. Applications for virtual and remote interactions with patients are somewhat too simple to use. If IoT technology is used well, it may be possible to treat physically erratic individuals without having to see a doctor often. As a result of this research, a prototype of an Internet of Things–based remote health monitoring system for senior patients has been developed. The suggested technique enables the care to better manage and keep an eye on the well-being of older patients. The system will design and implement efficient contact with the patient's families. This model has a number of sensors, including sensors for arthritis, body temperature, skin response, and pulse. Each sensor is paired with a system of proposals for analysis and validation. The data feasibility of the data obtained from the IoT sensors of the proposed system efficacy is being explored. The information obtained from the sensors and the extracted data is sent to cloud storage via distributed storage. In the performance studies, the efficacy of the proposed system is evaluated based on the data retrieved and used against certain health metrics like heartbeat and temperature sensors. IoT combined with wellness wearables may eliminate the need to visit a doctor for urgent health conditions. To ensure data accuracy & system scaling, Internet of Things devices are employed in the proposed system, & the power consumption and battery life are analysed.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101807"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145242","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}
Pub Date : 2025-02-01DOI: 10.1016/j.measen.2024.101804
Kailin Zhang , Yue Pan , Xiping Xu , Liang Xu , Wancheng Liu , Motong Hu , Yi Lu , Yajie Cao
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