Measurement Sensors最新文献_第3页

A scalable framework for secure and reliable wireless-based fog cloud communication

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101408

Kymbat Khairosheva , Abdul Razaque , Gulnara Bektemyssova , Joon Yoo

{"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}

引用次数: 0

Enhancing fingerprint identification using Fuzzy-ANN minutiae matching

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2025.101809

S.P. Singh , Dinesh Kumar Nishad , Saifullah Khalid

{"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}

引用次数: 0

Number plate recognition smart parking management system using IoT

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101409

Allah Ditta , Muhammad Maroof Ahmed , Tehseen Mazhar , Tariq Shahzad , Yazan Alahmed , Habib Hamam

{"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}

引用次数: 0

A comparative analysis of the health monitoring process using deep learning methods for brain tumour

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2025.101807

N. Manjunathan, N. Gomathi

{"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}

引用次数: 0

Corrigendum to “Opto-mechanical-thermal integration design of the primary optical system for a tri-band aviation camera” [Measure. PE 242 (2025) 116319]

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101804

Kailin Zhang , Yue Pan , Xiping Xu , Liang Xu , Wancheng Liu , Motong Hu , Yi Lu , Yajie Cao

引用次数: 0

Application of edge computing technology in smart grid data security

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101412

Zhuo Cheng, Jiangxin Li, Jianjun Zhang, Chen Wang, Hui Wang, Juyin Wu

{"title":"Application of edge computing technology in smart grid data security","authors":"Zhuo Cheng, Jiangxin Li, Jianjun Zhang, Chen Wang, Hui Wang, Juyin Wu","doi":"10.1016/j.measen.2024.101412","DOIUrl":"10.1016/j.measen.2024.101412","url":null,"abstract":"<div><div>In order to solve the problem that the two-way flow of power and information between user nodes and service nodes in the smart grid poses a huge threat to the privacy and security of user data, and at the same time, the limitation of the power bureau's computing resources also brings users response delay, service quality degradation and other problems, the author proposes the application of edge computing technology in smart grid data security. Combining with edge computing technology, the author proposes a proxy blind signcryption scheme based on certificateless without bilinear mapping. By blinding the power and information, the signcrypter can not know the specific power consumption information of the user, so as to ensure the data privacy and security of the user. Implement forward security using proxy key update mechanism and perform batch verification of user signature ciphertext. The experimental results indicate that: The total running time required for executing proxy authorization and verification, proxy key generation, signature and decryption algorithms in this scheme is 5.617 ms, with a ciphertext length of 80 Bytes. Compared with other existing literature, the maximum reduction is 85.6 % and 86 %, respectively.</div></div><div><h3>Conclusion</h3><div>This scheme is more suitable for protecting data security and privacy in the data transmission process of smart grids due to its lower running time and communication cost.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101412"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145249","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

Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101405

T. Swathi Priyadarshini, Mohd Abdul Hameed

引用次数: 0

Enhanced defect sensing technology in turbid water environments using multi-beam sonar

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101805

Wenhui Wang, Yikai Li, Rufei He, Yao Li

{"title":"Enhanced defect sensing technology in turbid water environments using multi-beam sonar","authors":"Wenhui Wang, Yikai Li, Rufei He, Yao Li","doi":"10.1016/j.measen.2024.101805","DOIUrl":"10.1016/j.measen.2024.101805","url":null,"abstract":"<div><div>In this paper, we report a novel defect perception technology utilizing multi-beam sonar for applications in turbid water environments. Our goal is to improve the precision and speed of identifying target image defects. We categorize the target image recognition dataset following specific guidelines and devise a target image imaging model customized for the distinct characteristics of turbid water settings. By employing the weighted time average (WMT) algorithm, we calculate the time window for each beam within the water environment. Moreover, we utilize the phase difference sequence method to enhance target image details in turbid water, and leverage the time of arrival (TOA) estimation method to suppress background noise and sidelobes. Through the implementation of a dynamic detection threshold, our technology facilitates defect perception in turbid water environments using multi-beam sonar. Experimental results demonstrate that this method achieves an accuracy of 96.05 % in recognizing image defects in turbid water environments, significantly enhancing both the accuracy and efficiency of defect recognition. It also overcomes the typical challenges of underwater detection in turbid and low-light conditions.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101805"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145245","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

Pneumonia detection from X-ray images using federated learning–An unsupervised learning approach

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101410

Neeta Rana , Hitesh Marwaha

{"title":"Pneumonia detection from X-ray images using federated learning–An unsupervised learning approach","authors":"Neeta Rana , Hitesh Marwaha","doi":"10.1016/j.measen.2024.101410","DOIUrl":"10.1016/j.measen.2024.101410","url":null,"abstract":"<div><div>The emergence of advanced data analysis techniques has revolutionized patient healthcare by enabling the early and efficient detection of diseases. Traditionally, disease identification relied solely on the expertise of medical professionals. However, limitations in physician availability, particularly in resource-constrained regions, can hinder timely diagnosis. Fortunately, data analysis techniques are now widely employed to address a multitude of medical disease detection. This paper presents a novel Pneumonia disease detection model by analyzing the chest X-ray data. The development of robust diagnostic tools faces a critical challenge: the lack of access to large, labeled training datasets. This challenge arises because of privacy concerns about medical data. This research proposes a solution that tackles both data scarcity and privacy concerns. It leverages an unsupervised learning model trained on decentralized datasets. The unsupervised learning approach used is an Autoencoder neural network, and the decentralized learning technique used for model training is Federated Learning. The proposed approach trains the model on data residing at multiple locations, such as healthcare institutions, without compromising patient privacy. The datasets used to train the proposed model consist of chest X-ray images of pneumonia patients and healthy individuals. It offers satisfactory performance when compared to other existing Pneumonia detection models. In similar studies, medical institutions can collaborate and develop efficient medical tools without breaching patients’ data privacy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101410"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143856","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

Spatiotemporal data modeling and prediction algorithms in intelligent management systems

Measurement Sensors Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101411

Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

{"title":"Spatiotemporal data modeling and prediction algorithms in intelligent management systems","authors":"Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng","doi":"10.1016/j.measen.2024.101411","DOIUrl":"10.1016/j.measen.2024.101411","url":null,"abstract":"<div><div>In order to solve the problem of difficulty in learning semantic pattern representations between user dynamic interest sequences using path based and knowledge graph based entity embedding methods, the author proposes research on spatiotemporal data modeling and prediction algorithms in intelligent management systems. The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. Finally, experiments were conducted using wireless network datasets to evaluate the performance of the model. The experimental results show that based on data analysis, it can be seen that the prediction of the system has effectively improved by 99 %.</div></div><div><h3>Conclusion</h3><div>The spatiotemporal data modeling and prediction algorithm proposed by the author in the intelligent management system significantly improves prediction accuracy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101411"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145241","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