Measurement Sensors最新文献

{"title":"Congestion aware clustered WSN based on an improved ant colony algorithm","authors":"R. Anto Pravin ,&nbsp;X.S. Asha Shiny ,&nbsp;V. Baby Vennila ,&nbsp;P. Selvaraju ,&nbsp;R. Uma Mageswari ,&nbsp;S. Satish kumar","doi":"10.1016/j.measen.2024.101280","DOIUrl":"10.1016/j.measen.2024.101280","url":null,"abstract":"<div><p>Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"34 ","pages":"Article 101280"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002563/pdfft?md5=f48deea33570e814dd4f73c5f2915e50&pid=1-s2.0-S2665917424002563-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736632","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}

{"title":"Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN","authors":"R. Anto Pravin ,&nbsp;K. Murugan ,&nbsp;C. Thiripurasundari ,&nbsp;Prasanna Ranjith Christodoss ,&nbsp;R. Puviarasi ,&nbsp;Syed Ismail Abdul Lathif","doi":"10.1016/j.measen.2024.101282","DOIUrl":"10.1016/j.measen.2024.101282","url":null,"abstract":"<div><p>Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101282"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002587/pdfft?md5=38ff83f4be2c5ec4478c1aa989ffded6&pid=1-s2.0-S2665917424002587-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845616","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}

{"title":"Inclusion of kinesthetics feedback with vision to improve the control of neural activity of the primary motor cortex","authors":"S. Sobitha Ahila ,&nbsp;D. Rasi ,&nbsp;Logeshwari Dhavamani ,&nbsp;M. Rabiyathul Bachiriya ,&nbsp;G.S. Prasanna Lakshmi ,&nbsp;K. Vimala Devi","doi":"10.1016/j.measen.2024.101276","DOIUrl":"10.1016/j.measen.2024.101276","url":null,"abstract":"<div><p>In healthy people, the brain controls movement with a high amount of feedback from many modes of perception. Disease or injury compromises these sensory pathways, as well as their neural impulse counterparts, in many people, resulting in major deficits and a lower quality of life. The use of kinesthetics feedback can be used as a therapy method for a variety of neurological diseases like Parkinson's disease (PD) and stroke is gaining popularity. One of these therapeutic possibilities is to use a closed-loop feedback model with mental imagery as the self-regulation support to improve volitional control of malfunctioning or damaged brain networks and nodes. BMIs (Brain–Machine Interfaces) promise to restore function to these people by letting them to control a gadget with their thoughts. The majority of present BMI implementations rely on visual feedback for closed-loop control; But it has been argued that adding more sensory modalities could improve control. The paper shows that kinesthetics feedback may be combined with vision to considerably improve control of a primary motor cortex neural activity (MI). These findings imply that in paralysed individuals with residual kinesthetics feeling, BMI control can be greatly enhanced, and they provide the framework for augmenting cortically controlled BMIs with a variety of surrogate or natural sensory feedback.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101276"},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002526/pdfft?md5=7f3bfa09882c2dede9e26119cf6f8cce&pid=1-s2.0-S2665917424002526-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709836","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}

{"title":"IoT-based trusted wireless communication framework by machine learning approach","authors":"S. Chakaravarthi ,&nbsp;S. Saravanan ,&nbsp;M. Jagadeesh ,&nbsp;S. Nandhini","doi":"10.1016/j.measen.2024.101271","DOIUrl":"10.1016/j.measen.2024.101271","url":null,"abstract":"<div><p>The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"34 ","pages":"Article 101271"},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002472/pdfft?md5=1d4a6936b309c04375288d529a833869&pid=1-s2.0-S2665917424002472-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622335","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}

{"title":"Linear modeling techniques for liquid height regulation in two tank - Two variable system","authors":"A. Sreekanth Reddy ,&nbsp;G. Nageswara Reddy","doi":"10.1016/j.measen.2024.101275","DOIUrl":"10.1016/j.measen.2024.101275","url":null,"abstract":"<div><p>In this paper, two linear modeling techniques are presented for regulating the liquid height in two tank - two variable system (TT-TVS). Initially, the nonlinear dynamics of TT-TVS are linearized using the Taylor series linearization technique. However, this method reveals that some parameters of TT-TVS are inexact, necessitating the adoption of system identification techniques or mathematical approaches to accurately identify the linear dynamics. To address this, two new approaches are proposed: (i) a mathematical approach utilizing real-time input-output data, and (ii) a Linear Variable Parameter Transfer Function (LVPTF) model identification approach, which employs real-time data and MATLAB curve fitting tool. A comparative analysis between the proposed identification techniques and existing methods from the literature is also presented. The results indicate that the LVPTF modeling technique offers superior accuracy in identifying the linear dynamics of TT-TVS.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101275"},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002514/pdfft?md5=b75b2a9666e48b6eff2fdde73abacdda&pid=1-s2.0-S2665917424002514-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691675","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}

{"title":"Evaluation of correlation of physicochemical parameters and major ions present in groundwater of Raipur using discretization","authors":"Mridu Sahu ,&nbsp;Anushree Shrivastava ,&nbsp;D.C. Jhariya ,&nbsp;Shivangi Diwan ,&nbsp;Jalina Subhadarsini","doi":"10.1016/j.measen.2024.101278","DOIUrl":"10.1016/j.measen.2024.101278","url":null,"abstract":"<div><p>Groundwater, vital for human consumption and agriculture, ecosystem support, and industrial activities, requires sustainable management using proper quality assessment techniques. This study examines the relationship between physicochemical parameters and major ions in groundwater samples collected from 44 regions in Raipur, using sensor-based data acquisition alongside traditional methods. Employing K-means clustering for data discretization, correlations between parameters are highlighted. Results show positive associations among EC, TDS, TH, and TA. ArcGIS interpolation maps visualize spatial distribution. Addressing class imbalance, an upsampling technique is utilized. Machine learning algorithms, including Logistic Regression and Random Forest, classify water quality with accuracies of 98.8 % and 98.3 %, respectively. This research, blending traditional and sensor-based methods, emphasizes informed water management<strong>.</strong></p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"34 ","pages":"Article 101278"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266591742400254X/pdfft?md5=bebf3fe3c74e04f17b1ad4fcf00fdd06&pid=1-s2.0-S266591742400254X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622334","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}

{"title":"Classification and risk estimation of osteoarthritis using deep learning methods","authors":"Aparna R. Patil ,&nbsp;Satish Sampatrao Salunkhe","doi":"10.1016/j.measen.2024.101279","DOIUrl":"10.1016/j.measen.2024.101279","url":null,"abstract":"<div><p>The classification of knee osteoarthritis is solely based on contextual factors, with image processing algorithms playing a significant role in computer-aided diagnosis (CAD) systems. The inconsistent real-time pre-processing, on the other hand, has a significant impact on the diagnosing process. In this work, a Densely Connected Fully Convolutional Network (DFCN) for knee osteoarthritis classifier based on multiple learning (ML) strategies effectively classify knee osteoarthritis on the basis of risk estimation. Spatial osteoarthritis contextual vectors extracted by identifying the relationship between contextual variables using a machine learning approach. The hidden convolutional layers are used to compute edge interpretation, contextual cues, and input correction. The fused layer, which is simply a concentration of derived features, supports automatic learning of contextual features of osteoarthritis classification. The standard datasets from the Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST) are used for experimental purposes to validate the proposed method. The results shows that the proposed DFCN is significantly improves the feature recognition for accurate classification around 94 % which is significantly higher than existing CNN results and flexibility to real-time implementation in the CAD system. It can also be used to automatically detect osteoarthritis types using a lightweight CNN architecture.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101279"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002551/pdfft?md5=5c75f3522afc98856aee926028af04a6&pid=1-s2.0-S2665917424002551-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702159","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}

{"title":"Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system","authors":"T. Sudhamathi ,&nbsp;K. Perumal","doi":"10.1016/j.measen.2024.101277","DOIUrl":"10.1016/j.measen.2024.101277","url":null,"abstract":"<div><h3>Objective</h3><p>The worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data.</p></div><div><h3>Limitation</h3><p>A significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach.</p></div><div><h3>Method</h3><p>Initially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR).</p></div><div><h3>Result</h3><p>A simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R² were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture.</p></div><div><h3>Conclusion</h3><p>The study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101277"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002538/pdfft?md5=9c1bc3f103cab29b17a2503287aa5c9f&pid=1-s2.0-S2665917424002538-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705286","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}

{"title":"The influence of cracks on pillar strength based on SRM and DFN models","authors":"Ruijuan Liu ,&nbsp;Shuang Liu ,&nbsp;Lina Xiang ,&nbsp;Yan Jiang ,&nbsp;Chunyan Zhang","doi":"10.1016/j.measen.2024.101270","DOIUrl":"https://doi.org/10.1016/j.measen.2024.101270","url":null,"abstract":"<div><p>This research focuses on the examination of natural fractures within underground mines, emphasizing their substantial impact on the strength and stability of ore pillars. The study adopts the Strength Reduction Method (SRM) theory and employs the Discrete Fracture Network (DFN) model, offering a novel approach to investigating the behavior of fractured rock masses. The objective of this article is to analyze the influence of natural fractures on the strength of ore pillars by employing SRM and DFN methods. The research begins by establishing a multi-level amplification program that incorporates a homogenization process. The findings reveal that, for a W/H ratio of 0.5, the strength reduction aligns consistently with empirical equations. A notable observation is that when W/H is less than or equal to 1.0, there is good agreement, but when W/H exceeds 1.0, there is a tendency to overestimate pillar strength. Subsequent investigations emphasize the significance of considering pillar development in the overall assessment of pillar forces. The study underscores the importance of integrating pillar development into the analysis, aligning with previously established research results. Therefore, by evaluating the strength and failure mechanism of columns under different aspect ratios, we studied the influence of discrete discontinuous bodies on column stability, revealed the influence of natural cracks on column strength, and provided theoretical basis and reference for the design and support of underground mines.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"34 ","pages":"Article 101270"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002460/pdfft?md5=867db966c4fc2f7f5d9cfba7fe95c648&pid=1-s2.0-S2665917424002460-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594803","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}

{"title":"Analysis and development of smart production and distribution line system in smart grid based on optimization techniques involving digital twin","authors":"Thangaraja Arumugam ,&nbsp;Nitin Kundlik Kamble ,&nbsp;Venkataramana Guntreddi ,&nbsp;N. Vishnu Sakravarthy ,&nbsp;S. Shanthi ,&nbsp;Sivakumar Ponnusamy","doi":"10.1016/j.measen.2024.101272","DOIUrl":"https://doi.org/10.1016/j.measen.2024.101272","url":null,"abstract":"<div><p>The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"34 ","pages":"Article 101272"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002484/pdfft?md5=08cb6478a43674a63832459ed0bbd335&pid=1-s2.0-S2665917424002484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607450","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}