Open-circuit switch faults (OCSFs) in power semiconductor switches are caused by wire bonding failures, gate driver malfunction, surge voltage/current, electromagnetic interference, and cosmic radiation. Under OCSFs, the signal characteristics are not excessively high, but prolonged OCSFs risk cascading system failures. This letter presents a comprehensive analysis of various deep neural network (DNN)-based architectures, such as long short-term memory (LSTM) and convolutional neural network (CNN), to diagnose multiclass OCSFs in three-phase active front-end rectifiers (TP-AFRs). A novel multisensor time-series sequence (MTSS) dataset is acquired at 500 Hz, comprising 624 observations from 19 sensor signals for single, double, and triple-switch OCSFs. The intertwining issue in the MTSS dataset is visualized using t-SNE, and the initial experiments with support vector machine (SVM) rendered the highest test accuracy of 93% against k-nearest neighbor, artificial neural network, and decision tree classifiers. Further, our investigations revealed that an architecture with two-layer CNN, one-layer LSTM, and one fully connected layer achieves a competitive testing accuracy of 95.03%, showing an improvement of 2.03% from the SVM classifier, and 7.03% from the one-layer LSTM network. These findings demonstrate the potential of this approach for enhancing reliability of TP-AFRs with the direct application of downsampled raw electrical signals.
{"title":"Deep Learning-Based Multiswitch Open-Circuit Fault Diagnosis for Active Front-End Rectifiers Using Multisensor Signals","authors":"Sourabh Ghosh;Ehtesham Hassan;Asheesh Kumar Singh;Sri Niwas Singh","doi":"10.1109/LSENS.2024.3524033","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3524033","url":null,"abstract":"Open-circuit switch faults (OCSFs) in power semiconductor switches are caused by wire bonding failures, gate driver malfunction, surge voltage/current, electromagnetic interference, and cosmic radiation. Under OCSFs, the signal characteristics are not excessively high, but prolonged OCSFs risk cascading system failures. This letter presents a comprehensive analysis of various deep neural network (DNN)-based architectures, such as long short-term memory (LSTM) and convolutional neural network (CNN), to diagnose multiclass OCSFs in three-phase active front-end rectifiers (TP-AFRs). A novel multisensor time-series sequence (MTSS) dataset is acquired at 500 Hz, comprising 624 observations from 19 sensor signals for single, double, and triple-switch OCSFs. The intertwining issue in the MTSS dataset is visualized using t-SNE, and the initial experiments with support vector machine (SVM) rendered the highest test accuracy of 93% against k-nearest neighbor, artificial neural network, and decision tree classifiers. Further, our investigations revealed that an architecture with two-layer CNN, one-layer LSTM, and one fully connected layer achieves a competitive testing accuracy of 95.03%, showing an improvement of 2.03% from the SVM classifier, and 7.03% from the one-layer LSTM network. These findings demonstrate the potential of this approach for enhancing reliability of TP-AFRs with the direct application of downsampled raw electrical signals.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976132","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 : 2024-12-26DOI: 10.1109/LSENS.2024.3523334
Rahul Mishra;Aishwarya Soni;Ayush Jain;Priyanka Lalwani;Raj Shah
Recent years have witnessed significant growth in sensors-based human locomotion activities recognition due to the availability of low-cost, low-power, and compact sensors and microcontroller units. While significant research has been conducted on human locomotion activity recognition using inertial sensors, most prior studies heavily rely on data from all axes of the sensors. However, the importance of dominant axes in reducing training and inference time has been largely overlooked in these investigations. This letter presents a novel approach, dominant axes-human activity recognition, which aims to identify the dominant axes of inertial sensors to effectively recognize human locomotion activities. The proposed approach effectively reduces both training and inference time while still achieving substantial accuracy. The approach begins with data collection through dedicated smartphone applications and sensory probes. Subsequently, the collected sensory data undergoes preprocessing and annotation for model training. Further, cross-validation is performed during the training phase to determine the dominant axes, leveraging information about the orientation within the dataset. Finally, this work conducts experiments on the collected dataset to assess the approach's efficacy in terms of accuracy and training time.
{"title":"Optimizing Activity Recognition Through Dominant Axis Identification in Inertial Sensors","authors":"Rahul Mishra;Aishwarya Soni;Ayush Jain;Priyanka Lalwani;Raj Shah","doi":"10.1109/LSENS.2024.3523334","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3523334","url":null,"abstract":"Recent years have witnessed significant growth in sensors-based human locomotion activities recognition due to the availability of low-cost, low-power, and compact sensors and microcontroller units. While significant research has been conducted on human locomotion activity recognition using inertial sensors, most prior studies heavily rely on data from all axes of the sensors. However, the importance of dominant axes in reducing training and inference time has been largely overlooked in these investigations. This letter presents a novel approach, dominant axes-human activity recognition, which aims to identify the dominant axes of inertial sensors to effectively recognize human locomotion activities. The proposed approach effectively reduces both training and inference time while still achieving substantial accuracy. The approach begins with data collection through dedicated smartphone applications and sensory probes. Subsequently, the collected sensory data undergoes preprocessing and annotation for model training. Further, cross-validation is performed during the training phase to determine the dominant axes, leveraging information about the orientation within the dataset. Finally, this work conducts experiments on the collected dataset to assess the approach's efficacy in terms of accuracy and training time.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940715","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}
Most EEG-based biometrics rely on either convolutional neural networks (CNNs) or graph convolutional neural networks (GCNNs) for personal authentication, potentially overlooking the limitations of each approach. To address this, we propose EEG-BBNet, a hybrid network that combines CNNs and GCNNs. EEG-BBNet leverages CNN's capability for automatic feature extraction and the GCNN's ability to learn connectivity patterns between EEG electrodes through graph representation. We evaluate its performance against solely CNN-based and graph-based models across three brain–computer interface tasks, focusing on daily motor and sensory activities. The results show that while EEG-BBNet with Rho index functional connectivity metric outperforms graph-based models, it initially lags behind CNN-based models. However, with additional fine-tuning, EEG-BBNet surpasses CNN-based models, achieving a correct recognition rate of approximately 90%. This improvement enables EEG-BBNet to adapt its learning in new sessions and to acquire different domain knowledge across various BCI tasks (e.g., motor imagery to steady-state visually evoked potentials), demonstrating promise for practical authentication.
{"title":"EEG-BBNet: A Hybrid Framework for Brain Biometric Using Graph Connectivity","authors":"Payongkit Lakhan;Nannapas Banluesombatkul;Natchaya Sricom;Phattarapong Sawangjai;Soravitt Sangnark;Tohru Yagi;Theerawit Wilaiprasitporn;Wanumaidah Saengmolee;Tulaya Limpiti","doi":"10.1109/LSENS.2024.3522981","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3522981","url":null,"abstract":"Most EEG-based biometrics rely on either convolutional neural networks (CNNs) or graph convolutional neural networks (GCNNs) for personal authentication, potentially overlooking the limitations of each approach. To address this, we propose EEG-BBNet, a hybrid network that combines CNNs and GCNNs. EEG-BBNet leverages CNN's capability for automatic feature extraction and the GCNN's ability to learn connectivity patterns between EEG electrodes through graph representation. We evaluate its performance against solely CNN-based and graph-based models across three brain–computer interface tasks, focusing on daily motor and sensory activities. The results show that while EEG-BBNet with Rho index functional connectivity metric outperforms graph-based models, it initially lags behind CNN-based models. However, with additional fine-tuning, EEG-BBNet surpasses CNN-based models, achieving a correct recognition rate of approximately 90%. This improvement enables EEG-BBNet to adapt its learning in new sessions and to acquire different domain knowledge across various BCI tasks (e.g., motor imagery to steady-state visually evoked potentials), demonstrating promise for practical authentication.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993288","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}
Rate-integrating gyroscopes provide significant advantages in temperature stability and bandwidth. However, their performance is not fully realized due to the X–Y mismatches in the micro-electromechanical systems (MEMS) resonator, usually caused by fabrication imperfections, aging, or temperature fluctuation. This letter presents a novel approach for real-time detection and dynamic compensation of these mismatches based on a virtual rotation technique. The proposed method detects the mismatch parameters as the angle dependence of clockwise and counterclockwise frequencies, which has the same degree of freedom as the stiffness and damping mismatch parameters, meaning that it could fully detect all of the mismatch information. A method to determine the mismatch compensation signals based on linear transformation is developed, minimizing all of the mismatch signals by four independent PI controllers. The real-time mismatch compensation was experimentally demonstrated using the software-defined MEMS gyroscope system. This approach paves the way for the practical deployment of rate-integrating MEMS gyroscopes.
{"title":"Real-Time Detection and Dynamic Compensation of Mismatch in Rate-Integrating MEMS Gyroscopes Using Virtual Rotation","authors":"Takashiro Tsukamoto;Fumito Miyazaki;Taichi Uchiumi;Yasushi Tomizawa;Shuji Tanaka","doi":"10.1109/LSENS.2024.3520910","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520910","url":null,"abstract":"Rate-integrating gyroscopes provide significant advantages in temperature stability and bandwidth. However, their performance is not fully realized due to the \u0000<italic>X–Y</i>\u0000 mismatches in the micro-electromechanical systems (MEMS) resonator, usually caused by fabrication imperfections, aging, or temperature fluctuation. This letter presents a novel approach for real-time detection and dynamic compensation of these mismatches based on a virtual rotation technique. The proposed method detects the mismatch parameters as the angle dependence of clockwise and counterclockwise frequencies, which has the same degree of freedom as the stiffness and damping mismatch parameters, meaning that it could fully detect all of the mismatch information. A method to determine the mismatch compensation signals based on linear transformation is developed, minimizing all of the mismatch signals by four independent PI controllers. The real-time mismatch compensation was experimentally demonstrated using the software-defined MEMS gyroscope system. This approach paves the way for the practical deployment of rate-integrating MEMS gyroscopes.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940697","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}
As the need for realistic and immersive 3-D representations of the environment continues to increase across various industries, finding efficient ways to represent data has become paramount. A well-known approach to partitioning 3-D space into a structured data format is the use of octrees, primarily due to their efficiency in handling both sparse and dense 3-D data. This method is particularly useful in applications involving automotive light detection and ranging (LiDAR) sensors, which are widely used in autonomous driving systems for their ability to capture detailed spatial information in real-time. This letter introduces the fast octree generator (FOG) algorithm, a novel approach for generating octrees from 3-D LiDAR point clouds that leverages hardware acceleration. FOG achieves a performance improvement of up to 88.8% compared to PCL's octree implementation, enabling real-time octree generation for high-end sensors on embedded platforms.
{"title":"FOG: Fast Octree Generator for LiDAR Point Clouds","authors":"Ricardo Roriz;Diogo Costa;Mongkol Ekpanyapong;Tiago Gomes","doi":"10.1109/LSENS.2024.3520800","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520800","url":null,"abstract":"As the need for realistic and immersive 3-D representations of the environment continues to increase across various industries, finding efficient ways to represent data has become paramount. A well-known approach to partitioning 3-D space into a structured data format is the use of octrees, primarily due to their efficiency in handling both sparse and dense 3-D data. This method is particularly useful in applications involving automotive light detection and ranging (LiDAR) sensors, which are widely used in autonomous driving systems for their ability to capture detailed spatial information in real-time. This letter introduces the fast octree generator (FOG) algorithm, a novel approach for generating octrees from 3-D LiDAR point clouds that leverages hardware acceleration. FOG achieves a performance improvement of up to 88.8% compared to PCL's octree implementation, enabling real-time octree generation for high-end sensors on embedded platforms.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918338","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}
Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant role in understanding sleep patterns and disorders and underscore the need for robust scoring methodologies. In response, this letter introduces LG-Sleep, a novel subject-independent deep neural network architecture designed for mice sleep scoring through electroencephalogram (EEG) signals. LG-Sleep extracts local and global temporal transitions within EEG signals to categorize sleep data into three stages: wake, rapid eye movement (REM) sleep, and non-REM sleep. The model leverages local and global temporal information by employing time-distributed convolutional neural networks to discern local temporal transitions in EEG data. Subsequently, features derived from the convolutional filters traverse long short-term memory blocks, capturing global transitions over extended periods. Crucially, the model is optimized in an autoencoder–decoder fashion, facilitating generalization across distinct subjects and adapting to limited training samples. Experimental findings demonstrate superior performance of LG-Sleep compared to conventional deep neural networks. Moreover, the model exhibits good performance across different sleep stages even when tasked with scoring based on limited training samples.
{"title":"LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring","authors":"Shadi Sartipi;Mie Andersen;Natalie Hauglund;Celia Kjaerby;Verena Untiet;Maiken Nedergaard;Mujdat Cetin","doi":"10.1109/LSENS.2024.3523427","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3523427","url":null,"abstract":"Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant role in understanding sleep patterns and disorders and underscore the need for robust scoring methodologies. In response, this letter introduces LG-Sleep, a novel subject-independent deep neural network architecture designed for mice sleep scoring through electroencephalogram (EEG) signals. LG-Sleep extracts local and global temporal transitions within EEG signals to categorize sleep data into three stages: wake, rapid eye movement (REM) sleep, and non-REM sleep. The model leverages local and global temporal information by employing time-distributed convolutional neural networks to discern local temporal transitions in EEG data. Subsequently, features derived from the convolutional filters traverse long short-term memory blocks, capturing global transitions over extended periods. Crucially, the model is optimized in an autoencoder–decoder fashion, facilitating generalization across distinct subjects and adapting to limited training samples. Experimental findings demonstrate superior performance of LG-Sleep compared to conventional deep neural networks. Moreover, the model exhibits good performance across different sleep stages even when tasked with scoring based on limited training samples.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993371","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 : 2024-12-25DOI: 10.1109/LSENS.2024.3521956
Yang Liu;Jin Wu;Fulong Ma;Chengxi Zhang
This letter investigates attitude estimation based on biquaternions (complex quaternions) for robotic applications utilizing a single-vector observation from sensors, such as accelerometer and magnetometer. In this work, we discover the evolution of novel form of quaternion, termed the biquaternion, where each component is a complex number instead of a real scalar, in the attitude approximation process. This biquaternion form arises from the intermediate solution of differential equations obtained from quaternion attitude dynamics. We study the evolution trajectories of biquaternions in the attitude estimation workspace, unveiling their inherent patterns and physical interpretations. Furthermore, we investigate the convergence performance of the biquaternion-based attitude estimator by tuning different parameters, demonstrating its potential superiority over traditional real quaternion estimators. The proposed biquaternion attitude estimation framework offers a unique perspective on attitude representation and opens up new avenues for enhancing estimation accuracy and robustness.
{"title":"Biquaternion Evolution in Attitude Estimation Using a Generalized Vector Measurement","authors":"Yang Liu;Jin Wu;Fulong Ma;Chengxi Zhang","doi":"10.1109/LSENS.2024.3521956","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3521956","url":null,"abstract":"This letter investigates attitude estimation based on biquaternions (complex quaternions) for robotic applications utilizing a single-vector observation from sensors, such as accelerometer and magnetometer. In this work, we discover the evolution of novel form of quaternion, termed the biquaternion, where each component is a complex number instead of a real scalar, in the attitude approximation process. This biquaternion form arises from the intermediate solution of differential equations obtained from quaternion attitude dynamics. We study the evolution trajectories of biquaternions in the attitude estimation workspace, unveiling their inherent patterns and physical interpretations. Furthermore, we investigate the convergence performance of the biquaternion-based attitude estimator by tuning different parameters, demonstrating its potential superiority over traditional real quaternion estimators. The proposed biquaternion attitude estimation framework offers a unique perspective on attitude representation and opens up new avenues for enhancing estimation accuracy and robustness.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993289","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}
An efficient electrochemical sensor based on Cr–Au interdigitated electrode porous silicon has been developed to rapidly assess Escherichia coli (E. coli) bacteria in water. Coliform bacteria, particularly E. coli, contribute significantly to waterborne contamination, driven by overuse and insufficient cleanliness around water bodies. This letter incorporates the fabrication of porous silicon (PSi), characterization, synthesis of bacterial dilutions, and testing of the sensor in the presence of varying E. coli dilutions. The dilutions are prepared from the stock solution of bacterial concentrations and hydrogen peroxide (H2O2). The interaction of porous silicon with bacteria incubated in H2O2 leads to a change in potential across the electrodes in real time. The limits of detection and sensitivity for the sensor are 0.187 CFU/mL and 113 mV⋅mL/CFU, respectively. The response time and the recovery time of the sensor are 80 and 90 ms, respectively. In addition, analyses such as repeatability and testing in tap water, Pseudomonas, and Citrobacter are conducted. For a user-friendly output, the sensor has been interfaced with a signal conditioning circuit and a display. This prototype offers a quick and precise way to identify the quality of drinking water, making it a potential solution to the growing problems caused by water pollution.
{"title":"Rapid and Sensitive Electrochemical Detection of Escherichia coli in Water Using Cr–Au IDE-Porous Silicon Sensor","authors":"Vandana Kumari Chalka;Kamaljit Rangra;Saakshi Dhanekar","doi":"10.1109/LSENS.2024.3522457","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3522457","url":null,"abstract":"An efficient electrochemical sensor based on Cr–Au interdigitated electrode porous silicon has been developed to rapidly assess <italic>Escherichia coli (E. coli)</i> bacteria in water. Coliform bacteria, particularly <italic>E. coli</i>, contribute significantly to waterborne contamination, driven by overuse and insufficient cleanliness around water bodies. This letter incorporates the fabrication of porous silicon (PSi), characterization, synthesis of bacterial dilutions, and testing of the sensor in the presence of varying <italic>E. coli</i> dilutions. The dilutions are prepared from the stock solution of bacterial concentrations and hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>). The interaction of porous silicon with bacteria incubated in H<sub>2</sub>O<sub>2</sub> leads to a change in potential across the electrodes in real time. The limits of detection and sensitivity for the sensor are 0.187 CFU/mL and 113 mV⋅mL/CFU, respectively. The response time and the recovery time of the sensor are 80 and 90 ms, respectively. In addition, analyses such as repeatability and testing in tap water, <italic>Pseudomonas</i>, and <italic>Citrobacter</i> are conducted. For a user-friendly output, the sensor has been interfaced with a signal conditioning circuit and a display. This prototype offers a quick and precise way to identify the quality of drinking water, making it a potential solution to the growing problems caused by water pollution.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975822","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}
The distance between wireless sensors in random fields is crucial for performance analysis and sensor network deployment. However, the exact distribution models are normally of great complexity and can hardly lead to closed-form analytics for most cases. In this letter, we investigate the intersensor distance distribution in random fields, propose a polynomial intersensor distance distributional substitute, and develop two strategies for distributional parameter mapping for different application scenarios. Simulation results presented in this letter verify the effectiveness and efficiency of the low-complexity distributional substitution technique. The verified analyses given in this letter can help to provide mathematically tractable performance metrics for wireless sensor networks where sensors are randomly distributed over the 2-D space.
{"title":"Distributional Substitution for Intersensor Distances in Random Fields","authors":"Jia Ye;Shuping Dang;Shuaishuai Guo;Raed Shubair;Marwa Chafii","doi":"10.1109/LSENS.2024.3521994","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3521994","url":null,"abstract":"The distance between wireless sensors in random fields is crucial for performance analysis and sensor network deployment. However, the exact distribution models are normally of great complexity and can hardly lead to closed-form analytics for most cases. In this letter, we investigate the intersensor distance distribution in random fields, propose a polynomial intersensor distance distributional substitute, and develop two strategies for distributional parameter mapping for different application scenarios. Simulation results presented in this letter verify the effectiveness and efficiency of the low-complexity distributional substitution technique. The verified analyses given in this letter can help to provide mathematically tractable performance metrics for wireless sensor networks where sensors are randomly distributed over the 2-D space.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940696","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}
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