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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the...
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.
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摘要 :
RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the...
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.
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Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for im...
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Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for improving the overall throughput by enabling concurrent transmissions of the backscattering tags. The state-of-the-art approaches for parallel backscatter assume that all the states of the collided signals are distinguishable in the In-phase and Quadrature (IQ) signal plane. In this paper, we disclose the superclustering phenomenon that makes the assumption untenable and significantly degrades the overall performance. Moreover, we observe that the indistinguishable states at different channels are not the same due to the intrinsic channel diversity. Motivated by the observation, we propose Canon, an approach that exploits the channel diversity of the backscatter tags for reliable parallel decoding. In Canon, we address two critical challenges: (i) designing the Multi-Carrier Backscatter (MCB) module to extract the collided signals simultaneously from multiple channels, (ii) designing the Multi-Channel Cluster Union (MCCU) algorithm to distinguish each state of the collided signals. The experiments demonstrate that Canon can achieve over 10x higher throughput than the state-of-the-art approaches.
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The traditional design method of the structure is commonly conceived by designers based on demands, thoughts, and experiences. However, this process is often time-consuming and hard to get the optimal solution. Automatic optimal d...
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The traditional design method of the structure is commonly conceived by designers based on demands, thoughts, and experiences. However, this process is often time-consuming and hard to get the optimal solution. Automatic optimal design is the future development trend of structural design. The main purpose of this study is to propose an automatic optimization design method for building structures. The optimization method is based on constraint sensitivity data. Design constraint sensitivities can indicate the optimal changing directions of design variables. Based on the initial design, the sensitivity data can provide the optimal direction of structural material adjustment. Employing sensitivity data can realize the compliance design by the minimum material increment, and realize the optimization design by the maximum material decrement, thus to meet the performance requirements of super high-rise building structures with high efficiency and low cost. The design automation process is also described based on a modeling-analysis-design methodology. A frame shear wall structure is to be employed to show the viability and process of constraint sensitivity data-driven structural design automation.
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Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power cons...
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Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.
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摘要 :
Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power cons...
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Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.
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摘要 :
Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power cons...
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Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.
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Leakage detection is a crucial issue for factories with numerous pipelines and valves. Conventional methods for leakage detection are mainly rely on manual checking, which results in both high delay and low accuracy. In this paper...
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Leakage detection is a crucial issue for factories with numerous pipelines and valves. Conventional methods for leakage detection are mainly rely on manual checking, which results in both high delay and low accuracy. In this paper, we propose TagLeak, a real-time and low-cost system for automatic leakage detection with commercial off-the-shelf (COTS) RFID devices. The key intuition behind TagLeak is that the leaked liquid around tags will change their phase and RSSI (Received Signal Strength Indicator) readings. Multiple challenges need to be addressed before we can turn the idea into a functional system, including: i) it is difficult to detect the slight signal variation that caused by the leaked liquid, based on the coarse-grained RSSI sequence; ii) multipath and interferences can undermine the tags signal, making the variation caused by leaked liquid more difficult to detect. We propose solutions to these challenges and evaluate the systems performance in different environments. The experimental results tell that TagLeak achieves a higher than 90.2% true positive rate (TPR) while keeps false positive rate (FPR) below 14.3%. Moreover, as an exploration of the industrial Internet, we have deployed TagLeak in a real-world digital twin system Pavatar for liquid leakage detection in an ultra-high-voltage converter station (UHVCS).
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摘要 :
Leakage detection is a crucial issue for factories with numerous pipelines and valves. Conventional methods for leakage detection are mainly rely on manual checking, which results in both high delay and low accuracy. In this paper...
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Leakage detection is a crucial issue for factories with numerous pipelines and valves. Conventional methods for leakage detection are mainly rely on manual checking, which results in both high delay and low accuracy. In this paper, we propose TagLeak, a real-time and low-cost system for automatic leakage detection with commercial off-the-shelf (COTS) RFID devices. The key intuition behind TagLeak is that the leaked liquid around tags will change their phase and RSSI (Received Signal Strength Indicator) readings. Multiple challenges need to be addressed before we can turn the idea into a functional system, including: i) it is difficult to detect the slight signal variation that caused by the leaked liquid, based on the coarse-grained RSSI sequence; ii) multipath and interferences can undermine the tags signal, making the variation caused by leaked liquid more difficult to detect. We propose solutions to these challenges and evaluate the systems performance in different environments. The experimental results tell that TagLeak achieves a higher than 90.2% true positive rate (TPR) while keeps false positive rate (FPR) below 14.3%. Moreover, as an exploration of the industrial Internet, we have deployed TagLeak in a real-world digital twin system Pavatar for liquid leakage detection in an ultra-high-voltage converter station (UHVCS).
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Software-defined networking (SDN) is deemed as a promising direction to offer generalizability of wireless sensor networks (WSN). To introduce SDN into WSNs, however, means a series of non-trivial challenges due to the wireless an...
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Software-defined networking (SDN) is deemed as a promising direction to offer generalizability of wireless sensor networks (WSN). To introduce SDN into WSNs, however, means a series of non-trivial challenges due to the wireless and ad-hoc nature of WSNs. In this paper, we present our study towards a software-defined architecture for multi-function wireless sensor networks. Our proposal called Pangu is built upon the opportunistic routing protocol stack and introduces the concept of modality properties of sensor nodes. It enables centralized network control over a WSN while preserving the flexibility of underlying ad-hoc routing. We tackle the critical problems of the architecture design by presenting three essential components of Pangu. Moreover, we implement Pangu on a real-world testbed and evaluate it with various experiments.
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