The Experts below are selected from a list of 36837 Experts worldwide ranked by ideXlab platform
Zengfeng Huang - One of the best experts on this subject based on the ideXlab platform.
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KDD - Personalized PageRank to a Target Node, Revisited
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Co-Authors: Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, Zengfeng HuangAbstract:Personalized PageRank (PPR) is a widely used Node proximity measure in graph mining and network analysis. Given a source Node $s$ and a Target Node $t$, the PPR value $\pi(s,t)$ represents the probability that a random walk from $s$ terminates at $t$, and thus indicates the bidirectional importance between $s$ and $t$. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source Node $s$ and every Node $t \in V$. However, the single-source query only reflects the importance of each Node $t$ with respect to $s$. In this paper, we consider the {\em single-Target PPR query}, which measures the opposite direction of importance for PPR. Given a Target Node $t$, the single-Target PPR query asks for the PPR value of every Node $s\in V$ to a given Target Node $t$. We propose RBS, a novel algorithm that answers approximate single-Target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.
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personalized pagerank to a Target Node revisited
arXiv: Data Structures and Algorithms, 2020Co-Authors: Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, Zengfeng HuangAbstract:Personalized PageRank (PPR) is a widely used Node proximity measure in graph mining and network analysis. Given a source Node $s$ and a Target Node $t$, the PPR value $\pi(s,t)$ represents the probability that a random walk from $s$ terminates at $t$, and thus indicates the bidirectional importance between $s$ and $t$. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source Node $s$ and every Node $t \in V$. However, the single-source query only reflects the importance of each Node $t$ with respect to $s$. In this paper, we consider the {\em single-Target PPR query}, which measures the opposite direction of importance for PPR. Given a Target Node $t$, the single-Target PPR query asks for the PPR value of every Node $s\in V$ to a given Target Node $t$. We propose RBS, a novel algorithm that answers approximate single-Target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.
Dong Yang - One of the best experts on this subject based on the ideXlab platform.
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RMLNet-A Reliable Wireless Network for a Multiarea TDOA-Based Localization System.
Sensors (Basel Switzerland), 2019Co-Authors: Yuan Xue, Hongchao Wang, Dong Yang, Weiting ZhangAbstract:Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile Nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of Target Node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing Target Nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor Nodes. In addition, an approach for Target area determination is proposed, which can approximately determine the region of the Target Node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the Target Node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing Target Nodes.
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A Model on Indoor Localization System Based on the Time Difference Without Synchronization
IEEE Access, 2018Co-Authors: Yuan Xue, Hongchao Wang, Dong YangAbstract:Localization has emerged as an attractive solution to enable new business models that rely on location-based services in wireless networks for communication, sensing, and control. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, the conventional TDOA requires precise time synchronization between a Target Node and anchor Nodes for measuring the time difference, which leads to a large number of packets for communication. To reduce packet transmission, we propose a model for measuring the time difference without time synchronization called ASync-TDOA. Different from the conventional model, ASync-TDOA can measure the time difference in a one-way-based ranging by introducing the reference Node. Specifically, the time difference between the Target Node and anchor Nodes can be directly measured by the server based on the timestamps from the reference Node. After that, the Target Node is accurately located using least squares and brute force for the time difference. We implement the ASync-TDOA model on a localization system with ultra-wideband signals to estimate the localization of the Target Node, which is easy to operate in practical engineering. The experiments show that the proposed ASync-TDOA is efficient in reducing the packet transmissions and improving the TDOA measurement and localization precision.
Hanzhi Wang - One of the best experts on this subject based on the ideXlab platform.
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KDD - Personalized PageRank to a Target Node, Revisited
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Co-Authors: Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, Zengfeng HuangAbstract:Personalized PageRank (PPR) is a widely used Node proximity measure in graph mining and network analysis. Given a source Node $s$ and a Target Node $t$, the PPR value $\pi(s,t)$ represents the probability that a random walk from $s$ terminates at $t$, and thus indicates the bidirectional importance between $s$ and $t$. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source Node $s$ and every Node $t \in V$. However, the single-source query only reflects the importance of each Node $t$ with respect to $s$. In this paper, we consider the {\em single-Target PPR query}, which measures the opposite direction of importance for PPR. Given a Target Node $t$, the single-Target PPR query asks for the PPR value of every Node $s\in V$ to a given Target Node $t$. We propose RBS, a novel algorithm that answers approximate single-Target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.
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personalized pagerank to a Target Node revisited
arXiv: Data Structures and Algorithms, 2020Co-Authors: Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, Zengfeng HuangAbstract:Personalized PageRank (PPR) is a widely used Node proximity measure in graph mining and network analysis. Given a source Node $s$ and a Target Node $t$, the PPR value $\pi(s,t)$ represents the probability that a random walk from $s$ terminates at $t$, and thus indicates the bidirectional importance between $s$ and $t$. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source Node $s$ and every Node $t \in V$. However, the single-source query only reflects the importance of each Node $t$ with respect to $s$. In this paper, we consider the {\em single-Target PPR query}, which measures the opposite direction of importance for PPR. Given a Target Node $t$, the single-Target PPR query asks for the PPR value of every Node $s\in V$ to a given Target Node $t$. We propose RBS, a novel algorithm that answers approximate single-Target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.
Yuan Xue - One of the best experts on this subject based on the ideXlab platform.
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RMLNet-A Reliable Wireless Network for a Multiarea TDOA-Based Localization System.
Sensors (Basel Switzerland), 2019Co-Authors: Yuan Xue, Hongchao Wang, Dong Yang, Weiting ZhangAbstract:Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile Nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of Target Node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing Target Nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor Nodes. In addition, an approach for Target area determination is proposed, which can approximately determine the region of the Target Node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the Target Node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing Target Nodes.
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A Model on Indoor Localization System Based on the Time Difference Without Synchronization
IEEE Access, 2018Co-Authors: Yuan Xue, Hongchao Wang, Dong YangAbstract:Localization has emerged as an attractive solution to enable new business models that rely on location-based services in wireless networks for communication, sensing, and control. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, the conventional TDOA requires precise time synchronization between a Target Node and anchor Nodes for measuring the time difference, which leads to a large number of packets for communication. To reduce packet transmission, we propose a model for measuring the time difference without time synchronization called ASync-TDOA. Different from the conventional model, ASync-TDOA can measure the time difference in a one-way-based ranging by introducing the reference Node. Specifically, the time difference between the Target Node and anchor Nodes can be directly measured by the server based on the timestamps from the reference Node. After that, the Target Node is accurately located using least squares and brute force for the time difference. We implement the ASync-TDOA model on a localization system with ultra-wideband signals to estimate the localization of the Target Node, which is easy to operate in practical engineering. The experiments show that the proposed ASync-TDOA is efficient in reducing the packet transmissions and improving the TDOA measurement and localization precision.
Qinghua Luo - One of the best experts on this subject based on the ideXlab platform.
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A Least Square Dynamic Localization Algorithm Based on Statistical Filtering Optimal Strategy
Communications Signal Processing and Systems, 2020Co-Authors: Xiaozhen Yan, Zhihao Han, Yipeng Yang, Qinghua Luo, Cong HuAbstract:In wireless sensor network localization, many anchor Nodes and Target Node exchange information at specified time intervals to obtain the distance information between each anchor Node and the Target Node. With this information, the coordinates of the Target Node can be achieved through the calculation of the positioning algorithm. However, as there are numerous negative factors like non-line-of-sight measurement, complex multipath fading, which leads to high-level localization error. To improve localization accuracy, an improved least square localization algorithm is proposed, which combines the least square localization method with the statistical filtering optimization strategy. The simulation results show that this algorithm can effectively reduce localization error and achieve more accurate localization.
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CSPS (2) - A Least Square Dynamic Localization Algorithm Based on Statistical Filtering Optimal Strategy.
Lecture Notes in Electrical Engineering, 2019Co-Authors: Xiaozhen Yan, Zhihao Han, Yipeng Yang, Qinghua LuoAbstract:In wireless sensor network localization, many anchor Nodes and Target Node exchange information at specified time intervals to obtain the distance information between each anchor Node and the Target Node. With this information, the coordinates of the Target Node can be achieved through the calculation of the positioning algorithm. However, as there are numerous negative factors like non-line-of-sight measurement, complex multipath fading, which leads to high-level localization error. To improve localization accuracy, an improved least square localization algorithm is proposed, which combines the least square localization method with the statistical filtering optimization strategy. The simulation results show that this algorithm can effectively reduce localization error and achieve more accurate localization.
