The Experts below are selected from a list of 26640 Experts worldwide ranked by ideXlab platform
Liye Zhang - One of the best experts on this subject based on the ideXlab platform.
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Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Zhe Xiao, Xiuju Fu, Liye ZhangAbstract:Maritime Traffic service networks and information systems play a vital role in maritime Traffic safety management. The data collected from the maritime Traffic networks are essential for the perception of Traffic dynamics and predictive Traffic regulation. This paper is devoted to surveying the key processing components in maritime Traffic networks. Specifically, the latest progress on maritime Traffic data mining technologies for maritime Traffic Pattern extraction and the recent effort on vessels’ motion forecasting for better situation awareness are reviewed. Through the review, we highlight that the Traffic Pattern knowledge presents valued insights for wide-spectrum domain application purposes, and serves as a prerequisite for the knowledge based forecasting techniques that are growing in popularity. The development of maritime Traffic research in Pattern mining and Traffic forecasting reviewed in this paper affirms the importance of advanced maritime Traffic studies and the great potential in maritime Traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.
J.-h. Huang - One of the best experts on this subject based on the ideXlab platform.
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Reverse Huffman tree for nonuniform Traffic Pattern
Electronics Letters, 1991Co-Authors: J.-h. HuangAbstract:The tree protocol has been an important protocol in multiaccess communication channels with Traffic that arrives uniformly. In the Letter, a reverse Huffman tree protocol is proposed for multiaccess networks with nonuniform Traffic Pattern. Some tree construction protocols are developed and reverse Huffman tree protocol is shown to have the smallest delay.
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Reverse Huffman tree for nonuniform Traffic Pattern
Electronics Letters, 1991Co-Authors: J.-h. HuangAbstract:The tree protocol has been an important protocol in multi-access communication channels with Traffic that arrives uniformly. A reverse Huffman tree protocol is proposed for multi-access networks with a nonuniform Traffic Pattern. Some tree construction protocols are developed and the reverse Huffman tree protocol is shown to have the smallest delay.
Zhe Xiao - One of the best experts on this subject based on the ideXlab platform.
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Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Zhe Xiao, Xiuju Fu, Liye ZhangAbstract:Maritime Traffic service networks and information systems play a vital role in maritime Traffic safety management. The data collected from the maritime Traffic networks are essential for the perception of Traffic dynamics and predictive Traffic regulation. This paper is devoted to surveying the key processing components in maritime Traffic networks. Specifically, the latest progress on maritime Traffic data mining technologies for maritime Traffic Pattern extraction and the recent effort on vessels’ motion forecasting for better situation awareness are reviewed. Through the review, we highlight that the Traffic Pattern knowledge presents valued insights for wide-spectrum domain application purposes, and serves as a prerequisite for the knowledge based forecasting techniques that are growing in popularity. The development of maritime Traffic research in Pattern mining and Traffic forecasting reviewed in this paper affirms the importance of advanced maritime Traffic studies and the great potential in maritime Traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.
Feng Deng - One of the best experts on this subject based on the ideXlab platform.
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ICNC - Unsupervised maritime Traffic Pattern extraction from spatio-temporal data
2015 11th International Conference on Natural Computation (ICNC), 2015Co-Authors: Yong Deng, Feng DengAbstract:Maritime Traffic Pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime Traffic model. In this paper, we propose a framework of maritime Traffic Pattern extraction from vessel AIS information, which learns a Traffic Pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, Traffic Pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts Traffic Pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective Traffic Pattern. The proposed method strongly supports the further research on maritime Traffic Pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.
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Unsupervised maritime Traffic Pattern extraction from spatio-temporal data
2015 11th International Conference on Natural Computation (ICNC), 2015Co-Authors: Fumin Sun, Qingmeng Zhu, Feng Deng, Yong Deng, Hanyue ChuAbstract:Maritime Traffic Pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime Traffic model. In this paper, we propose a framework of maritime Traffic Pattern extraction from vessel AIS information, which learns a Traffic Pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, Traffic Pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts Traffic Pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective Traffic Pattern. The proposed method strongly supports the further research on maritime Traffic Pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.
Xueyan Tang - One of the best experts on this subject based on the ideXlab platform.
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Scheduling Sensor Data Collection with Dynamic Traffic Patterns
IEEE Transactions on Parallel and Distributed Systems, 2013Co-Authors: Wenbo Zhao, Xueyan TangAbstract:The network Traffic Pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. In contrast to most existing TDMA schedules designed for a static network Traffic Pattern, this paper proposes a novel TDMA schedule that is capable of efficiently collecting sensor data for any network Traffic Pattern and is thus well suited to continuous data collection with dynamic Traffic Patterns. In the proposed schedule, the energy consumed by sensor nodes for any Traffic Pattern is very close to the minimum required by their workloads given in the Traffic Pattern. The schedule also allows the base station to conclude data collection as early as possible according to the Traffic load, thereby reducing the latency of data collection. We present a distributed algorithm for constructing the proposed schedule. We develop a mathematical model to analyze the performance of the proposed schedule. We also conduct simulation experiments to evaluate the performance of different schedules using real-world data traces. Both the analytical and simulation results show that, compared with existing schedules that are targeted on a fixed Traffic Pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic Traffic Patterns.
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Scheduling data collection with dynamic Traffic Patterns in wireless sensor networks
2011 Proceedings IEEE INFOCOM, 2011Co-Authors: Wenbo Zhao, Xueyan TangAbstract:The network Traffic Pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. This paper develops a novel TDMA schedule that is capable of efficiently collecting sensor data for any network Traffic Pattern and is thus well suited to continuous data collection with dynamic Traffic Patterns. Following this schedule, the energy consumed by sensor nodes for any Traffic Pattern is very close to the minimum required by their workloads given in the Traffic Pattern. The schedule also allows the base station to conclude data collection as early as possible according to the Traffic load, thereby reducing the latency of data collection. Experimental results using real-world data traces show that, compared with existing schedules that are targeted on a fixed Traffic Pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic Traffic Patterns.
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INFOCOM - Scheduling data collection with dynamic Traffic Patterns in wireless sensor networks
2011 Proceedings IEEE INFOCOM, 2011Co-Authors: Wenbo Zhao, Xueyan TangAbstract:The network Traffic Pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. This paper develops a novel TDMA schedule that is capable of efficiently collecting sensor data for any network Traffic Pattern and is thus well suited to continuous data collection with dynamic Traffic Patterns. Following this schedule, the energy consumed by sensor nodes for any Traffic Pattern is very close to the minimum required by their workloads given in the Traffic Pattern. The schedule also allows the base station to conclude data collection as early as possible according to the Traffic load, thereby reducing the latency of data collection. Experimental results using real-world data traces show that, compared with existing schedules that are targeted on a fixed Traffic Pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic Traffic Patterns.
