检索结果-内蒙古大学图书馆

Time and Memory Efficient Online Piecewise Linear Approximation of sensor Signals

sensorS 2018年第6期18卷 1672-1672页

作者： Gruetzmacher, Florian Beichler, Benjamin Hein, Albert Kirste, Thomas Haubelt, Christian Univ Rostock Inst Appl Microelect & Comp Engn D-18051 Rostock Germany Univ Rostock Inst Comp Sci D-18051 Rostock Germany

Piecewise linear approximation of sensor signals is a well-known technique in the fields of data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment's length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm's worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

关键词： piecewise linear approximation segmentation sensor data abstraction sensor signal compression CPLR wireless sensor networks embedded sensor processing

来源：评论

学校读者我要写书评

暂无评论

sensor Networks for Railway Monitoring: Detecting Trains from their Distributed Vibration Footprints

Sensor Networks for Railway Monitoring: Detecting Trains fro...

引用

9th IEEE International Conference on Distributed Computing in sensor Systems (DCoSS)

作者： Berlin, Eugen Van Laerhoven, Kristof Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9780769550411

We report in this paper on a wireless sensor network deployment at railway tracks to monitor and analyze the vibration patterns caused by trains passing by. We investigate in particular a system that relies on having a distributed network of sensor nodes that individually contain efficient feature extraction algorithms and classifiers that fit the restricted hardware resources, rather than using few complex and specialized sensors. A feasibility study is described on the raw data obtained from a real-world deployment on one of Europe's busiest railroad sections, which was annotated with the help of video footage and contains vibration patterns of 186 trains. These trains were classified in 6 types by various methods, the best performing at an accuracy of 97%. The trains' length in wagons was estimated with a mean-squared error of 3.98. Visual inspection of the data shows further opportunities in the estimation of train speed and detection of worn-out cargo wheels.

关键词： feature extraction sensor data abstraction event classification railway monitoring wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

sensor Networks for Railway Monitoring: Detecting Trains from their Distributed Vibration Footprints

Sensor Networks for Railway Monitoring: Detecting Trains fro...

引用

IEEE International Conference on Distributed Computing in sensor Systems

作者： Eugen Berlin Kristof Van Laerhoven Department of Computer Science Technische Universit?t Darmstadt

ISBN: (纸本)9781479902064

关键词： feature extraction sensor data abstraction event classification railway monitoring wireless sensor networks Wireless sensor networks TRAIN Feature extraction railways Vibration Caravans hardware resource DISTRIBUTED NETWORKS Footprints

来源：评论

学校读者我要写书评

暂无评论

Geosensor data Representation Using Layered Slope Grids

引用

sensorS 2012年第12期12卷 17074-17093页

作者： Lee, Yongmi Jung, Young Jin Nam, Kwang Woo Nittel, Silvia Beard, Kate Ryu, Keun Ho Korea Inst Sci Technol & Informat Taejon 305806 South Korea Chungbuk Natl Univ Database Bioinformat Lab Chonju 361763 South Korea Kunsan Natl Univ Dept Comp & Informat Engn Kunsan 573701 South Korea Univ Maine Sch Comp & Informat Sci Orono ME 04467 USA

Environmental monitoring applications are designed for supplying derived and often integrated information by tracking and analyzing phenomena. To determine the condition of a target place, they employ a geosensor network to get the heterogeneous sensor data. To effectively handle a large volume of sensor data, applications need a data abstraction model, which supports the summarized data representation by encapsulating raw data. For faster data processing to answer a user's queries with representative attributes of an abstracted model, we propose such a data abstraction model, the Layered Slopes in Grid for sensor data abstraction (LSGSA), which is based on the SGSA. In a single grid-based layer for each sensor type, collected data is represented by slope directional vectors in two layered slopes, such as height and surface. To answer a user query in a central monitoring server, LSGSA is used to reduce the time needed to extract event features from raw sensor data as a preprocessing step for interpreting the observed data. The extracted features are used to understand the current data trends and the progress of a detected phenomenon without accessing raw sensor data.

关键词： sensor data abstraction sensor data representation geosensor network slope grid GIS surface model

来源：评论

学校读者我要写书评

暂无评论

An on-line piecewise linear approximation technique for wireless sensor networks

An on-line piecewise linear approximation technique for wire...

引用

35th Annual IEEE Conference on Local Computer Network (LCN)

作者： Berlin, Eugen Van Laerhoven, Kristof Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9781424483891

Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data. Experiments show that, at the cost of slightly more processing power on the node, our algorithm performs a shape abstraction of the sensed time series which, depending on the nature of the data, can extensively reduce network traffic and nodes' power consumption.

关键词： sensor data abstraction piecewise linear approximation SWAB time series shape analysis wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Geosensor data abstraction for Environmental Monitoring Application

Geosensor Data Abstraction for Environmental Monitoring Appl...

引用

5th International Conference on Geographic Information Science

作者： Jung, Young Jin Nittel, Silvia Univ Maine Orono ME 04469 USA

ISBN: (纸本)9783540874720

Environmental observation applications are designed for monitoring phenomena using heterogeneous sensor data types and for providing derived and often integrated information. To effectively handle such a large variety of different sensors, both in scale and type and data volume, we propose a geosensor abstraction for large-scale geosensor networks. Our SGSA(Slope Grid for sensor data abstraction) represents collected data in single grid-based layers, and allows for summarizing the measured data in various integrated grid layers. Within each cell, a slope vector is used to represents the trend of the observed sensor data. This slope is used as a simplifying factor for processing queries over several sensor types. To handle dynamic sensor data, the proposed abstraction model also supports rapid data update by using a mapping table. This model can be utilized as a data representation model in various geosensor network applications.

关键词： sensor data abstraction Geosensor network Slope grid GIS Surface model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：