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2007 | Buch

Learning from Data Streams

Processing Techniques in Sensor Networks

herausgegeben von: João Gama, Mohamed Medhat Gaber

Verlag: Springer Berlin Heidelberg

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Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate.

The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.

This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.

Inhaltsverzeichnis

Frontmatter

Overview

Frontmatter
1. Introduction
João Gama, Mohamed Medhat Gaber
2. Sensor Networks: An Overview
João Barros
3. Data Stream Processing
João Gama, Pedro Pereira Rodrigues
4. Data Stream Processing in Sensor Networks
Mohamed Medhat Gaber

Data Stream Management Techniques in Sensor Networks

Frontmatter
5. Data Stream Management Systems and Architectures
M. A. Hammad, T. M. Ghanem, W. G. Aref, A. K. Elmagarmid, M. F. Mokbel
6. Querying of Sensor Data
Niki Trigoni, Alexandre Guitton, Antonios Skordylis
7. Aggregation and Summarization in Sensor Networks
Abstract
Sensor networks generate enormous quantities of data which need to be processed in a distributed fashion to extract interesting information. We outline how ideas and algorithms from data stream query processing are revolutionizing data processing in sensor networks. We also discuss how sensor networks pose some particular problems of their own and how these are being overcome.
Nisheeth Shrivastava, Chiranjeeb Buragohain
8. Sensory Data Monitoring
Abstract
The goal of sensory data monitoring is to maximise the quality of data gathered by a sensor network. The principal problems for this task are, specifiying which data is most relevant to user’s goals, minimising the cost of gathering that data, and clearing the gathered data. This chapter outlines the state-of-the-art in addressing each of these challenges.
Rachel Cardell-Oliver

Mining Sensor Network Data Streams

Frontmatter
9. Clustering Techniques in Sensor Networks
Abstract
The traditional knowledge discovery environment, where data and processing units are centralized in controlled laboratories and servers, is now completely transformed into a web of sensorial devices, some of them with local processing ability. This scenario represents a new knowledge-extraction environment, possibly not completely observable, that is much less controlled by both the human user and a common centralized control process.
Pedro Pereira Rodrigues, João Gama
10. Predictive Learning in Sensor Networks
Abstract
Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios—centralized and distributed—and present illustrative algorithms for these contexts.
João Gama, Rasmus Ulslev Pedersen
11. Tensor Analysis on Multi-aspect Streams
Abstract
Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Aside from time stamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent-window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real data sets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
Jimeng Sun, Spiros Papadimitriou, Philip S. Yu

Applications

Frontmatter
12. Knowledge Discovery from Sensor Data for Security Applications
Abstract
Evolving threat situations in a post-9/11 world demand faster and more reliable decisions to thwart the adversary. One critical path to enhanced threat recognition is through online knowledge discovery based on dynamic, heterogeneous data available from strategically placed wide-area sensor networks. The knowledge discovery process needs to coordinate adaptive predictive analysis with real-time analysis and decision support systems. The ability to detect precursors and signatures of rare events and change from massive and disparate data in real time may require a paradigm shift in the science of knowledge discovery. This chapter describes a case study in the area of transportation security to describe both the key challenges, as well as the possible solutions, in this high-priority area. A suite of knowledge discovery tools developed for the purpose is described along with a discussion on future requirements.
Auroop R. Ganguly, Olufemi A. Omitaomu, Randy M. Walker
13. Knowledge Discovery from Sensor Data For Scientific Applications
Abstract
The current advances in sensors and sensor infrastructures offer new opportunities for monitoring the operations and conditions of man-made and natural environments. The ability to generate insights or new knowledge from sensor data is critical for many high-priority scientific applications especially weather, climate, and associated natural hazards. One example is sensor-based early warning systems for geophysical extremes such as tsunamis or extreme rainfall, which can help preempt disaster damage. Indeed, the loss of life during the 2004 Indian Ocean tsunami may have been significantly reduced, if not totally prevented, had sensor-based early warning systems been in place. One other example is high-resolution risk-mapping of insights obtained through a combination of historical and real-time sensor data, with physics-based computer simulations. Weather, climate and associated natural hazards have established history of using sensor data, such as data from DOPPLER radars. Recent advances in sensor technology and computational strengths have created a need for new approaches to analyzing data associated with weather, climate, and associated natural hazards. Knowledge discovery offers tools for extracting new, useful and hidden insights from data repositories. However, knowledge discovery techniques need to be geared towards scalable and efficient implementations of predictive insights, online or fast real-time analysis of incremental information, and solution processes for strategic and tactical decisions. Predictive insights regarding weather, climate and associated natural hazards may require models of rare, anomalous and extreme events, nonlinear phenomena, and change analysis, in particular from massive volumes of dynamic data streams. On the other hand, historical data may also be noisy and incomplete, thus robust tools need to be developed for these situations. This chapter describes some of the research challenges of knowledge discovery from sensor data for weather, climate and associated natural hazard applications and summarizes our approach towards addressing these challenges.
Auroop R. Ganguly, Olufemi A. Omitaomu, Yi Fang, Shiraj Khan, Budhendra L. Bhaduri
14. TinyOS Education with LEGO MINDSTORMS NXT
Abstract
The LEGO MINDSTORMS NXT (http://​mindstorms.​lego.​com/​.)—armed with its embedded ARM7 and ATmega48 microcontrollers (MCUs), Bluetooth radio, four input ports, three output ports, and dozens of sensors—is proposed as an educational platform for TinyOS. (http://​www.​tinyos.​net.) The purpose of this chapter is to assess NXT for use in wireless sensor network education. To this end, the following items are evaluated: NXT hardware/software, LEGO MINDSTORMS “ecosystem”, and educational elements. We outline how this platform can be used for educational purposes due to the wide selection of available and affordable sensors. For hardware developers, the ease of creating new sensors will be hard to resist. Also, in the context of education, TinyOS can be compared to other embedded operating systems based on the same hardware. This chapter argues that this comparability facilitate across-community adoption and awareness of TinyOS. Finally, we present the first TinyOS project on NXT, hosted both at TinyOS 2.x contrib and SourceForge under the nxtmote name.
Rasmus Ulslev Pedersen
Backmatter
Metadaten
Titel
Learning from Data Streams
herausgegeben von
João Gama
Mohamed Medhat Gaber
Copyright-Jahr
2007
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-73679-0
Print ISBN
978-3-540-73678-3
DOI
https://doi.org/10.1007/3-540-73679-4

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