Thomas Henderson

Computational Sensor Networks (eBook, PDF)

• Format: PDF

Produktbeschreibung

This text proposes a model-based approach to the design and implementation of Computational Sensor Networks (CSNs). This high-level paradigm for the development and application of sensor device networks provides a strong scientific computing foundation, as well as the basis for robust software engineering practices. Building upon a model-based approach the text discusses computational modeling of sensor networks and covers real-time computational mapping that allows for modification of system parameters according to real-time performance measures.

Drawing upon years of theoretical development and practical experience, and using numerous examples and illustrative applications, Thomas Henderson covers the sensor network as a computational science tool and describes:

A computational framework for sensor networks;

Emergent algorithms for cluster formation, coordinate frames, gradients, reaction-diffusion pattern formation, and level set computation in a sensor network;

How CSNs can be used to develop models to probe the structure of the sensor system itself by solving inverse problems based on strong models (e.g., PDEs) of the physical phenomena;

How CSNs can be used to model sensor network systems, including surveillance, environment modeling, structure monitoring, and biomedical monitoring;

Computational Sensor Networks is a must have book for sensor network application engineers, computer engineers, computer scientists and those involved in the development, design and building of sensor networks in an industrial, research and an academic environment.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 228
• Erscheinungstermin: 21. April 2009
• Englisch
• ISBN-13: 9780387096438
• Artikelnr.: 37288174

Autorenporträt

Thomas Henderson, University of Utah, Salt Lake City, UT, USA

Inhaltsangabe

CSN: Overview of Approach.- Leadership Algorithms.- Coordinate Frames and Gradient Calculation.- Pattern Formation in S-Nets.- Logical Sensors and Computational Mapping.- Mobile Robot Performance Analysis.- CSN: The Heat Equation.- Bayesian Estimation of Distributed Phenomena.