Volume

Dagstuhl Seminar Proceedings, Volume 5381

Publication Details

  • published at: 2006-09-20
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.05381.1
05381 Abstracts Collection – Form and Content in Sensor Networks

Authors: Leonidas J. Guibas, Uwe D. Hanebeck, and Thomas C. Henderson

Abstract
From 18.09.05 to 23.09.05, the Dagstuhl Seminar 05381 ``Form and Content in Sensor Networks'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Leonidas J. Guibas, Uwe D. Hanebeck, and Thomas C. Henderson. 05381 Abstracts Collection – Form and Content in Sensor Networks. In Form and Content in Sensor Networks. Dagstuhl Seminar Proceedings, Volume 5381, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{guibas_et_al:DagSemProc.05381.1,
  author =	{Guibas, Leonidas J. and Hanebeck, Uwe D. and Henderson, Thomas C.},
  title =	{{05381 Abstracts Collection – Form and Content in Sensor Networks}},
  booktitle =	{Form and Content in Sensor Networks},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5381},
  editor =	{Leonidas Guibas and Uwe D. Hanebeck and Thomas C. Henderson},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05381.1},
  URN =		{urn:nbn:de:0030-drops-7555},
  doi =		{10.4230/DagSemProc.05381.1},
  annote =	{Keywords: Sensors, signal processing, sensor networks, intelligent systems, sensor data processing}
}
Document
DOI: 10.4230/DagSemProc.05381.2
05381 Executive Summary – Form and Content in Sensor Networks

Authors: Leonidas J. Guibas, Uwe D. Hanebeck, and Thomas C. Henderson

Abstract
From the September 18th until September 23rd, 2005 a Dagstuhl Seminar took place with the topic "Form and Content in Sensor Networks". 26 participants from four different countries, which are experts in sensor networks from the topics information processing, communication and robotics, presented current state of the art in the field of algorithm for sensor networks and how content and structure impact information processing in the networks. The presentations ranged from very theoretical computational models and algorithms to prototype implementations for monitoring the environment.
Cite as

Leonidas J. Guibas, Uwe D. Hanebeck, and Thomas C. Henderson. 05381 Executive Summary – Form and Content in Sensor Networks. In Form and Content in Sensor Networks. Dagstuhl Seminar Proceedings, Volume 5381, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{guibas_et_al:DagSemProc.05381.2,
  author =	{Guibas, Leonidas J. and Hanebeck, Uwe D. and Henderson, Thomas C.},
  title =	{{05381 Executive Summary – Form and Content in Sensor Networks}},
  booktitle =	{Form and Content in Sensor Networks},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5381},
  editor =	{Leonidas Guibas and Uwe D. Hanebeck and Thomas C. Henderson},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05381.2},
  URN =		{urn:nbn:de:0030-drops-7544},
  doi =		{10.4230/DagSemProc.05381.2},
  annote =	{Keywords: Sensors, signal processing, sensor networks, intelligent systems, sensor data processing}
}
Document
DOI: 10.4230/DagSemProc.05381.3
Robustness and Accuracy of Bayesian Information Fusion Systems

Authors: Gregor Pavlin, Jan Nunnink, and Frans Groen

Abstract
Modern situation assessment and controlling applications often require efficient fusion of large amounts of heterogeneous and uncertain information. In addition, fusion results are often mission critical. It turns out that Bayesian networks (BN) are suitable for a significant class of such applications, since they facilitate modeling of very heterogeneous types of uncertain information and support efficient belief propagation techniques. BNs are based on a rigorous theory which facilitates (i) analysis of the robustness of fusion systems and (ii) monitoring of the fusion quality. We assume domains where situations can be described through sets of discrete random variables. A situation corresponds to a set of hidden and observed states that the nature `sampled' from some true distribution over the combinations of possible states. Thus, in a particular situation certain states materialized while others did not, which corresponds to a point-mass distribution over the possible states. Consequently, the state estimation can be reduced to a classification of the possible combinations of relevant states. We assume that there exist mappings between hidden states of interest and optimal decisions/actions. In this context, we consider classification of the states accurate if it is equivalent to the truth in the sense that knowing the truth would not change the action based on the classification. Clearly, BNs provide a mapping between the observed symptoms and hypotheses about hidden events. Consequently, BNs have a critical impact on the fusion accuracy. We emphasize a fundamental difference between the model accuracy and fusion (i.e.classification) accuracy. A BN is a generalization over many possible situations that captures probability distributions over the possible events in the observed domain. However, even a perfect generalization does not necessarily support accurate classification in a particular situation. We address this problem with the help of the Inference Meta Model (IMM) which describes information fusion in BNs from a coarse, runtime perspective. IMM is based on a few realistic assumptions and exposes properties of BNs that are r elevant for the construction of inherently robust fusion systems. With the help of IMM we show that in BNs featuring many conditionally independent network fragments inference can be very insensitive to the modeling parameter values. This implies that fusion can be robust, which is especially relevant in many real world applications where we cannot obtain precise models due to the lack of sufficient training data or expertise. In addition, IMM introduces a reinforcement propagation algorithm that can be used as an alternative to the common approaches to inference in BNs. We can show that the classification accuracy of this propagation algorithm is asymptotically approaching 1 as the number of conditionally independent network fragments increases. Because of these properties, the propagation algorithm can be used as a basis for effective detection of misleading fusion results as well as discovery of inadequate modeling components and erroneous information sources.
Cite as

Gregor Pavlin, Jan Nunnink, and Frans Groen. Robustness and Accuracy of Bayesian Information Fusion Systems. In Form and Content in Sensor Networks. Dagstuhl Seminar Proceedings, Volume 5381, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{pavlin_et_al:DagSemProc.05381.3,
  author =	{Pavlin, Gregor and Nunnink, Jan and Groen, Frans},
  title =	{{Robustness and Accuracy of Bayesian Information Fusion Systems}},
  booktitle =	{Form and Content in Sensor Networks},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5381},
  editor =	{Leonidas Guibas and Uwe D. Hanebeck and Thomas C. Henderson},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05381.3},
  URN =		{urn:nbn:de:0030-drops-7561},
  doi =		{10.4230/DagSemProc.05381.3},
  annote =	{Keywords: Robust Information Fusion, Bayesian Networks, Heterogeneous Information, Modeling Uncertainties}
}
Document
DOI: 10.4230/DagSemProc.05381.4
Verification and Validation of Sensor Networks

Authors: Thomas C. Henderson

Abstract
Sensor networks play an increasingly important role in critical systems infrastructure and should be correct, reliable and robust. In order to achieve these performance goals, it is necessary to verify the correctness of system software and to validate the more broadly defined world and system models. This includes: * Physical Phenomena (PDE models, statistical models, etc.), * Signals (Equations of state, physical properties, etc.), * Sensors (Physics models, noise models, etc.), * Hardware (Failure models, power consumption models, etc.), * RF (Antenna models, bandwidth, delay, propagation, etc.), * Embedded Code (Correctness, complexity, context), * Distributed Algorithms (Correctness, concurrency models, etc.), * Overall Sensor Network and Environment Models (Percolation theory, wave theory, information theory, simulation, etc.). We outline some of the V & V issues involved in the various aspects of sensor networks as well as possible approaches to their development and application both in simulation and in operational deployed systems.
Cite as

Thomas C. Henderson. Verification and Validation of Sensor Networks. In Form and Content in Sensor Networks. Dagstuhl Seminar Proceedings, Volume 5381, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{henderson:DagSemProc.05381.4,
  author =	{Henderson, Thomas C.},
  title =	{{Verification and Validation of Sensor Networks}},
  booktitle =	{Form and Content in Sensor Networks},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5381},
  editor =	{Leonidas Guibas and Uwe D. Hanebeck and Thomas C. Henderson},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05381.4},
  URN =		{urn:nbn:de:0030-drops-7532},
  doi =		{10.4230/DagSemProc.05381.4},
  annote =	{Keywords: Models, verification, validation}
}

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  • Authors
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  • Groen, Frans
  • Guibas, Leonidas J.
  • H
  • Hanebeck, Uwe D.
  • Henderson, Thomas C.
  • N
  • Nunnink, Jan
  • P
  • Pavlin, Gregor

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