eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2006-09-20
5381
1
11
10.4230/DagSemProc.05381.1
article
05381 Abstracts Collection – Form and Content in Sensor Networks
Guibas, Leonidas J.
Hanebeck, Uwe D.
Henderson, Thomas C.
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol05381/DagSemProc.05381.1/DagSemProc.05381.1.pdf
Sensors
signal processing
sensor networks
intelligent systems
sensor data processing
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2006-09-20
5381
1
4
10.4230/DagSemProc.05381.2
article
05381 Executive Summary – Form and Content in Sensor Networks
Guibas, Leonidas J.
Hanebeck, Uwe D.
Henderson, Thomas C.
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol05381/DagSemProc.05381.2/DagSemProc.05381.2.pdf
Sensors
signal processing
sensor networks
intelligent systems
sensor data processing
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2006-09-20
5381
1
0
10.4230/DagSemProc.05381.3
article
Robustness and Accuracy of Bayesian Information Fusion Systems
Pavlin, Gregor
Nunnink, Jan
Groen, Frans
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol05381/DagSemProc.05381.3/DagSemProc.05381.3.pdf
Robust Information Fusion
Bayesian Networks
Heterogeneous Information
Modeling Uncertainties
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2006-09-20
5381
1
4
10.4230/DagSemProc.05381.4
article
Verification and Validation of Sensor Networks
Henderson, Thomas C.
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol05381/DagSemProc.05381.4/DagSemProc.05381.4.pdf
Models
verification
validation