@inproceedings {INPROC-2014-03,
author = {Damian Philipp and Patrick Baier and Christoph Dibak and Frank D{\"u}rr and Kurt Rothermel and Susanne Becker and Michael Peter and Dieter Fritsch},
title = {{MapGENIE: Grammar-enhanced Indoor Map Construction from Crowd-sourced Data}},
booktitle = {Proceedings of the 12th IEEE International Conference on Pervasive Computing and Communications (PerCom 2014)},
address = {Budapest, Hungary},
publisher = {IEEE Computer Society Conference Publishing Services},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {139--147},
type = {Conference Paper},
month = {March},
year = {2014},
doi = {10.1109/PerCom.2014.6813954},
keywords = {Public Sensing; Opportunistic Sensing; Indoor Mapping; Map Reconstruction; IMU; Grammar},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2014-03/INPROC-2014-03.pdf,
http://dx.doi.org/10.1109/PerCom.2014.6813954},
contact = {damian.philipp@ipvs.uni-stuttgart.de patrick.baier@ipvs.uni-stuttgart.de christoph.dibak@ipvs.uni-stuttgart.de frank.duerr@ipvs.uni-stuttgart.de susanne.becker@ifp.uni-stuttgart.de michael.peter@ifp.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {While location-based services are already well established in outdoor
scenarios, they are still not available in indoor environments. The reason for
this can be found in two open problems: First, there is still no off-the-shelf
indoor positioning system for mobile devices and, second, indoor maps are not
publicly available for most buildings. While there is an extensive body of work
on the first problem, the efficient creation of indoor maps remains an open
challenge.
We tackle the indoor mapping challenge in our MapGENIE approach that
automatically derives indoor maps from traces collected by pedestrians moving
around in a building. Since the trace data is collected in the background from
the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of
traditional indoor map creation and increases the efficiency of indoor mapping.
To enhance the map building process, MapGENIE leverages exterior information
about the building and uses grammars to encode structural information about the
building. Hence, in contrast to existing work, our approach works without any
user interaction and only needs a small amount of traces to derive the indoor
map of a building. To demonstrate the performance of MapGENIE, we implemented
our system using Android and a foot-mounted IMU to collect traces from
volunteers. We show that using our grammar approach, compared to a purely
trace-based approach we can identify up to four times as many rooms in a
building while at the same time achieving a consistently lower error in the
size of detected rooms.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-03&engl=1}
}
@inproceedings {INPROC-2013-57,
author = {Susanne Becker and Michael Peter and Dieter Fritsch and Damian Philipp and Patrick Baier and Christoph Dibak},
title = {{Combined grammar for the modeling of building interiors}},
booktitle = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
address = {Kapstadt, S{\"u}dafrika},
publisher = {International Society for Photogrammetry and Remote Sensing},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
series = {ISPRS Acquisition and Modelling of Indoor and Enclosed Environments},
volume = {II-4/W1},
pages = {1--6},
type = {Conference Paper},
month = {December},
year = {2013},
keywords = {Public Sensing; Opportunistic Sensing; Smartphone; Indoor; Mapping},
language = {German},
cr-category = {J.5 Arts and Humanities,
C.2.4 Distributed Systems},
contact = {Susanne Becker susanne.becker@ifp.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {As spatial grammars have proven successful and efficient to deliver LoD3
models, the next challenge is their extension to indoor applications, leading
to LoD4 models. Therefore, a combined indoor grammar for the automatic
generation of indoor models from erroneous and incomplete observation data is
presented. In building interiors where inaccurate observation data is
available, the grammar can be used to make the reconstruction process robust,
and verify the reconstructed geometries. In unobserved building interiors, the
grammar can generate hypotheses about possible indoor geometries matching the
style of the rest of the building. The grammar combines concepts from L-systems
and split grammars. It is designed in such way that it can be derived from
observation data fully automatically. Thus, manual predefinitions of the
grammar rules usually required to tune the grammar to a specific building
style, become obsolete. The potential benefit of using our grammar as support
for indoor modeling is evaluated based on an example where the grammar has been
applied to automatically generate an indoor model from erroneous and incomplete
traces gathered by foot-mounted MEMS/IMU positioning systems.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2013-57&engl=1}
}
@inproceedings {INPROC-2013-35,
author = {Patrick Baier and Frank D{\"u}rr and Kurt Rothermel},
title = {{Efficient Distribution of Sensing Queries in Public Sensing Systems}},
booktitle = {Proceedings of the 10th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2013)},
publisher = {IEEE Computer Society},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--9},
type = {Conference Paper},
month = {October},
year = {2013},
language = {German},
cr-category = {C.2 Computer-Communication Networks},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2013-35/INPROC-2013-35.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {The advent of mobile phones paved the way for a new paradigm for gathering
sensor data termed Public Sensing (PS). PS uses built-in sensors of mobile
devices to opportunistically gather sensor data. For instance, the microphones
of a crowd of mobile phones can be used to capture sound samples, which can be
used to construct a city noise map.
A great challenge of PS is to reduce the energy consumption of mobile devices
since otherwise users might not be willing to participate. One crucial part in
the overall power consumption is the energy required for the communication
between the mobile devices and the infrastructure. In particular, the
communication required for sending sensing queries to mobile devices has been
largely neglected in the related work so far.
Therefore, in this paper, we address the problem of minimizing communication
costs for the distribution of sensing queries. While existing systems simply
broadcast sensing queries to all devices, we use a selective strategy by
addressing only a subset of devices. In order not to negatively affect the
quality of sensing w.r.t. completeness, this subset is carefully chosen based
on a probabilistic sensing model that defines the probability of mobile devices
to successfully perform a given sensing query.
Our evaluations show that with our optimized sensing query distribution, the
energy consumption can be reduced by more than 70\% without significantly
reducing the quality of sensing.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2013-35&engl=1}
}
@inproceedings {INPROC-2013-33,
author = {Patrick Baier and Frank D{\"u}rr and Kurt Rothermel},
title = {{Opportunistic Position Update Protocols for Mobile Devices}},
booktitle = {Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013)},
publisher = {ACM},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--9},
type = {Conference Paper},
month = {September},
year = {2013},
language = {English},
cr-category = {C.2 Computer-Communication Networks},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2013-33/INPROC-2013-33.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Many location-based applications such as geo-social networks rely on location
services storing mobile object positions. To update positions on location
servers, position update protocols are used. On the one hand, these protocols
decide when an update has to be sent to ensure a certain quality of position
information. On the other hand, they try to minimize the energy consumption of
the mobile device by reducing communication to a minimum.
In this paper, we show how to improve the energy efficiency of different update
protocols by taking the energy characteristics of the mobile network interface
into account. In particular, we show that the energy consumption can be reduced
on average by 70\% using an opportunistic update strategy sending position
updates together with messages of other applications. We present a Markov model
to predict the arrival of messages and an online optimization algorithm
calculating an optimized schedule to send position updates.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2013-33&engl=1}
}
@inproceedings {INPROC-2012-26,
author = {Patrick Baier and Frank D{\"u}rr and Kurt Rothermel},
title = {{TOMP: Opportunistic Traffic Offloading Using Movement Predictions}},
booktitle = {Proceedings of the 37th IEEE Conference on Local Computer Networks (LCN)},
address = {Clearwater},
publisher = {IEEE Computer Society},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--8},
type = {Conference Paper},
month = {October},
year = {2012},
language = {English},
cr-category = {C.2 Computer-Communication Networks},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2012-26/INPROC-2012-26.pdf,
http://www.comnsense.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Recent forecasts predict that the amount of cellular data traffic will
significantly increase within the next few years. The reason for this trend is
on the one hand the high growth rate of mobile Internet users and on the other
hand the growing popularity of high bandwidth streaming applications. Given the
fact that cellular networks (e.g. UMTS) have only limited capacity, the
existing network infrastructure will soon reach its limits. As a result, the
concept of traffic offloading attracts more and more attention in research
since it aims at the reduction of cellular traffic by shifting it to local-area
networks like Wifi. Within the last few years, some first approaches for
automatically offloading cellular traffic were proposed. These approaches
either assume the wide availability of publicly accessible Wifi networks or
knowledge about social relations of mobile users. However, these assumptions
are usually not fulfilled. To face this issue, we developed the TOMP system.
TOMP implements a system to distribute data from the infrastructure to a set of
mobile devices by partly shifting traffic from the cellular network to the
level of inter-device communication. In contrast to the prevailing approaches,
TOMP does not rely on open Wifi networks and only uses information about the
position and speed of mobile device. By using predictions about the future
movement of mobile users, TOMP determines devices that are most suitable
targets for traffic offloading. In this paper we show by simulation that TOMP
can save up to 40\% of cellular messages in comparison to a typical cellular
network.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2012-26&engl=1}
}
@inproceedings {INPROC-2012-01,
author = {Patrick Baier and Frank D{\"u}rr and Kurt Rothermel},
title = {{PSense: Reducing Energy Consumption in Public Sensing Systems}},
booktitle = {Proceedings of the 26th IEEE International Conference on Advanced Information Networking and Applications (AINA-2012)},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {136--143},
type = {Conference Paper},
month = {March},
year = {2012},
doi = {10.1109/AINA.2012.33},
keywords = {ad-hoc; mobile; public sensing},
language = {English},
cr-category = {C.2 Computer-Communication Networks},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2012-01/INPROC-2012-01.pdf,
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6184863&isnumber=6184848,
http://www.comnsense.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Utilizing peoples' mobile devices for gathering sensor data has attracted a lot
of attention within the last few years. As a result, a great variety of systems
for sensing environmental phenomena like temperature or noise have been
proposed. However, most of these systems do not take into account that mobile
devices have only limited energy resources. For instance, an often assumed
prerequisite is that mobile devices are always aware of their position. Given
the fact that a position fix is a very energy consuming operation, continuous
positioning would quickly drain a device's battery. Since the owners of the
mobile devices will not tolerate a significant reduction of the devices'
battery lifetime, such an approach is not suitable. To address this issue we
present PSense, a flexible system for efficiently gathering sensor data with
mobile devices. By avoiding unnecessary position fixes, PSense reduces the
energy consumption of mobile devices by up to 70\% compared to existing mobile
sensing approaches. This is achieved by introducing an adaptive positioning
mechanism and by utilizing energy efficient short-range communication to
exchange position related information.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2012-01&engl=1}
}
@inproceedings {INPROC-2011-74,
author = {Patrick Baier and Harald Weinschrott and Frank D{\"u}rr},
title = {{Effiziente automatisierte Erstellung von Stra{\ss}enkarten}},
booktitle = {7.GI/ITG KuVS-Fachgespr{\"a}ch. Ortsbezogene Anwendungen und Dienste.},
editor = {Roth J{\"o}rg K{\"u}pper Axel},
address = {Berlin},
publisher = {Logos Verlag Berlin GmbH},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {85--92},
type = {Conference Paper},
month = {September},
year = {2011},
isbn = {978-3-8325-2935-2},
language = {German},
cr-category = {C.2.1 Network Architecture and Design,
C.2.2 Network Protocols,
C.2.3 Network Operations,
C.2.4 Distributed Systems,
E.1 Data Structures},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2011-74/INPROC-2011-74.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Das relativ junge Paradigma des Urban Sensing erm{\"o}glicht die kosteng{\"u}nstige
Bereitstellung von Sensordaten, welche in diesem Umfang bisher nicht zug{\"a}nglich
waren. Ein potentieller Verwendungszweck dieser Daten liegt im Bereich der
Kartografie, indem von Mobilger{\"a}ten erfasste GPS-Daten genutzt werden, um
Stra{\ss}enkarten automatisch zu erstellen. Dadurch kann eine Ersparnis
hinsichtlich Aufwand und Kosten, im Vergleich zu konventionellen Methoden der
Kartenerstellung, erzielt werden. Diese Arbeit stellt einen solchen Ansatz zur
effizienten, automatisierten Erstellung von Stra{\ss}enkarten mithilfe von
GPS-Sensordaten vor. Diese Daten werden dabei automatisch von Personen
gesammelt, die ihre Mobilger{\"a}te wie gewohnt mit sich f{\"u}hren, zus{\"a}tzlich aber
auf ihren allt{\"a}glichen Wegen GPS-Positionsinformationen erfassen, welche sie
einem zentralen System zur Verf{\"u}gung stellen. Dies geschieht automatisch, ohne
dass eine Interaktion dieser Personen n{\"o}tig ist. Im Gegenzug soll der
Ressourcenverbrauch der teilnehmenden Mobilger{\"a}te m{\"o}glichst minimiert werden.
Daher koordiniert der in dieser Arbeit vorgestellte Ansatz die Erfassung der
GPS-Daten so, dass die Mobilger{\"a}te diese m{\"o}glichst nur dann erfassen, wenn sie
sich in einem Gebiet befinden, welches bis zu diesem Zeitpunkt noch nicht
ausreichend kartografisch erfasst wurde. Um diese gezielte Koordination der
Mobilger{\"a}te zu erm{\"o}glichen, werden im Rahmen dieser Arbeit Qualit{\"a}tsmetriken
f{\"u}r Geodaten vorgestellt, welche den erfassten Stra{\ss}en quantitative Gr{\"o}{\ss}en
zuordnen, um so einen Vergleich dieser Daten zu erm{\"o}glichen.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2011-74&engl=1}
}
@inproceedings {INPROC-2011-51,
author = {Patrick Baier and Harald Weinschrott and Frank D{\"u}rr and Kurt Rothermel},
title = {{MapCorrect: Automatic Correction and Validation of Road Maps Using Public Sensing}},
booktitle = {36th Annual IEEE Conference on Local Computer Networks (LCN 2011)},
address = {Bonn, Germany},
publisher = {IEEE Computer Society},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--8},
type = {Conference Paper},
month = {October},
year = {2011},
keywords = {ad-hoc; mobile; public sensing},
language = {English},
cr-category = {C.2 Computer-Communication Networks},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2011-51/INPROC-2011-51.pdf,
http://www.comnsense.de},
contact = {patrick.baier@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {With the increasing proliferation of small and cheap GPS receivers, a new way
of generating road maps could be witnessed over the last few years.
Participatory mapping approaches like OpenStreetMap, for instance, introduced a
way to generate road maps collaboratively from scratch. Nevertheless, one of
the main problems of these maps is their unknown quality in terms of accuracy.
To address this issue, we propose MapCorrect: An automatic map correction and
validation system. MapCorrect automatically collects GPS traces from people's
mobile devices to correct a given road map and validate it. Since the
collection of GPS data raises concerns about the energy consumption of the
participating mobile devices, we tackle this issue by introducing a selective
sensing mechanism. Furthermore, we show by simulation that using this approach
up to 50\% of energy on the mobile phones can be saved while not impairing the
map correction and validation process at all.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2011-51&engl=1}
}