Ortsbezogene Anwendungen und Dienste

192 Pages·2013·9.4 MB·English

Matthias Werner, Mario Haustein (Hrsg.)
Ortsbezogene Anwendungen und Dienste
9. Fachgespräch der GI/ITG-Fachgruppe Kommunikation und Verteilte Systeme
wirkende Hrsg.)
Mit n (
Luftbild: TU Chemnitz/Wolfgang ThiemeKarte: Jens Pönisch, Geodaten © OpenStreetMap-http://www.openstreetmap.org/copyrightMontage: Mario Haustein, Michael Kunz Matthias Werner, Mario Haustei
ISBN 978-3-941003-77-4 Fakultät für Informatik . www.tu-chemnitz.de/informatik
Prof. Dr. Matthias Werner, Mario Haustein (Hrsg.)

Ortsbezogene Anwendungen und Dienste

- 9. Fachgespräch der GI/ITG-Fachgruppe
Kommunikation und Verteilte Systeme -

Prof. Dr. Matthias Werner, Mario Haustein (Hrsg.)

Ortsbezogene Anwendungen und Dienste

9. Fachgespräch der GI/ITG-Fachgruppe Kommunikation
und Verteilte Systeme
13. & 14. September 2012

Universitätsverlag Chemnitz
2013
Impressum

Bibliografische Information der Deutschen Nationalbibliothek

Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der
Deutschen Nationalbibliografie; detaillierte bibliografische Angaben sind im
Internet über http://dnb.d-nb.de abrufbar.

Cover
Luftbild: TU Chemnitz/Wolfgang Thieme
Karte: Jens Pönisch, Geodaten (c) OpenStreetMap-Mitwirkende
http://www.openstreetmap.org/copyright
Montage: Mario Haustein, Michael Kunz

Technische Universität Chemnitz/Universitätsbibliothek
Universitätsverlag Chemnitz
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ISBN 978-3-941003-77-4

http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-104609
Tagungsbeiträge
Vorträge 6
G.Eichler,R.Schwaiger:
Fromsingledevicelocalizationtowardsmobilenetwork-basedroutecalculation. . . 7
D.Bade,D.Gleim:
TowardsSensor-supportedIndoorLocalizationUsingCloud-basedMachine
LearningTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
P.Reisdorf,M.Obst,G.Wanielik:
Bayes-FilterfürkombinierteGPS/GLONASS-Lokalisierung. . . . . . . . . . . . . 35
F.Dorfmeister,M.Maier,M.Schönfeld,S.Verclas:
SmartBEEs:EnablingSmartBusinessEnvironmentsBasedonLocationInformation
andSensorNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
D.Ludewig,C.Kleiner:
KonzepteundImplementierungzurVerbesserungderPrivatsphärebeiortsbezogenen
mobilenDiensten. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
J.Roth:
ASpatialHashtableOptimizedforMobileStorageonSmartPhones. . . . . . . . . 71
F.Fuchs-Kittowski:
IntegrationundBereitstellungvonortsbezogenenDatenfürmobileAR-Anwendungen 85
M.Maier,F.Dorfmeister,M.Schönfeld,M.Kessel:
AToolforVisualizingandEditingMultipleParallelTracksofTimeSeriesDatafrom
SensorLogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
M.Haustein,A.Löscher,M.Werner:
AdaptiveObjektlokalisierungdurchTiefenbildanalysemittelseinerKinect-Kamera . 109
J.Roth:
ModularisierteRoutenplanungmitderdonavio-Umgebung . . . . . . . . . . . . . 119
M.Kessel,M.Maier,M.Schönfeld,F.Dorfmeister:
TestingSensorFusionAlgorithmsinIndoorPositioningScenarios. . . . . . . . . . 133
M.Schirmer,H.Höpfner:
Vademecum:einneuerAnsatzfürdiePOI-Auswahlineinemmobilen
InformationssystemfürTouristen1
S.Siegl,C.Kleiner:
VergleichplattformübergreifenderundnativermobilerAnwendungenfürortsbasierte
WebServices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
F.Linke:
Kontext-sensitiveDiensteimNotfallmanagement2
1DerVollartikelzumVortragkonntewegendesTodesvonProf.HagenHöpfnerimOktober2012leidernichtfertiggestelltwerden.
2ZudiesemVortragliegtkeinVollartikelvor.
5
Poster 157
L.Fischer,A.Hoffmann,N.Hahn:
IndoorPositioningbyFusionofIEEE802.11FingerprintingandCompassBearing . 157
T.M.Stupp,D.Graff,A.Busse,J.Richling:
EinTaskmodellfürRaum-Zeit-Scheduling . . . . . . . . . . . . . . . . . . . . . . 161
M.Schönfeld,M.Werner,F.Dorfmeister:
Location-basedAccessControlProvidingOne-timePasswordsThrough2DBarcodes 165
A.Heller:
LokalisierungmobilerRobotermittelsRFID-NaviFloor . . . . . . . . . . . . . . . 169
V.Schau,S.Späthe,C.Erfurth,W.Rossak,K.Krohn:
Lessonlearned:MobileundSelbstorganisierendeKommunikations-und
DatenplattformfürEinsatzkräfteimProjektSpeedUp . . . . . . . . . . . . . . . . 181
S.Zickau,M.Slawik,D.Thatmann,A.Küpper:
TowardsLocation-basedServicesinaCloudComputingEcosystem . . . . . . . . . 187
S.Göndör,P.Ruppel,J.Devendraraj:
TowardsMobile-HostedLocation-BasedSocialNetworks3
3ZudiesemPosterliegtkeinExtendedAbstractvor.
6
From single device localization towards
mobile network-based route calculation
1 Gerald Eichler, 2 Roland Schwaiger
Deutsche Telekom AG, Telekom Innovation Laboratories
Internet & Services: Information Relevance
1 Deutsche-Telekom-Allee 7, D-64295 Darmstadt, Germany
[email protected]
2 Ernst-Reuter-Platz 7, D-10587 Berlin, Germany
[email protected]

Abstract: The contribution analyses and compares several methods for device- and network-centric
localization. The paper introduces several approaches for route calculation by schedule-based context
enrichment and an efficient solution for ticketing in public transport. Route accuracy is verified by
experimental results from a field trial with several smartphone localization techniques. Aggregated,
anonymized and vectorized data is a pre-condition for enhanced track and trace solutions and traffic
measurements.
Keywords: location based services, metadata enrichment, vector data mapping, track & trace, public
transport, mobile ticketing.
1 Localization for mobile ticketing
Modern smartphones support multiple methods for device-centric localization. However, some available options
have drawbacks, especially high battery power consumption but also limited indoor access for Global
Positioning (GPS)-based localization, the need for provider specific databases for cell-based localization or the
need for subscription at a third party for Wireless Local Area Network (WLAN)-based solutions. For the last, the
precision and reliability varies within different urban and rural areas dramatically, although co-localization tries
to reduce such effects.
Network-centric route calculations (tracing) mostly imply problems with efficiency and privacy, when extending
punctual localization to track and trace solutions [EiBo06]. Additionally, it implies to comply with much more
regulations as other solutions [WeSo07]. Device-specific localization features are sometimes difficult to access,
depending on the vendor and operating system (OS), although get location is a simple OS system call. Both
approaches offer great advantages, when combining proactive track and trace and reactive geo-fencing.
Route, infrastructure data and schedule mapping are important input factors for traffic measurements and
prediction in public transport. With pseudonymization and anonymization, data can be aggregated to traffic
vectors. Cell change is an efficient trigger point for device-driven self-localization. A future option for enhanced
statistics is provider-controlled signaling-based localization (SBL).
The “Ring&Ride” project is a good example how a user can profit from a Location Based Service (LBS)
ticketing solution with a minimum of effort [LME+09]. In its simplest way, tracking is initiated and stopped by
freecall without any application installation. Within the project, different localization techniques were compared
with the goal of a reliable route calculation for correct tariffing of tickets. To verify the route and identify the
vehicle of transport e.g., train (ICE vs. S-Bahn), tram, bus or walking passages, the collected data is aligned with
integrated timetables of public transport. Any useful context information increases the data quality [ELR09] and
can support the estimation of missing single positions of a track vector.
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2 Mobile ticketing approach
Electronic tickets for the use of public transport become more and more important. The huge number of mobile
smartphones is accompanied by easy-to-install apps and reliable payment solutions. However, most solutions for
the application of electronic tickets are limited either locally or to a dedicated single transport provider e.g.,
Deutsche Bahn. Offers like “city plus” support local traffic at source and destination, but countrywide unique
solutions are still missing. The reason is a lack of interoperable fare management. Public transport vendors are
often not fond of interwork because of a fear that their earnings could be too low while others are the winners. In
other words, there is still a lot of intransparency for real traffic flows.
When thinking of common solutions, three parts of an interactive process chain need to be unified, in order to
add comfort for the traveler (customer), see Figure 1:
1. Point of sales: the front-office, respectively the way, where customer get their contracts and tickets
2. Check-in and check-out: the method how customers indicate the staring point and end of their travel
3. Post processing: the back-office, where fare calculation, billing, statistics and cost sharing is carried out
All three parts contain critical points which need flexible solutions to increase the acceptance rates for both,
customers and public transport providers. Customers do not like long term or complex contracts, want to be very
flexible, prefer different ways to get a ticket, and aim for a certain amount of privacy. Public transport providers
look for efficiency in use of their vehicle capacities, a minimum of technical infrastructure for ticketing to be
maintained and transparent statistics. Last but not least, a lean backoffice is required.
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Figure 1: Integrated mobile ticketing solution for interoperable fair management
2.1 Use case and role model
The general use case is fairly simple:
A customer C wants to travel ad-hoc from A to B on Route R with a single ticket T using vehicles
V1, V2, …, Vn provided by public transport providers P1, P2, …, Pn.
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To reach this goal, a clear role distribution of the involved parties is required. At least one party needs to be the
service owner, being in charge as legal unit and subcontracting all required sub-services, see Figure 2. There is
no need to establish a new company. It is preferred to select an established “Verkehrsverbund”, which is best
qualified for this role.
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CCuussttoommeerr TTrraannssppoorrttaattiioonn
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Figure 2: Role model for interoperable fair management
2.2 Tracing and additional information sources
Smartphones offer multiple options for localization. Mainly known are GPS tracking, mobile network cell ID
tracking or WLAN-based tracking, relying on SSID data bases. A trace is defined as a list of single locations,
described by longitude and latitude. In practice, all these methods result into incomplete or imprecize traces
between check- and check-out. GPS coverage is limited indoors, like in big stations, while cell density and
WLAN is low in rural areas.
However, there is no need for exact traces as there are additional sources for reliable route calculation.
1. Spatial correlation: for all public transport systems, the regular routes are predefined as trains, trams or
buses have dedicated routes.
2. Chronological correlation: transport systems are based on known schedules. Furthermore, real-time data
can significant increase this type of correlation.
By combining customer location data (recorded traces), public network infrastructure data (vehicle stops and
predefined tracks) and timetable data (schedule and delay), a high degree of correct route calculation can be
reached. As a result, origin and destination of the travel as well as the used vehicles per section can be
determined, see Figure 3.
Timetable data e.g., were imported from the HaCon Fahrplan-Auskunfts-System (Hafas)1 database provided by
Deutsche Bahn (DB). The database contains nearly all timetables of public transport in Germany, Austria and
Switzerland as well as real-time information.

1 Hannover Consulting (HaCon), URL: http://www.hacon.de/hafas/
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