10 Location Based Services

(1)

10.1 Positioning

10.2 Car Navigation 10.3 Map Matching 10.4 Privacy

10.5 Summary

10 Location Based Services

(2)

• Location Based Services (LBS) are services,

– That are accessible with mobile devices through a mobile network

– Integrating a mobile device’s location with other

information so as to provide added value to the user

• Classification of LBS applications

– Person-oriented

• Person located can control the service

– Device-oriented

• Person or object located is not controlling the service

10 Location Based Services

https://www.acid21.com/

(3)

• Types of application design

– Pull Services

– Push Services

10 Location Based Services

Event Data transfer

Request Response

(4)

• Application examples

10 Location Based Services

Push Services Pull Services Person-

oriented

Communication A message is pushed to you asking whether you allow a friend to locate you

You request from a friend finder application who is near you

Information You get an alert that a terror alarm has been issued by the city you are in

You look for the nearest cinema in your area and navigation instructions to get there M-Commerce

and Advertising

A discount voucher is being sent to you from a restaurant in the area you are in

You look for events happening in the area you are in

Device- oriented

An alert is sent to you from an asset-tracking application that one of your shipments has just deviated from its foreseen route

You request information on where your truck fleet currently is located in the country

(5)

• Application areas

– Travel services

• Routing, city, hotel and restaurant guides

– Emergency

• Locating a person in need, warn persons in danger

– Fleet management – Phone tracker

• Child watch, anti-theft tracking

– Friend finder, blind dating – Mobile phone tariffs

10 Location Based Services

http://sourceforge.net/dbimage.php?id=132227

(6)

• Self-locating: User (his mobile device) locates himself

– Global Positioning System (GPS, GLONASS, Galileo) – Manual position input (not smart)

• External locating: Operator

locates the user’s mobile device

– Determining the current mobile network cell – Signal comparison between three adjacent cells

• Locating provided by third parties

– Location transponder – Peer-to-Peer locating

10.1 Positioning

http://www.nundenn.de/

(7)

• Global Positioning System (GPS)

– Enables three dimensional positioning near the earth – Measuring the runtime of signals between the satellite

and the GPS-receiver, from which the distance and the position can be deduced (trilateration)

– The transmitted signal describes a circular sphere centered at the

satellite on which surface the signal is received at the same

time → circular baseline of equal receiving times

on earth

10.1 Positioning

http://www.uni-giessen.de/ilr/frede/

lehrveranstaltungen/MP_51/2.6-GPS.pdf

(8)

– From the intersection of baselines of (at least) three satellites the position on the earths surface is deduced – Prerequisite is the temporal coordination of the

satellites and the receiver, otherwise the baseline of a fourth satellite is needed

10.1 GPS

http://www.uni-giessen.de/.../2.6-GPS.pdf [La13]

(9)

• Illustrating the equation for one

satellite

10.1 GPS

(10)

– Navigational Satellite Timing and Ranging - Global Positioning System (NAVSTAR-GPS)

• Currently the most important positioning and navigation system worldwide

• Operator: United States Air Force

• 1973: decision on development

• 1978-1985: launch of 11 block I-satellites

• March 1994: launch of the last block II-satellites

• July 1995: full operational capability

• 1.5.2000: enhanced accuracy for civilian users (from approx.

100m to 20m)

10.1 GPS

https://timeandnavigation.si.edu/

(11)

– Three major segments: space, control and user segment

– Between 24 and 32 satellites – Six orbits

• 55^oinclination each

• 20200 km altitude

• 11 h 55 min orbital period

– From every point at earth at least 5 satellites are always visible

– 5 different types of satellites:

Block I, Block II, Block IIA, Block IIR, and Block IIF

Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 852

10.1 GPS

http://www.iww.forst.uni-goettingen.de/

(12)

– Block II satellites

• Weight: 1500 kg

• Span 5.1 m

• Durability 7.5 years

• 2 (IIR 3) rubidium and 2 cesium atomic clocks with a stability of at least 10 ^{- 13}s

• Power source: solar panels 750 watt

• Radiated power 50 watt

• A hydrazine propulsion system for orbital correction

10.1 GPS

http://www.kowoma.de/

gps/Satelliten.htm

IIA IIR

http://www.irs.uni-stuttgart.de/

(13)

– Control segment

• Almost passive ground stations to track the flight paths of the satellites

4 monitoring stations operated by the U.S. air force

Since end of 2005 6 more stations operated by the NGA (National Geospatial-Intelligence Agency)

One "Master Control Station" for evaluation

• Every satellite can

always be received

by at least two ground stations

10.1 GPS

http://www.kowoma.de/gps/Bodenstationen.htm

(14)

– GPS-receiver

• Composed of an antenna, receiver-processors, and a highly- stable clock

• May also include a display for providing location and speed information

• Dedicated devices or integrated into cars, mobile phones,

watches, etc.

10.1 GPS

http://www.ecvv.com/

http://3.imimg.com/

(15)

– Message format

• Week and time within the week

• Position

10.1 GPS

every 6 seconds

http://imaging.utk.edu/publications/papers/dissertation/Anis_Pilot.pdf

(16)

• Health of the satellite

• Ephemeris

– In a spherical polar coordinate system – Updated every 2h

• Almanac

– Coarse orbit and status information for all satellites

– Data related to error correction

– Updated every 24h

10.1 GPS

http://www.uni-giessen.de/ilr/frede/

lehrveranstaltungen/MP_51/2.6-GPS.pdf

(17)

– Random errors: current local conditions at the location of the receiver

• Dense forests: signal damping

• Mountains, narrow valleys, street canyons: shadowing effects

• Large buildings: multipath effect

• Tall metal construction elements:

echo effect, signal distortion

• Strong transmitters and high-voltage power lines

– Best receiving conditions in the open country with small relief

10.1 GPS

http://www.kowoma.de/en/gps/errors.htm

(18)

– Clock errors

• Relativistic effects

– Time moves the faster the weaker the field of gravitation is – Time runs slower during very fast movements

• Inaccuracy of the time of the receiver clock

– Change of speed of propagation of radio waves in the atmosphere

• Ionosphere (80-900 km): electromagnetic waves are slowed down inversely proportional to the square of their frequency

• Troposphere (0-15 km): refraction due to different concentrations of

water vapour

10.1 GPS

http://www.kowoma.de/en/gps/errors.htm

(19)

• Differential GPS (DGPS)

10.1 GPS

base or reference station:

set up on a precisely known location calculates its position based on

satellite signals and compares this location to the known location

correction signal GPS receiver

calculates its position based on satellite signals and applies the difference

underlying premise: any two receivers that are relatively close together will experience similar atmospheric errors

(20)

• Cell of origin (COO)

– Every cell belongs to one base station

– Different frequencies are used in neighbouring cells

– Base stations know about mobile phones

within their cell

– Accuracy of the position

depends on the size of the cell

10.1 Positioning

http://www.cisco.com/c/dam/en/us/

http://emf2.bundesnetzagentur.de/karte.html

(21)

• Signal comparison

– Cell phone is in contact with several base stations – Either base station calculates the position

• Angle of arrival (AOA)

• Signal strength

• Time of arrival (TOA)

– Or cell phone calculates its position

• Signal running time difference

– Accuracy: approx. 50-200m

10.1 Positioning

http://etutorials.org/shared/

(22)

• Location transponder

– Mobile device gets location information from transponders (e.g. RFID)

– Advantages

• Quite precise: 1-5m

• May send additional information

• Suitable for indoor applications

– Disadvantages

• Mobile device needs additional software

• Transponders have to cover the whole area

• Synchronization of transponders and applications is necessary

10.1 Positioning

• Peer-to-peer positioning

– Exchange of position information between mobile devices (C2C, C2X)

– Advantages

• Decentralized position information acquisition

• Position information

from specialized devices

– Disadvantages

• No standard established yet

• Chicken-and-egg problem

10.1 Positioning

https://www.car-2-car.org/

(27)

• In-car navigation systems support the driver by

– Showing the vehicle's current location on a map – Giving visual and audio information on how to

efficiently get

from one location to another

(route guidance)

10.2 Car Navigation

http://www.giga.de/apps/navigon/

(28)

• Components of a navigation system

10.2 Car Navigation

traffic reports

map section (current position)

communication

sensor system mass storage

positioning map

matching

HMI routing

route guidance

digital map

traffic reports

measurements

screen speech

input keyboard

speech output

map data (off board)

> route segments

< rerouting

> operating instructions

< status

> route segments, route list

< chosen route

map section (start)

(29)

• GDF (Geographic Data Files)

– Standardized geometrical and topological model for road networks and other geographic data

– Interchange file format – Contains rules for

• Data capture

• Management

• Representation

– Nationwide GDF-data for Germany since 1995

– A GDF database (in the exchange format) will never be used as such

10.2 GDF

(30)

• Level 0: geometry and topology

– Nodes, edges and faces

• Level 1: features

– "Simple features" e.g. road segments, rivers, borders and road signs

– Features may have specific

attributes (e.g.

one way street,

roadway width,

number of lanes) and relations

10.2 GDF

http://www.ertico.com/download/misc/GDF/gdf_intro.pdf

(31)

• Level 2: complex features

– Aggregation of simple features

– Simplified representation (Generalization)

10.2 GDF

(32)

10.2 GDF

Junction

Road Element Level 1

Level 2

Inter- section

http://www.ikg.uni-bonn.de/vorlesungsarchiv/Gis_iv_SS_02/Vortraege/Becker.ppt

(33)

• Features are grouped into feature themes

10.2 GDF

(34)

• Feature Themes are divided into Feature Classes

– Example: Theme: Roads and ferries(Code 41)

Classes: 4110 RoadElement

4120 Junction

4130 FerryConnection

• Physical representation of GDF

– In ASCII files, ISO 8251-1

– A country consists of a collection of files

– A file contains several records, one for every GDF-element e.g.

a PointFeature is one (logical) record – Mediarecords of 80 signs each

– Data within one record is exactly defined by fields and length

10.2 GDF

(35)

10.2 GDF

52= Line Feature ID = 20001532 Feature Code = 4110=RoadElement Split = 0

#Edges = 1

20003220

#Faces = 0

#Attributes = 1 20002083 From 20001751 To 20001752 24 = Edge Feature

ID = 20003220 from 20002222 to 20002223 Face 20000611 Rightface 20003926 Status = 2

51 = Point Feature ID = 20001751 Feature Code = 4120 = junction

#Knots = 1

20002222

#Attributes = 0

51 = Point Feature ID = 20001752 Feature Code = 4120 = junction

#Knots = 1

20002223

#Attributes = 0 25 = New Node

ID = 20002222 XYZ-ID 20009835 Status = 2 (normal)

25 = New Node ID = 20002223 XYZ-ID 20009837 Status = 2 (normal)

44= Segm Attribute ID = 20002083

#Attributes = 6

ON Official Name 20001871 AN Alternate Name 20001872 FC Functional Road Class

1 8B Net 1 Class 3 FW Form of Way 4 Part of a roundabout DF Direction of Traffic 3 Closed in negative 41 = Name Record

ID = 20001871

http://www.ikg.uni-bonn.de/.../Becker.ppt

(39)

• Relations

– Not topological e.g. the relation ´is capital of´

between Paris and France

– Usually relations between two features

– N-nary relations also possible, e.g. a bridge leads a road over a river

10.2 GDF

http://www.ertico.com/download/misc/GDF/data_model.pdf

(40)

• Automotive navigation

– Pilotage, recognition of "landmarks"

– Dead reckoning

• Previously determined position

• Recordings of the wheel rotation and steering direction

– Inertial navigation systems (INS)

• Initially provided with the car’s position, orientation, and speed

• Recordings of motion and rotation sensors

– Radiolocation

• Measuring phase and runtime of radiowaves

• Terrestrial (e.g. UMTS) or satellite-based (GPS)

10.2 Car Navigation

www.ikg.uni-hannover.de

(41)

• Sensor system

– Wheel speed sensors

• Provide the speed of the vehicle and the covered distance

• Electronic tacho-signal

– Odometer

• Measuring the covered distance

• Change of direction can be calculated using two odometers, one each for the wheels on an axle (differential odometer)

– Electronic compass – Gyroscope

• Current sensors most often are built in micro- electro-mechanical systems technique (MEMS)

10.2 Car Navigation

http://vnc.thewpp.ca/

(42)

• Technology of very small devices

– Evolved from semiconductor device fabrication

– Materials are silicon, polymers, ceramics

• Main criterion is that there are elements having some

sort of mechanical functionality

• Microsensors convert a measured mechanical signal into an electrical signal

10.2 MEMS Sensors

http://www.fst.umac.mo/

(43)

• Kinds of sensors

– Accelerometer – Gyroscope

– Magnetometer – Thermal sensor – Pressure sensor

• Inertial sensor system:

Accelerometer + gyroscope

10.2 MEMS Sensors

http://britneyspears.ac/physics/

(44)

• Accelerometer

– Consists of a spring- mass system

– Often capacitance change due to acceleration force is used (relatively

insensitive to temperature)

10.2 MEMS Sensors

http://www.princeton.edu/

"half of a capacitor", mass, moving

"half of a capacitor", immobile spring

(45)

– Parameters

• Mass m

• Spring stiffness coefficient k

• Resonance frequency (5-22 kHz)

– Challenges

• ∆Capacity approx. 1 fF (10^-15 F)

• Acceleration 1-50 g

• Application frequency < resonance frequency

– Applications

• INS, airbags

• Smart phones, hard disk head crash protection

10.2 MEMS Sensors

http://www.conrad.de/

http://www.kfztech.de/

(46)

• Gyroscope

– Often realized as vibrating structure gyroscope – Depends on the Coriolis force

(deflecting a moving object within a rotating reference system)

– Typical device: tuning fork

• Contain a pair of masses that are driven to oscillate with equal amplitude but in opposite directions

• When rotated, the Coriolis force creates an orthogonal vibration that can be sensed

10.2 MEMS Sensors

http://www.sandia.gov/

(47)

• Inertial navigation system

– Three gyroscopes and three accelerometers are combined in an inertial measurement unit (IMU)

– Current systems are highly integrated and cost about 5 €

(footprint: 3 mm x 4.5 mm, resolution: 0.98mg, 0.004°/s)

10.2 MEMS Sensors

http://www.zess.uni-siegen.de/

http://www.electronicproducts.com/

(48)

• Problems

– Dead reckoning and INS: the errors are cumulative – GPS: in urban areas often

unavailable or degraded

• Goal

– Accuracy of odometer and gyroscope

– Long-term accuracy of GPS

• Solution: sensor fusion

– GPS + odometer: good but perhaps problems with turns in tunnels

– GPS + odometer + gyroscope

10.2 Car Navigation

http://gpsworld.com/wp-content/

(49)

• Sensor fusion

– Combining of data so that the resulting information is

"better" than when the

sources were used

individually

– Complex task

10.2 Car Navigation

[MMG12]

(50)

– Odometer + steering direction  position p_DR – Accelerometers + gyroscopes  position p_INS – Satellite positioning system  position p_GPS

– Wanted:

position p_EST= combination(p_DR, p_INS , p_GPS)

10.2 Car Navigation

(51)

• Kalman filter

– Operates recursively on streams of noisy input data – Produces a statistically optimal estimate of the

underlying system state

(optimal filter for a linear model subject to Gaussian noise)

10.2 Car Navigation

http://www.swarthmore.edu/NatSci/

(52)

• Routing

– Search the best way in a weighted graph – Cost

• Distance

• Effective velocity

• Energy efficiency

– Algorithms (chap. 2.6)

• Dijkstra

• Bellman-Ford

• Floyd-Warshall

10.2 Car Navigation

[St12]

(53)

• Map matching

– Process of aligning a sequence of observed user positions with the road network on a digital map – Influencing factors

• Accuracy of the positioning

• Systematic error behavior of the used sensors

• Quality of the map

10.3 Map Matching

http://www.dieter.pfoser.org/

(54)

10.3 Map Matching

http://www.uni-stuttgart.de/iagb/forschung/messtechnik/projekte/mapmatching-Dateien/Cz_Internet_Strasse.pdf

(55)

– Simplified matching algorithm

10.3 Map Matching

measure position determine error region

determine possible roads

Chose "bes t"

matching does

calculated one?

region equals measured adjust position

yes no

(56)

• Point-to-Point Matching

– Match the measured position to the "closest" point (node) on the map

– Difficulties

• The position is usually not represented as node on the map it is one point on a line (street) instead

• Streets with more shape points are chosen with higher probability

10.3 Map Matching

(57)

• Point-to-Curve Matching

– Identify the road segment which is "closest" to the measured point

– Distance measures might be

• Minimum distance, mean distance, Hausdorff distance

• Let A denote the segment between a=(a₁,a₂) and b=(b₁,b₂).

The minimum distance between some point c=(c₁,c₂) and A is the minimum of:

with

– Shortcoming: No use of "historical" information

10.3 Map Matching

(58)

• Curve-to-Curve Matching

– A better way to proceed is to consider m positions simultaneously

10.3 Map Matching

http://ebus.informatik.uni-leipzig..../vt06-ve07-pdf.pdf

(59)

– Further criteria to evaluate the quality of a matching

• Angle between segments and the direction of movement

• Connectivity of segments

– Additional information that might help to improve the quality or reduce the

computation costs

• Allowed driving directions (one way streets, ban on turns, separated lanes)

• Driving speed e.g. helps to decide whether a car drives on a highway or a street parallel to it

10.3 Map Matching

(60)

• Threats to privacy

– Subsequent queries of a user’s location may be used to create a movement profile

– Tracking of persons possible (stalking) – Danger of a

"glassy citizen"

• Data privacy

protection necessary

10.4 Privacy

http://pixelbuy.com/Ebay/Import/GPS%20TK202/google+.jpg

(61)

• Legal aspects

– Directive on privacy and electronic communications of the European Parliament (2002/58/EC, 2009/136/EC)

10.4 Privacy

(62)

• Classification of system architectures

– Non-cooperative architecture

• Users depend only on their knowledge to preserve their location privacy

– Centralized trusted party architecture

• A centralized entity is responsible for gathering information and providing the required privacy for each user

– Peer-to-Peer cooperative architecture

• Users collaborate with each other without the interleaving of a centralized entity to provide customized privacy for each single user

10.4 Privacy

(63)

• Non-cooperative architecture

– Landmark objects

• Instead of reporting the exact location, report the location of a closest landmark

• The query answer will be based on the landmark

• Voronoi diagrams can be used to identify the closest landmark

– "False Dummies"

• A user sends m locations, only one of them is the true one while m-1 are false dummies

• The server replies with a service for each received location

• Only the user knows the true location, and hence the true answer

10.4 Privacy

http://mdm2007.uni-mannheim.de

(64)

• Centralized trusted party architecture

– Quadtree Spatial Cloaking

• Achieve k-anonymity, i.e., a user

is indistinguishable from other

k-1 users

• Recursively divide the space into quadrants until a quadrant has less than k users

• The previous quadrant, which still meet the k-anonymity constraint, is returned

10.4 Privacy

Goal: 5-Anonymity for

http://mdm2007.uni-mannheim.de/downloads/

Seminar_Mokbel_Aref_MDM_2007-05-09.ppt

(65)

• Example

for a 9-anonymity

in a crowd of 70 people

10.4 Privacy

(66)

– Clique Cloak Algorithm

• Each user requests

A (k=3)

C (k=2) B (k=4)

D (k=4) F (k=5)

H (k=4) E (k=3)

m (k=3)

(79)

– Nearest-Neighbor k-Anonymizing

• Determine a set S containing u and k - 1 u’s nearest neighbors

• Randomly select an element v from S

• Determine a set S’ containing v and v’s k - 1 nearest neighbors

• The MBR of all users in S’ and

u is taken as the u’s location

10.4 Privacy

(80)

10.4 Privacy

S

(81)

10.4 Privacy

S

S’

(82)

10.4 Privacy

S

S’

(83)

– Basic Pyramid Structure

• The entire system area is represented as a complete pyramid structure

divided into grids at different levels of various resolution

• Each grid cell maintains the number of users in that cell

• To anonymize a user request, we traverse the pyramid structure from the bottom level to the top level

until a cell satisfying the user privacy

profile is found

10.4 Privacy

http://mdm2007.uni-mannheim.de/.../Seminar_Mokbel_Aref_MDM_2007-05-09.ppt

(84)

• Peer-to-peer based systems

– Peer searching

• Broadcast a multi-hop request until at least k-1 peers are found

– Location adjustment – Spatial Cloaking

• Blur user location into

a region aligned to a

grid that cover the

k-1 nearest peers

10.4 Privacy

(85)

• Positioning

– GPS

– Network-based locating – Location transponder

• Car Navigation

– GDF

– Sensor system

– Navigation systems – Routing

10.5 Summary

(86)

• Map Matching

• Privacy

– System architectures – Algorithms

10.5 Summary

(87)

10.5 Summary

GIS collect

manage

analyse

display

operations on

graphs

LBS

navigation systems

encryption of locations GDF

position

map matching

routing