1. Introduction Approach Evaluation Conclusions
Context as a Service
Michael Wagner
Distributed Systems Group
University of Kassel
December 2010
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 1

2. Introduction Approach Evaluation Conclusions
Outline
Introduction
Motivation
Quality of context and cost of context
Challenges and objectives
Approach
Context model and ontology
Context Offering and Query Language
Discovery and matching
Selection
Binding
Evaluation
Conclusions
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 2

3. Introduction Approach Evaluation Conclusions
Introduction
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 3

4. Introduction Approach Evaluation Conclusions
Context provider
GPS Sensor
Digital compass
Context sensors
Proximity sensor
Light sensor
Accelerometer
Thermometer
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4

5. Introduction Approach Evaluation Conclusions
Context provider
GPS Sensor Cell-ID based
Position
Digital compass
Context reasoner
Network based
Context sensors
Position
Proximity sensor
Calendar based
Light sensor Position
Activity
Accelerometer
Reasoner
Thermometer Network based
Temperature
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4

6. Introduction Approach Evaluation Conclusions
Context provider
GPS Sensor Cell-ID based
Position Position
Digital compass
Context reasoner
Network based
Context sensors
Position
Proximity sensor
Similar type Calendar based
Light sensor of context Position
information Activity
Accelerometer
Reasoner
Thermometer Network based
Temperature Temperature
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4

7. Introduction Approach Evaluation Conclusions
Additional external context provider
WiFi
Positioning
GPS
GPS
WiFi
Positioning
WiFi
Positioning
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 5

8. Introduction Approach Evaluation Conclusions
Additional external context provider
WiFi
Positioning
GPS
GPS
WiFi
Positioning
WiFi
Positioning
RFID WiFi
Positioning Positioning
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 5

9. Introduction Approach Evaluation Conclusions
Using context in context-aware self-adaptive
applications
Several types of context consumers:
Application business logic: Context-information used within
the actual application (e.g. navigation from the current
position to another position)
Adaptation reasoning: Selection of the “best” variant of the
application with regard to the execution context
Context reasoning and fusion:
Deducing high-level implicit context from low-level explicit
context
Checking the consistency of context
...
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 6

10. Introduction Approach Evaluation Conclusions
Various context providers and consumers
Several context providers
internal and external
potentially providing the same type of information
but differing in quality and cost
and the representation of the information, quality and cost
data
Several context consumers
internal and external
potentially requesting the same type of information
but differing in quality and cost preferences
and the requested representation of the information, quality
and cost data
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 7

11. Introduction Approach Evaluation Conclusions
Current solutions
Most commonly: hard-linked references to context sensors and
reasoners, but
no support for dynamically appearing new context providers.
Few approaches support the dynamic selection and discovery
of context sensors [CAS06, HM04], but
developers have to know the data representations of the
context provider,
no support for activation and deactivation (and the resulting
problems) of context providers in order to save resources.
However, dynamic discovery, data interpretation and energy-saving
are essential requirements in pervasive computing [SHB10].
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 8

12. Introduction Approach Evaluation Conclusions
Quality of Context
“Quality of Context (QoC) is any information that
describes the quality of information that is used as
context information. Thus, QoC refers to information
and not to the process nor the hardware component that
possibly provide the information.”
[BKS03]
Cost of Context
“Cost of Context (CoC) is a parameter associated to the
context that indicates the resource consumption used to
measure or calculate the piece of context information.”
[VRL+ 09]
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 9

13. Introduction Approach Evaluation Conclusions
Context providers differ in the provided QoC, required CoC and the
provided representation of the context information, QoC and CoC.
Problem
Selection and activation of one of the available context providers
and thereby . . .
estimating the QoC of deactivated context providers.
taking into account the heterogeneous representations of
context information and the according QoC and CoC.
trading off the provided QoC and required CoC against the
QoC as requested by the consumer and his preferences
regarding cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 10

14. Introduction Approach Evaluation Conclusions
Context providers differ in the provided QoC, required CoC and the
provided representation of the context information, QoC and CoC.
Problem
Selection and activation of one of the available context providers
and thereby . . .
estimating the QoC of deactivated context providers.
taking into account the heterogeneous representations of
context information and the according QoC and CoC.
trading off the provided QoC and required CoC against the
QoC as requested by the consumer and his preferences
regarding cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 10

15. Introduction Approach Evaluation Conclusions
Challenges, requirements and objectives
Local and remote context sensors and reasoners are abstracted as
context services.
Main challenges:
Dynamic selection of context providers based on QoC and
CoC
Activation and deactivation of context sensors
Additional requirements and objectives:
Exchange and interpretation of heterogeneously represented
context information, QoC and CoC
Loose coupling of context providers and consumers
Dynamic discovery of external context services
Estimation of QoC of deactivated context providers based on
historical context values
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 11

16. Introduction Approach Evaluation Conclusions
Approach
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 12

17. Introduction Approach Evaluation Conclusions
Overview - Context model and ontology
Challenges and requirements: Context model and ontology
Exchange and
interpretation of context
information, QoC and CoC
Context as a Service
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18. Introduction Approach Evaluation Conclusions
Meta-model and Ontology
owl:Thing
is-a
EntityType Scope Representation
characterizes* hasRepresentation*
is-a
hasDimension*
Composite Representation Basic Representation
Entity: Physical or logical entity of the world that is described
by the information, e.g. PDA
Scope: Refers to the type of the provided information, e.g.
Location; meta-data are also considered as scopes
Representation: Describes how the information is internally
structured, e.g. GPS data
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19. Introduction Approach Evaluation Conclusions
Meta-model and Ontology
Ontology is used to provide a common vocabulary to bridge
semantic differences
Defines semantic concepts for entity (types), scopes and
representations
Captures relationships between the defined concepts
Information can be represented as individuals of ontological
concepts/classes
Data structures may be semantically annotated by references
to the ontology
Context as a Service
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20. Introduction Approach Evaluation Conclusions
Meta-model and Ontology
Ontology defines entity types, scopes, representations and
their relationships
Arbitrary number of representations for scopes
hasRepresentation*
Scope Representation
hasRepresentation*
is-a is-a is-a is-a
hasRepresentation*
LocationInfo DateTimeInfo DateTimeRep LocationRep
Date = 14011981
io is-a
is-a
DateTimeCustomRep LocationAddress
DateTimeInfo_Indv1
io hasRepresentation is-a
is-a
hasRepresentation DateTimeDefaultRep LocationWGS84
DateTimeInfo_Indv2
io
Day = 14 io
Month = Januar io
io Year = 1981 Street = Königstor
LocationInfo_Indv1 Number = 12
hasRepresentation City = Kassel
io
io
Latitute = 52.686
LocationInfo_Indv2
Longitude = -2.193
hasRepresentation
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21. Introduction Approach Evaluation Conclusions
Meta-model and Ontology
Internal structuring of context information is defined as
representations in the ontology
Inter-Representation-Operations (IROs) allow conversion
between different representations
Simple conversions, e.g. of units, defined in the ontology itself
Grounding to methods in libraries or to a conversion service
More details of the context model and ontology in
[RWK+ 08a, Rei10, Pas09]
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 17

22. Introduction Approach Evaluation Conclusions
Overview - Context provider and consumer
Challenges and requirements: Context model and ontology
Exchange and interpretation
of context information, QoC
and CoC
Loose coupling
Context Consumer 0..* Context Provider 0..*
Reasoner 0..*
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 18

23. Introduction Approach Evaluation Conclusions
Context Offering and Query Language
Aligned with the context meta-model and the ontology
Simple EMF/XML based language based on the MUSIC
Context Query Language (CQL) [RWK+ 08b] and the
Information Offer and Request Language (IORL) [Rei10]
In difference to the CQL also support for context offers
Support for complex filters and conditions similar to the IORL
In difference to the IORL also support for the different
metadata representations and for context selection
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 19

24. Introduction Approach Evaluation Conclusions
Context Offering and Query Language
We can query for or offer context information
corresponding to a certain scope
characterizing a certain entity
having a certain representation
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 20

25. Introduction Approach Evaluation Conclusions
Context Offering and Query Language - Overview
Context Offer/Request
Scope Representation Subscription
Frequency Source SourceType
Characterized Entity * Selection Function
Entity Recursive Negotiable Utility
*
Entity Constraint Significant change spec.
Constraints
Scope Constraint *
ScopeProperty or ScopeID Operator *
Value Delta
Metadata Constraint *
Metadata class Operator Value Delta Representation
*
*
Sub-Offer/Sub-Request
Context as a Service
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26. Introduction Approach Evaluation Conclusions
COQL - Example
1 <c o q l : COQLDocument xmi : v e r s i o n = [ . . . ]
2 <C o n t e x t Q u e r i e s q u e r y I D=” q u e r y 1 ”
3 s c o p e=” P o s i t i o n ”
4 r e p r e s e n t a t i o n=” P o l a r C o o r d i n a t e ”
5 s u b s c r i p t i o n M o d e=”ONCHANGE”
6 f r e q u e n c y=” 100 ”>
7 < E n t i t i e s e n t i t y R e f=” U s e r | A r a g o r n ”/>
8 </ C o n t e x t Q u e r i e s >
9 </ c o q l : COQLDocument>
Context as a Service
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27. Introduction Approach Evaluation Conclusions
Overview - Discovery and matching
Challenges and requirements: Context model and ontology
Exchange and interpretation Discovery and Matching
of context information, QoC
and CoC
Context Requests
Context offers
Loose coupling
Dynamic discovery
Estimation of QoC
Context Consumer 0..* Context Provider 0..*
Reasoner 0..*
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28. Introduction Approach Evaluation Conclusions
Matching problem
Combination of ontology reasoning and constraint matching
Usually, Constraint Satisfaction Problems (CSPs) are
NP-complete.
However, CSPs try to find an assignment of values to all the
variables so that none of the constraints is violated,
but we are only interested in the satisfiability in general.
→ Most of the solutions for CSPs are too heavy-weight.
→ Light-weight solution currently in research.
Context as a Service
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29. Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Accuracy < BatteryCost <
Entity: User | Paul 1 km 0.1 mWh
Scope: Position Memory <
Rep: CartesianCoordinates 0.5 MB
CONTEXT OFFER 1 CONTEXT OFFER 2
Accuracy: BatteryCost <
Entity: User ∆_longitude < 10 m ᴧ Entity: User 0.1 mWh
Scope: Position ∆_latitude < 10 m Scope: Position
Rep: WGS84 Rep: CartesianCoordinates
BatteryCost < Accuracy =
0.5 mWh 1 cell
GPS Sensor Cell-ID based Location Sensor
Context as a Service
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30. Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Accuracy < BatteryCost <
Entity: User | Paul
Scope: Position
1 km 0.1 mWh
Memory < 1. Scope and scope
Rep: CartesianCoordinates 0.5 MB
constraints
2. Representation
CONTEXT OFFER 1
Accuracy:
CONTEXT OFFER 2
BatteryCost < 3. Entity and entity
Entity: User ∆_longitude < 10 m ᴧ Entity: User 0.1 mWh
Scope: Position
Rep: WGS84
∆_latitude < 10 m Scope: Position
Rep: CartesianCoordinates
constraints
BatteryCost < Accuracy =
0.5 mWh 1 cell 4. Metadata constraints
GPS Sensor Cell-ID based Location Sensor
Conditions: Scopeq = Scopeo or Scopeq is a generalization of Scopeo
or Scopeq = nested scope of Scopeo and scopeConstraint holds!
Context as a Service
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31. Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Accuracy < BatteryCost <
Entity: User | Paul
Scope: Position
1 km 0.1 mWh
Memory < 1. Scope and scope
Rep: CartesianCoordinates 0.5 MB
constraints
2. Representation
CONTEXT OFFER 1
Accuracy:
CONTEXT OFFER 2
BatteryCost < 3. Entity and entity
Entity: User ∆_longitude < 10 m ᴧ Entity: User 0.1 mWh
Scope: Position
Rep: WGS84
∆_latitude < 10 m Scope: Position
Rep: CartesianCoordinates
constraints
BatteryCost < Accuracy =
0.5 mWh 1 cell 4. Metadata constraints
GPS Sensor Cell-ID based Location Sensor
Conditions: Repq = Repo or Repq is a generalization of Repo or
Repo can be transformed to Repq by an IRO
Context as a Service
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32. Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Accuracy < BatteryCost <
Entity: User | Paul
Scope: Position
1 km 0.1 mWh
Memory < 1. Scope and scope
Rep: CartesianCoordinates 0.5 MB
constraints
2. Representation
CONTEXT OFFER 1
Accuracy:
CONTEXT OFFER 2
BatteryCost < 3. Entity and entity
Entity: User ∆_longitude < 10 m ᴧ Entity: User 0.1 mWh
Scope: Position
Rep: WGS84
∆_latitude < 10 m Scope: Position
Rep: CartesianCoordinates
constraints
BatteryCost < Accuracy =
0.5 mWh 1 cell 4. Metadata constraints
GPS Sensor Cell-ID based Location Sensor
Conditions: (Entity q = Entityo or Entityq is a generalization of
Entityo ) and entityConstraint holds!
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26

33. Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Entity: User | Paul
Accuracy <
1 km
BatteryCost <
0.1 mWh
1. Scope and scope
Scope: Position Memory <
Rep: CartesianCoordinates 0.5 MB constraints
2. Representation
CONTEXT OFFER 1 CONTEXT OFFER 2
3. Entity and entity
Accuracy: BatteryCost <
Entity: User ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
Entity: User 0.1 mWh constraints
Scope: Position Scope: Position
Rep: WGS84 Rep: CartesianCoordinates
BatteryCost < Accuracy =
1 cell
4. Metadata constraints
0.5 mWh
GPS Sensor Cell-ID based Location Sensor
1. Metadataq = Metadatao or Metadataq is a generalization of Metadatao
2. Repq = Repo or Repq is a generalization of Repo or Repo can be
transformed to Repo by a IRO
3. Constraintq ∧ Constrainto satisfiable!
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26

34. Introduction Approach Evaluation Conclusions
Example for matching process - Result
CONTEXT QUERY 1
Accuracy < BatteryCost <
Entity: User | Paul 1 km 0.1 mWh
Scope: Position Memory <
Rep: CartesianCoordinates 0.5 MB
No Matching:
BatteryCost
CONTEXT OFFER 1 CONTEXT OFFER 2
Accuracy: BatteryCost <
Entity: User ∆_longitude < 10 m ᴧ Entity: User 0.1 mWh
Scope: Position ∆_latitude < 10 m Scope: Position
Rep: WGS84 Rep: CartesianCoordinates
BatteryCost < Accuracy =
0.5 mWh 1 cell
GPS Sensor Cell-ID based Location Sensor
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 27

35. Introduction Approach Evaluation Conclusions
Overview - Selection
Challenges and requirements: Context model and ontology
Exchange and interpretation Discovery and Matching
of context information, QoC Matching Results
and CoC Selection
function
Selection
Context Requests
Context offers
Loose coupling
Dynamic discovery
Estimation of QoC
Dynamic selection
Context Consumer 0..* Context Provider 0..*
Reasoner 0..*
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 28

36. Introduction Approach Evaluation Conclusions
Input for the selection: matching results
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 29

37. Introduction Approach Evaluation Conclusions
Problems during the selection
General approach: Calculation of an utility for each provider
by an utility function taking into account QoC and CoC and
selection of the provider with highest utility.
However, several additional problems to be handled in the
selection, because . . .
the selection algorithm has to use predefined QoC values for
deactivated context providers.
these predefined properties do not noteworthy reflect the
status of the provider after its activation.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 30

38. Introduction Approach Evaluation Conclusions
Problems during the selection
After activation, QoC values are much worse than predefined
QoC.
Solution:
Update of the predefined QoC values based on historical
values → Good result if QoC properties reflect malfunction of
the provider. Otherwise no improvement.
Ignoring the malfunctioned provider until a significant context
change has happened.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 31

39. Introduction Approach Evaluation Conclusions
Problems during the selection
After activation, QoC values are much worse than predefined
QoC.
Solution:
Update of the predefined QoC values based on historical
values → Good result if QoC properties reflect malfunction of
the provider. Otherwise no improvement.
Ignoring the malfunctioned provider until a significant context
change has happened.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 31

40. Introduction Approach Evaluation Conclusions
Problems during the selection
Additional optional requirement: Cost minimization
Same type of context information requested by different
consumers and with slightly different criteria.
Solution:
1. Check if intersection of matched context offers is nonempty
and if so
2. select context provider with the least cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 32

41. Introduction Approach Evaluation Conclusions
Problems during the selection
Additional optional requirement: Cost minimization
Same type of context information requested by different
consumers and with slightly different criteria.
Solution:
1. Check if intersection of matched context offers is nonempty
and if so
2. select context provider with the least cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 32

42. Introduction Approach Evaluation Conclusions
Overview - Binding
Challenges and requirements: Context model and ontology
Exchange and interpretation Discovery and Matching
of context information, QoC Matching Results
and CoC Selection
function
Selection
Context Requests
Context offers
Loose coupling Selection Result
Dynamic discovery Binding
Estimation of QoC Inter Representation
Operation
Data
Data
Dynamic selection Converted Data
Activation and Context Consumer 0..* Context Provider 0..*
deactivation Reasoner 0..*
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43. Introduction Approach Evaluation Conclusions
Evaluation
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 34

44. Introduction Approach Evaluation Conclusions
Demonstrator Meet-U
Planning Offline Navigation At Event
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 35

45. Introduction Approach Evaluation Conclusions
Demonstrator - Context dependencies
Adaptation decision
based on position, current activity and connectivity status.
Application Business Logic
Navigation mode requires precise position.
Planning mode requires information about current activity,
activity preferences and on current location of friends.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 36

46. Introduction Approach Evaluation Conclusions
Demonstrator - Context services
Build-in context providers:
Cell-id based location sensor (Low cost, low accuracy)
WiFi based location sensor (Medium cost, medium
accuracy)
GPS based location sensor (High cost, high accuracy)
Connectivity status reasoner
Activity reasoner estimating the activity based on position
and calendar data (Low costs, low accuracy)
Activity reasoner estimating the activity based on
microphone, accelerometers. calendar and position. (High
cost, medium accuracy)
External context provider:
Bluetooth-based location service (Medium costs, high acc.)
RFID-based location service (Low costs, high accuracy)
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 37

47. Introduction Approach Evaluation Conclusions
Evaluation criteria
Questions
Does the approach meet the requirements?
Discovery and matching of context providers
Support for heterogeneous context information
Selection of context providers
Performance and scalability test in a simulator
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 38

48. Introduction Approach Evaluation Conclusions
Conclusions and future work
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 39

49. Introduction Approach Evaluation Conclusions
Conclusions
Abstraction of dynamically appearing and disappearing local
and remote context sensors and reasoners as context services.
Middleware for context-aware self-adaptive applications
supporting the selection of different context services based on
QoC and CoC criteria
Semantic interpretation of heterogeneously represented
context information, QoC and CoC
Flexible access of information in the required representation
and automatic conversions
Support for estimation of QoC of deactivated context
providers
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Michael Wagner Distributed Systems Group University of Kassel 40

50. Introduction Approach Evaluation Conclusions
Future work
Very broad research topic with a lot of remaining open issues, e.g.
Privacy and security support (e.g. offering different context
levels based on privacy preferences)
Support for different discovery mechanisms and protocols
MDD support for context providers, consumers and reasoners
...
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 41

51. Introduction Approach Evaluation Conclusions
Thank you!
Thank you for your interest!
Questions?
Michael Wagner
University of Kassel
T. +49-(0)561-804-6281
eMail: wagner@vs.uni-kassel.de
net: http://www.vs.uni-kassel.de/
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 42

52. Literature I
Thomas Buchholz, Axel K¨pper, and Michael Schiffers.
u
Quality of context information: What it is and why we need it.
In In Proceedings of the 10th HP-OVUA Workshop, 2003, Geneva, Switzerland, Juli 2003.
Maria Chantzara, Miltiades Anagnostou, and Efstathios Sykas.
Designing a quality-aware discovery mechanism for acquiring context information.
In AINA ’06: Proceedings of the 20th International Conference on Advanced Information Networking and
Applications, pages 211–216, Washington, DC, USA, 2006. IEEE Computer Society.
Markus C. Huebscher and Julie A. McCann.
Adaptive middleware for context-aware applications in smart-homes.
In MPAC ’04: Proceedings of the 2nd workshop on Middleware for pervasive and ad-hoc computing, pages
111–116, New York, NY, USA, 2004. ACM.
Nearchos Paspallis.
Middleware-based development of context-aware applications with reusable components.
PhD thesis, Department of Computer Science, University of Cyprus, Nicosia, Cyprus, 2009.
Roland Reichle.
Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments.
PhD thesis, Distributed Systems Group, University of Kassel, Kassel, Germany, July 2010.
Context as a Service
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53. Literature II
Roland Reichle, Michael Wagner, Mohammad Khan, Kurt Geihs, Jorge Lorenzo, Massimo Valla, Cristina
Fra, Nearchos Paspallis, and George Papadopoulos.
A comprehensive context modeling framework for pervasive computing systems.
In Distributed Applications and Interoperable Systems, pages 281–295, 2008.
Roland Reichle, Michael Wagner, Mohammad Ullah Khan, Kurt Geihs, Massimo Valla, Cristina Fra,
Nearchos Paspallis, and George A. Papadopoulos.
A context query language for pervasive computing environments.
In CoMoRea, pages 434–440, Hong Kong, Mar 2008. IEEE Computer Society Press.
Gregor Schiele, Marcus Handte, and Christian Becker.
Pervasive computing middleware.
In Hideyuki Nakashima, Hamid Aghajan, and Juan Carlos Augusto, editors, Handbook of Ambient
Intelligence and Smart Environments, pages 201–227. Springer US, 2010.
Claudia Villalonga, Daniel Roggen, Clemens Lombriser, Piero Zappi, and Gerhard Tr¨ster.
o
Bringing quality of context into wearable human activity recognition systems.
In Kurt Rothermel, Dieter Fritsch, Wolfgang Blochinger, and Frank D¨rr, editors, First International
u
Workshop on Quality of Context (QuaCon 2009), volume 5786 of LNCS, pages 164–173, Stuttgart, June
2009. Springer-Verlag.
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