context as a service
DESCRIPTION
Presentation on current status of my PhD thesis (topic Context as a Service) 12/02/2010TRANSCRIPT
Introduction Approach Evaluation Conclusions
Context as a Service
Michael WagnerDistributed Systems Group
University of Kassel
December 2010
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 1
Introduction Approach Evaluation Conclusions
Outline
IntroductionMotivationQuality of context and cost of contextChallenges and objectives
ApproachContext model and ontologyContext Offering and Query LanguageDiscovery and matchingSelectionBinding
Evaluation
Conclusions
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 2
Introduction Approach Evaluation Conclusions
Introduction
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 3
Introduction Approach Evaluation Conclusions
Context provider
Light sensor
Proximity sensor
Accelerometer
Thermometer
GPS Sensor
Digital compass
Co
nte
xt s
en
sors
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4
Introduction Approach Evaluation Conclusions
Context provider
Light sensor
Network based
Temperature
Activity
Reasoner
Calendar based
Position
Network based
PositionProximity sensor
Accelerometer
Thermometer
GPS Sensor Cell-ID based
Position
Digital compass
Co
nte
xt s
en
sors
Co
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xt reaso
ne
r
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4
Introduction Approach Evaluation Conclusions
Context provider
Light sensor
Network based
Temperature
Activity
Reasoner
Calendar based
Position
Network based
PositionProximity sensor
Accelerometer
Thermometer
GPS Sensor Cell-ID based
Position
Digital compass
Co
nte
xt s
en
sors
Co
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xt reaso
ne
r
Similar type of context
information
Position
Temperature
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 4
Introduction Approach Evaluation Conclusions
Additional external context provider
WiFi
Positioning
GPS
WiFi
Positioning
GPS
WiFi
Positioning
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 5
Introduction Approach Evaluation Conclusions
Additional external context provider
WiFi
Positioning
GPS
WiFi
Positioning
GPS
WiFi
Positioning
RFID
Positioning
WiFi
Positioning
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 5
Introduction Approach Evaluation Conclusions
Using context in context-aware self-adaptiveapplications
Several types of context consumers:
� Application business logic: Context-information used withinthe actual application (e.g. navigation from the currentposition to another position)
� Adaptation reasoning: Selection of the “best” variant of theapplication 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
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
Introduction Approach Evaluation Conclusions
Current solutions
� Most commonly: hard-linked references to context sensors andreasoners, but
� no support for dynamically appearing new context providers.
� Few approaches support the dynamic selection and discoveryof context sensors [CAS06, HM04], but
� developers have to know the data representations of thecontext provider,
� no support for activation and deactivation (and the resultingproblems) of context providers in order to save resources.
However, dynamic discovery, data interpretation and energy-savingare essential requirements in pervasive computing [SHB10].
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 8
Introduction Approach Evaluation Conclusions
Quality of Context
“Quality of Context (QoC) is any information thatdescribes the quality of information that is used ascontext information. Thus, QoC refers to informationand not to the process nor the hardware component thatpossibly provide the information.”
[BKS03]
Cost of Context
“Cost of Context (CoC) is a parameter associated to thecontext that indicates the resource consumption used tomeasure or calculate the piece of context information.”
[VRL+09]Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 9
Introduction Approach Evaluation Conclusions
Context providers differ in the provided QoC, required CoC and theprovided representation of the context information, QoC and CoC.
Problem
Selection and activation of one of the available context providersand thereby . . .
� estimating the QoC of deactivated context providers.
� taking into account the heterogeneous representations ofcontext information and the according QoC and CoC.
� trading off the provided QoC and required CoC against theQoC as requested by the consumer and his preferencesregarding cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 10
Introduction Approach Evaluation Conclusions
Context providers differ in the provided QoC, required CoC and theprovided representation of the context information, QoC and CoC.
Problem
Selection and activation of one of the available context providersand thereby . . .
� estimating the QoC of deactivated context providers.
� taking into account the heterogeneous representations ofcontext information and the according QoC and CoC.
� trading off the provided QoC and required CoC against theQoC as requested by the consumer and his preferencesregarding cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 10
Introduction Approach Evaluation Conclusions
Challenges, requirements and objectives
Local and remote context sensors and reasoners are abstracted ascontext 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
Introduction Approach Evaluation Conclusions
Approach
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 12
Introduction Approach Evaluation Conclusions
Overview - Context model and ontology
Challenges and requirements:
� Exchange andinterpretation of contextinformation, QoC and CoC
Context model and ontology
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 13
Introduction Approach Evaluation Conclusions
Meta-model and Ontologyowl:Thing
ScopeEntityType Representation
is-a
hasRepresentation*characterizes*
Composite Representation Basic Representation
is-a
hasDimension*
� Entity: Physical or logical entity of the world that is describedby 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 internallystructured, e.g. GPS data
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 14
Introduction Approach Evaluation Conclusions
Meta-model and Ontology
� Ontology is used to provide a common vocabulary to bridgesemantic differences
� Defines semantic concepts for entity (types), scopes andrepresentations
� 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
Michael Wagner Distributed Systems Group University of Kassel 15
Introduction Approach Evaluation Conclusions
Meta-model and Ontology� Ontology defines entity types, scopes, representations and
their relationships� Arbitrary number of representations for scopes
RepresentationScope
DateTimeInfo
is-a is-a
hasRepresentation*
LocationInfo LocationRepDateTimeRep
LocationWGS84
LocationAddress
DateTimeDefaultRep
DateTimeCustomRep
LocationInfo_Indv1
LocationInfo_Indv2
DateTimeInfo_Indv1
DateTimeInfo_Indv2
Day = 14
Month = Januar
Year = 1981
Date = 14011981
Street = Königstor
Number = 12
City = Kassel
Latitute = 52.686
Longitude = -2.193
is-a is-a
is-a
is-a
is-a
is-aio
io
io
io
io
io
io
io
hasRepresentation
hasRepresentation
hasRepresentation
hasRepresentation
hasRepresentation*
hasRepresentation*
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 16
Introduction Approach Evaluation Conclusions
Meta-model and Ontology
� Internal structuring of context information is defined asrepresentations in the ontology
� Inter-Representation-Operations (IROs) allow conversionbetween 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
Introduction Approach Evaluation Conclusions
Overview - Context provider and consumer
Challenges and requirements:
� Exchange and interpretationof context information, QoCand CoC
� Loose coupling
Context Provider 0..*Context Consumer 0..*
Reasoner 0..*
Context model and ontology
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 18
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 MUSICContext Query Language (CQL) [RWK+08b] and theInformation 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 differentmetadata representations and for context selection
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 19
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
Introduction Approach Evaluation Conclusions
Context Offering and Query Language - Overview
Context Offer/Request
Sub-Offer/Sub-Request
Scope Representation
SourceFrequency
Subscription
SourceType
Constraints
*
*
ScopeProperty or ScopeID Operator Value Delta
Metadata Constraint
Metadata class Operator Value Delta
Scope Constraint
Characterized Entity *
*
Entity Recursive Negotiable
Entity Constraint
*
*
*
Selection Function
Utility
Significant change spec.
Representation
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 21
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 query ID=” query1 ”3 scope=” 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=” User | Aragorn ”/>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
Michael Wagner Distributed Systems Group University of Kassel 22
Introduction Approach Evaluation Conclusions
Overview - Discovery and matching
Challenges and requirements:
� Exchange and interpretationof context information, QoCand CoC
� Loose coupling
� Dynamic discovery
� Estimation of QoC
Context Provider 0..*Context Consumer 0..*
Discovery and Matching
Reasoner 0..*
Co
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Re
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ffers
Context model and ontology
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 23
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
Michael Wagner Distributed Systems Group University of Kassel 24
Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 25
Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
1. Scope and scopeconstraints
2. Representation
3. Entity and entityconstraints
4. Metadata constraints
Conditions: Scopeq = Scopeo or Scopeq is a generalization of Scopeoor Scopeq = nested scope of Scopeo and scopeConstraint holds!
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26
Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
1. Scope and scopeconstraints
2. Representation
3. Entity and entityconstraints
4. Metadata constraints
Conditions: Repq = Repo or Repq is a generalization of Repo orRepo can be transformed to Repq by an IRO
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26
Introduction Approach Evaluation Conclusions
Example for matching process
CONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
1. Scope and scopeconstraints
2. Representation
3. Entity and entityconstraints
4. Metadata constraints
Conditions: (Entityq = Entityo or Entityq is a generalization ofEntityo) and entityConstraint holds!
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26
Introduction Approach Evaluation Conclusions
Example for matching processCONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
1. Scope and scopeconstraints
2. Representation
3. Entity and entityconstraints
4. Metadata constraints
1. Metadataq = Metadatao or Metadataq is a generalization of Metadatao2. Repq = Repo or Repq is a generalization of Repo or Repo can be
transformed to Repo by a IRO3. Constraintq ∧ Constrainto satisfiable!
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 26
Introduction Approach Evaluation Conclusions
Example for matching process - Result
CONTEXT QUERY 1
Entity: User | Paul
Scope: Position
Rep: CartesianCoordinates
Memory < 0.5 MB
BatteryCost < 0.1 mWh
No Matching: BatteryCost
CONTEXT OFFER 2
Entity: User
Scope: Position
Rep: CartesianCoordinatesAccuracy =
1 cell
BatteryCost < 0.1 mWh
Cell-ID based Location Sensor
CONTEXT OFFER 1
Entity: User
Scope: Position
Rep: WGS84
Accuracy < 1 km
BatteryCost < 0.5 mWh
GPS Sensor
Accuracy: ∆_longitude < 10 m ᴧ
∆_latitude < 10 m
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 27
Introduction Approach Evaluation Conclusions
Overview - Selection
Challenges and requirements:
� Exchange and interpretationof context information, QoCand CoC
� Loose coupling
� Dynamic discovery
� Estimation of QoC
� Dynamic selection
Context Provider 0..*Context Consumer 0..*
Discovery and Matching
Reasoner 0..*
Co
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Re
qu
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ts Co
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ffers
Selection
Matching Results
Context model and ontology
Selection
function
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 28
Introduction Approach Evaluation Conclusions
Input for the selection: matching results
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 29
Introduction Approach Evaluation Conclusions
Problems during the selection
� General approach: Calculation of an utility for each providerby an utility function taking into account QoC and CoC andselection of the provider with highest utility.
� However, several additional problems to be handled in theselection, because . . .
� the selection algorithm has to use predefined QoC values fordeactivated context providers.
� these predefined properties do not noteworthy reflect thestatus of the provider after its activation.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 30
Introduction Approach Evaluation Conclusions
Problems during the selection
After activation, QoC values are much worse than predefinedQoC.
Solution:
� Update of the predefined QoC values based on historicalvalues → Good result if QoC properties reflect malfunction ofthe provider. Otherwise no improvement.
� Ignoring the malfunctioned provider until a significant contextchange has happened.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 31
Introduction Approach Evaluation Conclusions
Problems during the selection
After activation, QoC values are much worse than predefinedQoC.
Solution:
� Update of the predefined QoC values based on historicalvalues → Good result if QoC properties reflect malfunction ofthe provider. Otherwise no improvement.
� Ignoring the malfunctioned provider until a significant contextchange has happened.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 31
Introduction Approach Evaluation Conclusions
Problems during the selection
Additional optional requirement: Cost minimization
Same type of context information requested by differentconsumers and with slightly different criteria.
Solution:
1. Check if intersection of matched context offers is nonemptyand if so
2. select context provider with the least cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 32
Introduction Approach Evaluation Conclusions
Problems during the selection
Additional optional requirement: Cost minimization
Same type of context information requested by differentconsumers and with slightly different criteria.
Solution:
1. Check if intersection of matched context offers is nonemptyand if so
2. select context provider with the least cost.
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 32
Introduction Approach Evaluation Conclusions
Overview - Binding
Challenges and requirements:
� Exchange and interpretationof context information, QoCand CoC
� Loose coupling
� Dynamic discovery
� Estimation of QoC
� Dynamic selection
� Activation anddeactivation
Context Provider 0..*Context Consumer 0..*
Discovery and Matching
Reasoner 0..*
Co
nte
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Re
qu
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ts Co
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ffers
Selection
Binding
Selection Result
Converted Data
Matching Results
Context model and ontology
Selection
function
Inter Representation
OperationData
Data
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 33
Introduction Approach Evaluation Conclusions
Evaluation
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 34
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
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
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. (Highcost, 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
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
Introduction Approach Evaluation Conclusions
Conclusions and future work
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 39
Introduction Approach Evaluation Conclusions
Conclusions
� Abstraction of dynamically appearing and disappearing localand remote context sensors and reasoners as context services.
� Middleware for context-aware self-adaptive applicationssupporting the selection of different context services based onQoC and CoC criteria
� Semantic interpretation of heterogeneously representedcontext information, QoC and CoC
� Flexible access of information in the required representationand automatic conversions
� Support for estimation of QoC of deactivated contextproviders
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 40
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 contextlevels 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
Introduction Approach Evaluation Conclusions
Thank you!
Thank you for your interest!
Questions?
Michael WagnerUniversity of KasselT. +49-(0)561-804-6281
eMail: [email protected]: http://www.vs.uni-kassel.de/
Context as a Service
Michael Wagner Distributed Systems Group University of Kassel 42
Literature I
Thomas Buchholz, Axel Kupper, and Michael Schiffers.
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 andApplications, 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, pages111–116, 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.
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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 AmbientIntelligence and Smart Environments, pages 201–227. Springer US, 2010.
Claudia Villalonga, Daniel Roggen, Clemens Lombriser, Piero Zappi, and Gerhard Troster.
Bringing quality of context into wearable human activity recognition systems.In Kurt Rothermel, Dieter Fritsch, Wolfgang Blochinger, and Frank Durr, editors, First InternationalWorkshop on Quality of Context (QuaCon 2009), volume 5786 of LNCS, pages 164–173, Stuttgart, June2009. Springer-Verlag.
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