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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
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
Introduction                          Approach                 Evaluation   Conclusions




                                            Introduction




Context as a Service
Michael Wagner Distributed Systems Group University of Kassel                              3
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
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
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
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
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
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
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 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
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
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
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
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
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:                                Context model and ontology

            Exchange and
            interpretation of context
            information, QoC and CoC




Context as a Service
Michael Wagner Distributed Systems Group University of Kassel                                          13
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
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 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
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
                                                                              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


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 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
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
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
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


                                   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
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 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
Michael Wagner Distributed Systems Group University of Kassel                            22
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..*



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
                                                         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
Michael Wagner Distributed Systems Group University of Kassel                                                            25
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
Michael Wagner Distributed Systems Group University of Kassel                                                                  26
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
Michael Wagner Distributed Systems Group University of Kassel                                                                  26
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
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
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
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
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 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
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
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
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
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
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..*



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. (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
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 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
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 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
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
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.

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                PhD thesis, Distributed Systems Group, University of Kassel, Kassel, Germany, July 2010.


Context as a Service
Michael Wagner Distributed Systems Group University of Kassel                                                          43
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.
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                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.


Context as a Service
Michael Wagner Distributed Systems Group University of Kassel                                                          44

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Context as a Service

  • 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 Michael Wagner Distributed Systems Group University of Kassel 13
  • 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 Context as a Service Michael Wagner Distributed Systems Group University of Kassel 14
  • 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 Michael Wagner Distributed Systems Group University of Kassel 15
  • 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 Context as a Service Michael Wagner Distributed Systems Group University of Kassel 16
  • 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 Michael Wagner Distributed Systems Group University of Kassel 21
  • 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 Michael Wagner Distributed Systems Group University of Kassel 22
  • 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..* Context as a Service Michael Wagner Distributed Systems Group University of Kassel 23
  • 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 Michael Wagner Distributed Systems Group University of Kassel 24
  • 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 Michael Wagner Distributed Systems Group University of Kassel 25
  • 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 Michael Wagner Distributed Systems Group University of Kassel 26
  • 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 Michael Wagner Distributed Systems Group University of Kassel 26
  • 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..* Context as a Service Michael Wagner Distributed Systems Group University of Kassel 33
  • 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 Context as a Service 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 Michael Wagner Distributed Systems Group University of Kassel 43
  • 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. Context as a Service Michael Wagner Distributed Systems Group University of Kassel 44