UMR 5205 An Introduction to Ubiquitous and Pervasive Computing Lionel Brunie National Institute of...

UMR 5205

An Introduction to Ubiquitous and Pervasive Computing

Lionel Brunie

National Institute of Applied Sciences (INSA)LIRIS Laboratory/DRIM Team – UMR CNRS 5205

Lyon, France

http://liris.cnrs.fr/lionel.brunie

Master Course, Lyon, January 2015 - Ubiquitous and Pervasive Computing 2

Contents of the Course

Definitions and Main Characteristics

Basic Issues

Overview of some Research Propositions

A Focus on Context Modeling and Context-Awareness

Master Course, Lyon, January 2015 - Ubiquitous and Pervasive Computing

Definitions

MobileIncludes mobile devices

Context-awareConsiders the execution context

UbiquitousAccessible from everywhere

PervasiveAssociates ubiquity, mobility and context-awareness

Ambient (intelligence)Integrated within everyday life

3


From S. Frénot

A Pervasive System is First, a Distributed System!

Data persistence

Data delivery/exchange

Data distribution

Replication and consistence

Interoperability

Concurrent accesses

“Legacy” systems integration

Security and privacy

4


A Pervasive System is also a Mobile System (and a Sensor Network)

5


A Pervasive System is also a « User Centric » System

Invisible

« Smart » (do not interrupt me for nothing !)

User preferences aware

Context aware

Self learning

Pro-active

Secure and privacy keeping

6


A Pervasive System is also a Collaborative System

Mobile/Ambient Social Networks

Ambient social interactions

Heterogeneity

Mobility management

Announces:

ModelingScalability and filteringSemanticsMatching (publish-subscribe ?)

Context

Privacy

7


A Pervasive System is also a Collaborative System Collective Intelligence - Community

Context awareness and context sensibility

Information fusion → alarms

Sharing and confrontation of knowledge

Provisioning/composition/integration of services

Collaborative learning: histories, execution traces

Semantic emergence

Recommendation8


Basic Issues

Discovery

(Auto-)Organization and routing

Security and privacy

Context-awareness and user awareness (and modeling)

Composition, execution and migration of services

Adaptation (data, services, HMI)

Data availability, delivery and access (cache, replication, persistence…)

Mediation (data, services) and information/service sharing

Traceability and history

HMI – augmented reality

Collective “intelligence”, semantic emergence, social networks

(scalability – low energy consumption – “calm”)

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An Overview of Some Research

Propositions

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Pervasive Middleware: the PerSE MW

Adaptation

context

Models

•Context-awareness•Proactivity•Service composition

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Architecture of a PerSE Base

12


PerSE Layers

13


Access Control for Mobile Users: The Chameleon

Chains of trust certificates: if A trusts B and B trusts C then A trusts C… to some level (and while considering subjectivity and access rights mapping)

Multi-layered adaptive certificates (X316 certificate + FeMoS signature)

protection of sensible information dynamic construction of the certificate and its signature

Trust computing and reputation: cf. further course

14


The Chameleon: Architecture

15


Interface Adaptation and Context-aware Browsing

16


Multimedia Content Adaptation

17


A Special Focus onContext Modeling and Context-Awareness

18


Context-awareness: Challenges

Environment sensing sensing storage communication

Context-aware service context reasoning decision making action trigger collaboration

Context data & its semantics

Context models

Ac

tio

n

Context management collection organization representation storage presentation

19


Context representation

What is context? (complementing definitions by Dey and Winogard)

Context: characteristics, relationships, properties Context: interpretation (operational term)

Eg. Device has battery life of 3hrs: important only when we are on a trip

=> Context: How, Where, When, What, Who, and Which

Examples Bob locatedIn ReadingRoomreportedBy Sensor5 (who reported it) Bob locatedIn ReadingRoomhasPrecision 85% (how accurate is it) ReadingRoom locatedIn Library (where is it) locatedIn isTransitive => Bob locatedIn Library

20


Context: Issues and Approaches

Context Representation

Markup scheme based approaches

Ontology based approaches

Context Manipulation Agent and widget based approach

Broker middleware based approach

Service oriented middleware based approach

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The EHRAM Model: an Example

Activity

Context Entity

Device

Lecture Student Professor Phone

Geography Bob Alice SPhone095attends

85%

hasAccuracy

1024

hasProfessor

owns

hasOwner(by inverse axiom)

attends

engagedIn locatedWith(by symmetric axiom)

locatedWithisA isA isA

InstanceOf InstanceOf InstanceOf InstanceOf

isA isA isA isA

Entity(& literals)

Relation(entity & attribute type relationships)

Axiomaticrelation

Metadatarelation

hasMemory

DomainLayer

GenericLayer

Person

Hierarchicalrelation

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Context management

Objective: serialization of the EHRAM model for storage and processing of context data and its semantics

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Can we combine the best of the two worlds?

Necessary Features Database Approach

Ontology Approach

Formality Management of large context data Query optimization and processing

Scalability Semantic support Reasoning support

Context management

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Context data Context semantics

HCOM: Hybrid modeling approach

An hybrid approach Join the results

Process(Ontology)

Process(Database)

To separate

Context data

Context semantics

HCoM: a Hybrid Context Management model

HCoM separates data from the EHRAM model into

Context databaseContext database RulesRules

HCoM filters out context data before integration (pruning)

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Rules/Policy

Rule Repository

Context Selector and Context

Event Handler

Context Manager

Context-Aware Core Service

Context Data

RCDB

Acquisition Layer

Pre-Processing

Layer

Data Modeling

and Storage Layer

Management Modeling

Layer

Utilization LayerCollaboration Manager

Rule-Mining

Generic Ontology

Domain Ontology Builder

DomainOntology

Defined Rules

GenericOntology

Context-Onto

Rule Filter

Context Capturing Tools/Interface

Context Filter

Semantic Mapping of Context, Rules & Ontology

Context Aggregation

HCoM System Architecture

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Context Usage – Context Exchange – Context-Awareness

Objective: domain independent context-aware platform

CoCA: Collaborative Context-Aware service platform

CoCA consists of The context data models - HCoM/EHRAMThe RAID-Action engineThe collaboration manager

Context Management


Motivations

State of the Art

Context-Awareness

Discussion/Conclusion

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The RAID-Action engine in CoCA

Reasoning The process of deriving new knowledge using rules, context data and its

semantics. Reasoning rules may be derived from the context semantics or can be

defined based on domain policies, rules and regulations.

Aggregation and Interpretation Integrating numerous contexts into one to raise its level of abstraction.

Decision Decisions follow from reasoning and they are the major outcomes of the

CoCA service.

Actions Actions are results of decisions and are executed by applications.

Example Given: Students (>1) and a professor in a class room with a video

projector switched on and connected to a PC running a power point application! Conclude: Lecture is going on in the room!

Context Management


Motivations

State of the Art

Context-Awareness


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Collaborations in CoCA – using JXTA protocols

Bob_

Smartphone

CoCA Platform

Inst

alle

d

Peer12

Peer21

CoCA

Platform

Inst

alle

d

Peer22

CoCA Platform

Inst

alle

d

Peer23

CoCA

Platform

Inst

alle

d

Rendezvous

(Router)Peer2

Rendezvous(Router)

Peer1

Discovery?

Discovery?Discovery?

Discovery?

Response+

Context Data Transfer

Peer Group1Peer Group2

CoCA peers Self-organize into

peer groups Discover each other Discover each other’s services and resources

Uses JXTA P2P protocol

Discovery?Discovery?

Context Management


Motivations

State of the Art

Context-Awareness

Bob’s hand held device (Bob_Smartphone) has got a

context message that says Bob’s professor is now in «Coffee Room» . In order to perform

reasoning and suggest some action to Bob, Bob_Smartphone needs some semantics on the concept

“Coffee Room”.

Now, Bob_Smartphone has the necessary information about the

concept «Coffee Room» to perform reasoning and to suggest actions to

its owner BoB


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Layer 1: Capturing Layer Hardware tools

(e.g. Badges, sensors, cameras, etc.)

Software tools(e.g. Our WiFi based location

detector, Device ownership data through user interface ...)

Layer 2: Pre-processing LayerEHRAM Model

Layer 3: Management Modeling Lyer (HCoM Model) Context Manager

Learned Rules

Dispatch Rule

Dynamic Context

Domain Ontology

Generic Ontology

Static Context

RCDB

KDB Context FilterRepository

Layer 4: Reasoning and Decision Layer (CoCA Core)

Communication ManagerReasoning Aggregation Interpretation Decision

Action Parameters

Captured Rules

Context-Onto

Dispatcher Context

Select Relevant

RAID-Action Engine

Layer 5: Application Layer (Action Processing Interface)

Relations

Metadata

AxiomsHierarchies

Entities

Supplementary Services

CoCA Architecture

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Feedback on Context Modeling and Awareness

A good point: it works

It could work

It can work in a closed/well-defined environment interoperability issues

no lingua franca semantic gap

trust and security issues low resources devices scalability large intersection with the agenda of the Internet of Things!

Handling uncertainty

Handling trust and privacy

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Conclusion

Ubiquitous computing => user centricity

=> context-awareness

Ubiquitous computing => user centricity

=> content adaptation (cf. keynote)

Key issues application adaptability/flexibility interoperability scalability standardization identity privacy

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Open Discussion (from keynote)

The User Intention (i.e., the Why question): the Holy Graal of “calm IT”?

Sensing the environment Embedded sensors Internet of Things? Next-Generation Internet? Still widely open issues, esp. wrt inter-operability

Modeling the context The Babel Tower

Context-Awareness, Tracing, Pro-Activity, Calm sine qua non condition to a seamless integration into the user life Quality of Experience? User privacy? User control? Personalization vs privacy Pro-Activity ? Calm: a technological issue or a psycho-social issue?

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UMR 5205 An Introduction to Ubiquitous and Pervasive Computing Lionel Brunie National Institute of...

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