Agent-based Systems for Ubiquitous Computing

Agent-based Systems for

Ubiquitous Computing

José [email protected]

Laboratory for Advanced Collaboration

PUC–Rio, Brazil

Apresentação:

Introduction

Ubiquitous computing

Computer systems will seamlessly integrate into our everyday lives, providing services and information anytime and anywhere

M. Weiser – The Computer for the 21st Century. Scientific America, 265, Sep 1991.

Introduction

Support for mobility

UC infrastructures are more complex and deal with issues such as user mobility, disconnection, dynamic introduction and removal of devices, heterogeneous network connections, as well as the need to integrate the physical environment with the computing infrastructure

Introduction

Context-aware

A fundamental characteristic of a software infrastructure for UC applications is context-awareness, i.e., the capability of providing services based not only on user inputs, but also on implicit contextual information acquired (and deduced) from a wide range of distributed and heterogeneous sensors

Implicit information is used to automatically trigger services

Introduction

Discovery

Some chalenges for Ubicomp researchers:

Adaptation

T. Kindberg and A. Fox - System Software for Ubiquitous Computing. Pervasive Computing, Jan/Mar 2002.

Integration

Security

Robustness

Using agent technology to support UC Is motivated by agent’s intrinsic properties such as autonomy, mobility, proactivity

H. Harroud., M. Khedr and A. Karmouch - University of Ottawa.

Building Policy-Based Context-Aware Applications for Mobile Environments.

Mobility Aware Technologies and Applications, MATA 2004.

The use of multi-agent systems allows the natural partitioning of the whole application in a set of basic and independent tasks

Introduction

Agents in UC

Example: Travel aide

• A university professor flies to a new city and stays the night for two project meetings, one in the morning, one in the afternoon

• With a MAS, the professor’s PDA has a connection with the local weather and traffic network, his agent matches this information with his calendar and wakes him up 15 minutes earlier, preventing him to be delayed by a traffic jam

• In the first meeting, his agent receives the information that the second meeting was postponed, and arranges for another night’s stay

ACAI




CPM

PSA SAT

Network

SA




CPM

PSA SAT

Network

User moves

UA

SA

UA

ACAI Architecture




• CONTEXT POLICY MANAGERResponsible for monitoring the context information and managing the environment resources based on this context

CPM

• POLICY SERVICE AGENTManages policies of the domain under its administration to control the behavior and decision-making of the system agents

PSA

• SITE ASSISTANTIs in charge of preparing and setting up a temporary working environment to a user at a visited site

SAT

ACAI Agents

Example 2: Middleware infrastructure

Devised and developed in the project called Computers in the Human Interaction Loop (CHIL), with a view to easing service development and application integration.

CHIL

CHIL emphasizes on the development of ubiquitous, context-aware services in ‘smart rooms’ equipped with numerous sensors (i.e., microphones and cameras).

J. Soldatos, I. Pandis, K. Stamatis, L. Polymenakos and J. L. Crowley

Agent Based Mid. Infrastructure for Autonomous Context-Aware Ubiquitous Computing.

Journal of Computer Communications, 2006.

CHIL smart rooms

• One 64 channel microphone array

• Microphones for localization, in particular three clusters, each consisting of four microphones

• Four fixed cameras, used for overall monitoring of the room

• One active camera with pan, tilt and zoom (PTZ camera)

• A panoramic (or fish-eye) surveillance camera.

CHIL: Agent platform

• Software agents lack the capabilities required to support high performance transfer of sensor streams

Shortcoming:

• facilitate the implementation of communication between distributed entities based on rich semantics

• ease the implementation of transparent ad hoc communication between distributed components

• agents provide a certain degree of autonomy which constitutes a sound basis for implementing autonomic features

Agent infrastructures:

Infrastructures for distributed transfer of sensor streams are usually built as system level components that do no feature the high level capabilities of software agents

CHIL Low level middleware components

Low level middleware components are wrapped with agent based middleware, so that they behave as software agents

MoCA Architecture

Regulation

Regulation may be useful to control the interaction among heterogeneous devices and users, helping adaptation and providing security.

Ubiquitous systems are typically open systems

DynaCROM

OrganizationsPUC-Rio, LIP6

1st Case Study

EnvironmentsBrazil, FranceClassrooms, Professors’ Rooms, etc

RolesStudents, professors, etc

Marie, a computer science student at LIP6, is travelling to Brazil to spend one year at PUC-Rio as a visiting student.

Inference

Inference

Inference

Proposal

Multi-agent application

MoCA Other services

MoCA/MAX

JADE

infrastructure to regulate agents interaction in a ubiquitous environment

DynaCROM

Contribution

While current ubiquitous support is mainly concerned with topological aspects, we provided a way of considering the social context and its influence in the entities’ interaction process.

Ongoing work

Describe compound situations using Ontologies, and being capable to infer if some situation holds

Inference:

Propose case studies with a broader range of context scopes (device capabilities, sensors, etc...)

Context:

Provide mechanisms to localize and trigger services and applications

Response:

OrganizationPUC-Rio

2nd Case Study

EnvironmentActive Classrooms

RolesProfessor, student

A teacher using a Tablet-PC inside the Active Classroom may send slides to the students using Smartphones and to be presented by the datashow connected to the Server.

PersonP1, P2

Mobile devicesTabletPC-01, Smartphone-01, Server-01

ApplicationsSlideShowApp-1, SlideShowApp-2 , SlideShowApp-2

Base Ontology

hasCondition

Norm

Permission to control data-show server

isImplementedBy

Person isLocatedIn ACL && Role==Teacher

DatashowClient Agent

Norm enforcement

Infered Ontology

[R1: (?Dev1 ontologyURI:isCarriedBy ?Person)

(?Dev1 ontologyURI:isInside ?Env)

(?Person ontologyURI:isInside ?Env)

[R2:(?Person ontologyURI:isInside ?Env)

(?Env rfd:ID “AC-RDC”)

(?Person ontologyURI:playsRole ?Role)

(?Role rfd:ID ?“Teacher”)

(?Norm ontologyURI:appliesTo ?Person)

1) Infering the location of devices

Monitor Agent

knows the general norms in a given Organization

(ontology + rules)

is “aware” of agents entering end leaving the room

(service provided by MoCA)

must be able to assign a proper role to each agent

Is capable of infering the set of applicable norms

(DynaCROM)

Ongoing Implementation

Monitor Agent

When a norm is applicable:

Ongoing Implementation

A mobile agent encapsulating an application is created and sent to the user’s mobile device

FIM

Documents

Agent-based Systems for Ubiquitous Computing