An Intelligent Broker Architecture for ContextAware Systems

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An Intelligent Broker Architecture for
Context-Aware Systems

• Harry Chen
• Dept. of CSEE, UMBC
• PhD Dissertation Proposal Defense

January 2003
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Outline

• Introduction
• Research Review
• Context Broker Architecture
• Research Plan Summary

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Part I. Introduction
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Yesterday Gadgets are Everything
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Today Communication is Everything
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Tomorrow Service is Everything
Thank God! Everything is done for me!
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One Step Towards Tomorrow

• Context-aware Computing
• Brings us one step closer to the Pervasive
  Computing vision
• Enables computer systems to anticipate users
  needs and to act in advance
• An emerging paradigm to free everyday users from
  manually configuring and instructing computer
  systems

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Life is Not Perfect

• Building context-aware systems for Pervasive
  Computing is often difficult and costly
• User privacy issues when sharing personal
  information
• Supporting resource-poor mobile devices
• How to reason about sophisticated contexts in a
  dynamic environment
• Inconsistent and ambiguous contextual knowledge
• Security, trust, ... (goes on and on)

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My Research Objective

• To develop and prototype a broker-centric agent
  architecture to support context-aware systems
• To demonstrate this architecture can be used to
  reduce the difficulty and cost of building
  context-aware systems in an Intelligent Meeting
  Room environment

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Context Broker Architecture

• The broker of this architecture will
• Sense and reason about contexts on the behalf of
  capability-limited agents
• Enable agents to share contextual knowledge
• Protect the privacy of users
• Maintain consistent contextual knowledge

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Lets talk about

• Agent
• Broker
• Context
• Context-aware systems

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About Agent

• Study context-aware systems using intelligent
  agents (context-aware agents)
• Autonomous and Proactive
• Can communicate, not just connect
• Have beliefs about the World
• Have desires and intentions

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About Broker

• Broker, an overloaded term for agents with a
  specialized role
• Mediator mediate communication messages in a
  Multi-agent system (MAS)
• Facilitator facilitate task execution between
  agents to achieve cooperation
• Match-maker match/recommend service
  advertisements

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Broker in My Architecture

• Broker is a server agent that controls and
  manages the contextual knowledge of a Pervasive
  Computing environment
• Enables agents to contribute to and access a
  shared model of context
• Allows users to control the access of their
  personal information in a context-aware
  environment

B
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About Context

• In Merriam-Webster Dictionary
• 1 the parts of a discourse that surround a word
  or passage and can throw light on its meaning
• 2 the interrelated condition in which something
  exists or occurs

We are interested in 2
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Definitions of Context

• In context-aware computing
• No unified definition of context
• Most of the definitions agree that context has
  something to do with the interactions between the
  users and the computing systems

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My Definition of Context

• Context is information that can be used to
  characterize the situation of a person or an
  object in a Pervasive Computing environment.
• The identities and attributes of people and
  devices
• The locations of people and devices
• The activities that people are participating in
• The roles and intentions of people when
  participating in the activities

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Context-Aware Systems (1 of 2)
A Call-forwarding System
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Context-Aware Systems (1 of 2)

• Two types of contexts are used

Location Context
Activity Context
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Context-Aware Systems (2 of 2)
Shopping Assistant
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Context-Aware Systems (2 of 2)

• Three types of contexts are used

Location Context
Identity Context Attribute Context
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System Characteristics

• Context-aware agents often run on mobile devices
• For agents to become context-aware, context
  sensing and context reasoning mechanisms are
  required
• Context-aware systems often exploit user
  information (e.g. personal profile, user location
  social activity)

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Research Problem

• Building context-aware systems can be difficult
  and costly because
• (1) limited resources in mobile devices
• (2) lack of reusable context-aware mechanisms
• (3) privacy issues in accessing user information

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Limited Resources in Mobile Devices

• Battery Power Constraint
• Small devices gt limited built-in sensors
• Big devices gt too many external sensors can be
  awkward
• Information Storage Constraint
• Historic knowledge saves computation
• Limited storage gt must intelligently choose what
  to save and delete (hard problem!)

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Limited Resources in Mobile Devices

• Computing Power Constraint
• To process contexts needs CPU power
• Limited CPU power gt primitive contexts ONLY
  (limited intelligence)!
• Communication Constraint
• Contexts are hidden heterogeneous sources
• Not knowing where are the sources and how to
  communicate gt limited context-awareness

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Lack of Reusable Context-Aware Mechanisms

• 2 Essential Mechanisms
• Context Sensing acquiring information from the
  physical environment
• Context Reasoning interpreting the information
  that have been acquired
• In the existing systems, both are built from the
  scratch every time gt no reuse!

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Privacy Issues in Accessing User Information

• Privacy is about control of information
• In context-aware systems, users may not have full
  control of their information
• Sensors are hidden in the environment
• Information are collected without explicit
  consent from the users

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Privacy Issues in Accessing User Information

• What about sharing of information?
• You tell an agent something about you because you
  want its service, and later it tells someone
  else... (you are in trouble!)
• The downstream consequences of information are
  unknown or unspecified.

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Proposed Solution

• Context Broker Architecture
• Philosophy agents cant do everything by
  themselves, lets provide a powerful server
  entity to help them
• Rationale the Moores Law for mobile computing
  is likely to hold developing a centralized
  solution is much easier than any P2P solutions.

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Context Broker Architecture

• The key features
• Sense and reason about contexts on the behalf of
  capability-limited agents
• Enable agents to share contextual knowledge
• Protect the privacy of users
• Maintain consistent contextual knowledge

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Part II. Research Review
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The Purpose of Research Review

• (1) To argue building context-aware systems can
  be difficult and costly using the existing
  architecture
• (2) To put Context Broker Architecture research
  into perspective (so that we can compare and
  contrast it with the existing systems)

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Look From Two Different Angles
Types of Context-Aware Research
Approaches to Support Context-Aware Systems
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Types of Context-Aware Research
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Enhancing User Interfaces (1 of 2)

• Problem the user interface of the existing
  mobile devices demand too much user attentions
  (i.e. cognitive and visual).
• Solution to replace the traditional user
  interface by enabling devices to become
  context-aware.

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Enhancing User Interfaces (2 of 2)

• Microsoft research has developed a Cassiopeia
  E-105 that can
• Active voice recording application when detects
  the user is holding the device like a cell phone
  or microphone
• Automatically reformat the screen display
  (landscape ?portrait) depending how the user
  holds the device

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Guiding the Adaptation of System Behavior (1 of 2)

• Problem Environment changes can affect the
  performance of applications (e.g. using wireless
  PDA while walking on the street)
• Solution enabling applications to adapt their
  behavior in according to condition changes

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Guiding the Adaptation of System Behavior (2 of 2)

• A video streaming application can dynamically
  adjust the streaming quality of video without
  interrupting the viewers attention (Odyssey)
• Widely used contexts network bandwidth, error
  rate, connection setup time, and usage costs
• XeroxPARC Active Badge and PARC Tabs Applications
  (the 1st context-aware system)

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Building Pervasive Computing Services (1 of 3)

• Problem Complex computer systems are drawing
  humans into the world of computing
• Think Oxygen Mark Weisers vision
• Solution enabling computers to reason and act in
  according to the situation of users as they carry
  out their every activities

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Building Pervasive Computing Services (2 of 3)

• In MITs Intelligent Room, the open/close of
  window curtains are automated by detecting the
  body position of a user in a couch

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Building Pervasive Computing Services (3 of 3)

• In HPs Cooltown museum, the visits of Cooltown
  users are automatically documented based on what
  they have seen.

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Different Types of Context-Aware Research
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Approaches to Supports Context-Aware Systems
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Acquiring Context Directly from Sensors
Host Device
Host Device
Agent
Agent
Context Reasoning
Context Reasoning
Sensor
Sensor
Sensor
Sensor
Sensor
Sensor
Contexts in the Environment
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Facilitated by a Middle-ware Infrastructure
Host Device
Host Device
Middle-ware
Middle-ware
Context Reasoning
Context Reasoning
Sensor
Sensor
Sensor
Sensor
Sensor
Sensor
Contexts in the Environment
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Different Approaches to Support Context-Aware
Systems
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The Result of Research Review

• Conclusion building context-aware systems can be
  difficult and costly using the existing
  architecture
• No or very minimal information sharing
• No or limited reuse of context reasoning
• No explicit support for privacy protection
• Context Broker Architecture belongs to
• Building Pervasive Computing Services
• Neither direct sensors access or facilitated
  by middle-ware

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Part III. Context Broker Architecture
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Research Problems (again)

• (1) Limited resources in mobile devices
• (2) Lack of reusable context-aware mechanisms
• (3) Privacy issues in accessing user information

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Whats the Implication?

• A single agent has limited capability to acquire
  contexts

• Building context sensing and reasoning mechanisms
  from the scratch can be expensive

• Protecting user privacy in a context-aware
  environment is critical

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Context Broker Architecture
Semantic Web
Enterprise Servers


Host Device
Host Device
Domain Context Broker
Context Reasoning
Middle-ware

Contexts in the Environment
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Domain Context Broker

• The architecture structures contexts in a
  collection of micro-worlds, called domains.
• E.g. a meeting room domain, a school domain, a
  home domain etc.
• In each domain, there is a
• Domain Context Broker (broker)

B
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A Brokers Job is to

• Acquire contexts from heterogeneous sources on
  the behalf of context-aware agents
• Enable agents to contribute to and access a
  shared model of context
• Allow users to use policy to control the access
  of their personal information
• Detect and resolve inconsistent contextual
  knowledge

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A Conceptual Design of the Broker
(3)
(1)
Context Acquisition Component
Knowledge Base
(2)
Inference Engine
Broker Behavior
(4)
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(1) Knowledge Base
(1)
(2)
(3)
(4)

• Ontology domain ontology, domain heuristics and
  privacy ontology
• Context model information that can be used to
  characterize the situation of a person or an
  object in the domain
• i.e. identity, attribute, location, activity,
  role and intention context

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(2) Inference Engine

• Ontology Reasoning deduce facts that can be
  inferred from ontology knowledge (not context)
• Context Reasoning deduce facts that are parts of
  the context model (context)
• Knowledge Maintenance detect and resolve
  knowledge inconsistency in the context model

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(2) Inference Engine

• Hybrid Reasoning Mechanism
• The existing systems use ad-hoc procedures with
  deductive reasoning at core to reasoning about
  context
• I will attempt to develop a hybrid composition of
  logic reasoning (e.g. deduction and abduction),
  fuzz logic and statistical analysis (e.g.
  decision tree, Bayesian networks etc.) to reason
  about context

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(3) Context AcquisitionComponent
(1)
(3)
(2)
(4)

• Context Sensors physical sensors and virtual
  sensors
• Context Interpreter procedures/rules that
  translates sensed data into knowledge that be
  processed by the Inference Engine

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(4) Broker Behavior

• A collection of protocols that the broker follows
  when interact with context-aware agents
• Privacy Policy Negotiation the broker forms
  agreement with the users before disseminating
  their personal info.
• Knowledge Sharing the broker enables agents to
  acquire contexts that are otherwise not accessible

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(4) Broker Behavior
Privacy Negotiation Protocol
Agent
Broker
User
Request for user contexts
Propose privacy policy
Propose privacy policy
Reject proposal
Accept proposal
Inform user contexts
Propose modification
Propose privacy policy
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(4) Broker Behavior
Knowledge Sharing Protocols
Informing the broker of some contextual knowledge
Querying the broker for some contextual knowledge
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(4) Broker Behavior
Knowledge Sharing Protocols
Subscribe to the broker to be notified when
context changes
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Part IV. Research Plan and Summary
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Expected Research Contribution

• To show the difficulty and cost of building
  context-aware systems can be greatly reduced by
  using the Context Broker Architecture
• Prototype this architecture (COBRA)
• Evaluate the feasibility of COBRA through a
  series of experiments in an Intelligent Meeting
  Room environment

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How Will I Know When I am Done?

• (1) If I can show a working prototype of COBRA
  that exhibits certain system characteristics
• (2) If COBRA can help to build an extensible
  Intelligent Meeting Room system
• (3) If I can qualitatively compare COBRA with
  the existing architecture

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A working prototype means

• COBRA can support
• Context acquisitions (sensing and reasoning) from
  heterogeneous sources
• Knowledge sharing between distributed agents
• User privacy protection using policy rules
• Knowledge maintenance to resolve inconsistent and
  ambiguous contexts

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Help to build an extensible Intelligent Meeting
Room means

• The end system is not hard-wired and ad-hoc
• Is built using well-defined protocols,
  ontologies, APIs etc.
• Within a reasonable amount of effort, other
  developers can use COBRA to enhance or extend the
  system functionalities

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Comparing COBRA with the existing architecture
means

• I can show why COBRA is better/worse than other
  system architectures
• For example, MITs Intelligent Room, GTIs
  Context Toolkit etc.
• But this may not substantial because
• No comprehensive Pervasive Computing architecture
  has yet been developed
• What I am proposing to work on is still relative
  new research

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Scenario Intelligent Meeting Room
1 of 2
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Scenario Intelligent Meeting Room
2 of 2
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Critical Technologies

• Semantic Web Languages Tools
• Web Ontology Language (OWL), RDF/RDFS and XML
• JESS (Java Expert System Shell)
• Rule-based reasoning engine in Java
• FIPA Standards Framework
• Java agent libraries JADE LEAP

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Semantic Web in COBRA

• Benefit a new source of context!
• Vast information web services, personal
  websites, public announcements, news etc.
• Useful gt determining context, resolving
  inconsistent knowledge, predicting the future
  context etc.
• COBRA implementation ontologies privacy
  policies

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JESS in COBRA

• Benefit practical, efficient Java-compatible.
• Supports both forward backward-chaining
• Good experience in building HPs CoolAgent RS
  (context-aware system)
• COBRA implementation inference engine, heuristic
  rules knowledge maintenance

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FIPA Standards in COBRA

• Benefit standards for programming distributed
  agents readily available Java framework
• FIPA agent management, communication, life-cycle
  and more.
• JADE/LEAP VERY Good experience run agents on
  GSM phones and Pocket PC
• COBRA implementation broker behavior,
  Intelligent Meeting Room

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Research Plan (for the next 12-18 months)
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Summary

• The initial design of Context Broker Architecture
  shows great promise in reducing the difficult and
  cost of building context-aware systems.
• COBRA will enable resource-limited agents to
  contribute to and access a shared context model
• COBRA will allow users to control the access of
  their personal information in a context-aware
  environment

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Question?

• Related material
• http//users.ebiquity.org/hchen4/phd/