An Intelligent Broker Architecture for Context-Aware Systems

1
An Intelligent Broker Architecture for
Context-Aware Systems

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

January 2003
2
Outline

1. Introduction
2. Research Review
3. Context Broker Architecture
4. Research Plan Summary

3
Part I. Introduction
4
Yesterday Gadgets are Everything
5
Today Communication is Everything
6
Tomorrow Service is Everything
Thank God! Everything is done for me!
7
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

8
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)

9
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

10
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

11
Lets talk about

• Agent
• Broker
• Context
• Context-aware systems

12
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

13
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

14
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
15
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
16
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

17
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

18
Context-Aware Systems (1 of 2)
A Call-forwarding System
19
Context-Aware Systems (1 of 2)

• Two types of contexts are used

Location Context
Activity Context
20
Context-Aware Systems (2 of 2)
Shopping Assistant
21
Context-Aware Systems (2 of 2)

• Three types of contexts are used

Location Context
Identity Context Attribute Context
22
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)

23
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

24
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!)

25
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

26
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!

27
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

28
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.

29
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.

30
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

31
Part II. Research Review
32
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)

33
Look From Two Different Angles
Types of Context-Aware Research
Approaches to Support Context-Aware Systems
34
Types of Context-Aware Research
35
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.

36
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

37
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

38
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)

39
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

40
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

41
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.

42
Different Types of Context-Aware Research
Enhancing User Interface Guiding Behavior Adaptation Building Pervasive Computing Srv.
Microsoft Cassiopeia E-105E X
Video Streaming App. (Odyssey) X
XeroxPARC Active Badge Apps X X
Cooltown Museum X
MIT Intelligent Room X
Context Broker Architecture X
43
Approaches to Supports Context-Aware Systems
44
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
45
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
46
Different Approaches to Support Context-Aware
Systems
Direct Sensors Access Facilitated by Middle-ware
Microsoft Cassiopeia E-105E X
XeroxPARC Active Badge Apps X
Video Streaming App. (Odyssey) X
Context Toolkit X
MIT Intelligent Room X X
Context Broker Architecture
47
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

48
Part III. Context Broker Architecture
49
Research Problems (again)

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

50
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

51
Context Broker Architecture
Semantic Web
Enterprise Servers


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

Contexts in the Environment
52
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
53
A Brokers Job is to

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

54
A Conceptual Design of the Broker
(3)
(1)
Context Acquisition Component
Knowledge Base
(2)
Inference Engine
Broker Behavior
(4)
55
(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

56
(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

57
(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

58
(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

59
(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

60
(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
61
(4) Broker Behavior
Knowledge Sharing Protocols
Informing the broker of some contextual knowledge
Querying the broker for some contextual knowledge
62
(4) Broker Behavior
Knowledge Sharing Protocols
Subscribe to the broker to be notified when
context changes
63
Part IV. Research Plan and Summary
64
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

65
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

66
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

67
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

68
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

69
Scenario Intelligent Meeting Room
1 of 2
70
Scenario Intelligent Meeting Room
2 of 2
71
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

72
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

73
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

74
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

75
Research Plan (for the next 12-18 months)
Stage 1 Est. 1-2 mo. Develop design spec. for the Intelligent Meeting Room System COBRA
Stage 2 Est. 3-5 mo. Prototype Domain Context Broker
Stage 3 Est. 3-5 mo. Prototype a demo of the Intelligent Room System (midterm evaluation)
Stage 4 Est. 2-3 mo. Evaluate the feasibility of COBRA by conducting experiments
Stage 5 Est. 2-3 mo. Complete PhD. dissertation writing
76
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

77
Question?

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