Collaborative Context Recognition for Mobile Devices

1
Collaborative Context Recognition for Mobile
Devices

• Software for Context-Aware
• Multi-User Systems
• Professor Joao Sousa
• David Gonzalez

2
Overview

• Summary of Huuskonen CCR
• Theory Abstract.
• Model Interpretation.
• Implementation Close look.
• Long-term context
• Related works.
• Recommendations.

3
Theory Abstract

• Once upon a time....
• Mobile Devices(MD) were too limited(e.g. Power
  computing, Energy dependent, not common).
• Well, still is like that but they are
  ubiquitous.
• PCs are not wearable, but MDs are.
• MD User Interface are limited, but they are
  Communication Hubs.

4
Theory Abstract

• Human Computer Interaction(HCI) must integrate
  Sensors to engage a real Context experience.
• Sense of
• Location
• Social Situation
• Tasks
• Activities
• Must be easy to the user, but the implementation
  is not trivial.

5
Theory Abstract

• Context Awareness (CA)
• Humans are a Rank-A CA animals, because
• We use CA for primitive functions like Survival,
  Reproduction and Subsistence.
• Imitate and Learn is a common behavior, so We are
  Context-driven individuals.
• The issue is how transfer this to Machines.

6
Simple Model for Human Behavior
Doubt
Imitate
Do
CA
Lost
Ask
7
Mobile Context Awareness

• This is the first step to allow CCR.
• It merges IA and HCI.
• Examples
• Location
• Environmental Sensors
• Biometrics
• Acceleration sensors
• Multimedia

8
Application Area

• Geomarketing
• Jaiku
• Clarity Brickstream
• Nintendo 3DS
• Latitude by Google

9
Long-term goal

• State CCR as part of global Initiative.
• This is not isolated research, but a common
  effect of Computing Paradigm Shift.
• Establish improvements to the current
  architecture.
• Till now the architectures work, but lack of new
  frameworks to ease the inherent flexibility of
  this kind of systems.

10
Model Interpretation

• A CCR Looks like

Process
Method
Context Reasoning
CCR Server
Context Recognition
Weighted Voting Protocol
Context Awareness
Signal Processing
Sensors signals
11
Model Interpretation

• A CCR System Looks like

Process
Actor
Context Reasoning
CCR Server
Context Recognition
Mobile Device Group
Context Awareness
Mobile Device
Sensors signals
12
Implementation Close look
Actor
Apache Tomcat Windows, Linux
CCR Server
Symbian S60, IOS
Mobile Devices
13
Development up to present

• State CCR as part of global Initiative
• 2008, Bannach Context Recognition Network
• 2005, Sung Blum Wearable computing
• 2003, Huuskonen CCR for MD

14
Recommendations

• New SW Platforms are requires, in this particular
  case Android.
• Stronger Architecture are required in the
  Business layer, specifically Web Services.
• Ontologies are proposed, not yet implemented.

15
Architecture ideas
Presentation
Rich User Interfaces, Context Aware like DK
Business
More Flexibility and spreadable with Web Services
Data Access
Data Mining for new Contexts rules