Study the capacity to perform classification of sensory data from mobile devices, providing information based on the user behavior and the state of the device.
3. Personalized Sensing System
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Introduction
Theme, Importance and Impact
Study the capacity to perform classification of sensory data from mobile devices, providing
information based on the user behavior and the state of the device.
Impact in mechanisms of transfer
and data sharing;
Concept of searching for data to
meet user;
Learning user behavior based in
mobile sensory data;
Pervasive and Ubiquitous
Computing.
Large-scale, long-lived, mostly
mobile
Not so energy-constrained
Mobility is a driving factor
Security, trust and privacy are
important factors
Connection with emerging
applications;
Sharing data based in users
interests;
Information be available in the
network automatically without user
interaction;
Sensing people behavior;
Sensing proximity interactions;
4. Develop an middleware
capable to analyze sensory
data from mobile devices,
implementing techniques for
data classification (Sensing
Application) whose learning
will enable the transmission of
inferred information for network
sharing (Networking
Application) based on user’s
behaviors and device state
(Data Tagging)
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Objective
Macaba Pedro - MEISI ECATI - ULHT 2014
5. Middleware activation
Device configuration
Reading sensory data and
classification process
Learning process on the
inferred data
Device state sent to the
network
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PersonalSense Framework
Macaba Pedro - MEISI ECATI - ULHT 2014
7. Sensing Module
Maestro provide local sensors data
PersonalSense verifies the data file
Implement the classification process
Analyze sensors
Choose the best attribute
Build the Decision Tree
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8. Inference Module
Procedural Rules
Forward chaining
An action is executed when
conditions are satisfied.
Assign values to attributes
Evaluate conditions
Check if all conditions are
satisfied
Supervised Learning
Predictive Modules
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Method – PersonalSense
Attributes assign
values - defined
Conditions evaluated
Rules are checked
Actions executed
Method – PersonalSense
A Counter to each rule to detect the
action
Use the action to keep track how many
condition in the rule are currently
satisfied
The rule is ready to fire if all conditions
have become true
The attribute is flagged as defined and
undefined
Gain information model
Value 3
Without Value 0
Value 2
Without Value 0
Value 1
Without Value 0
Result?
Dependent Variable: value
FALSETRUE
9. Networking Interface
Data-Centric Characteristics
Seeks to adapt the network architecture to the
current network usage patterns
has a founding principle that a communication
network should allow a user to focus on the data
rather than having to reference a specific, physical
location where that data is to be retrieved from.
Security into the network at the data level
The name of content sufficiently describes the
information
PersonalSense in Data Centric Networking
Focus on data treatment
Receive data and sent data to an application
User behavior to receive some kind of data
Use a data storage cache at each level of the
network
Decrease the transmission traffic
Increase the speed of response
Allows a simpler configuration of network devices
9Macaba Pedro - MEISI ECATI - ULHT 2014
(Smart Pin, 2009)
11. Final Considerations
Service integration and
communication
Automatic provision of
information in the network
relying in opportunistics
meetings between devices
Classification of data
collected from the sensory
capabilities of devices, and
knowledge acquisition and
generation of behavioral
profiles
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(Smart Pin, 2009)
Test more mobile sensors
Test the interaction with
Maestroo and ICON in a
mobile device
Test with other classifier
algorithms
Elaboration of an
algorithm for classification
of collected sensor data
from mobile devices