Optimising Dynamic Composition of Bayesian
Networks for Context Sensing and Inference
Korbinian Frank
*
°
and Matthias Röckl
*
*Institute of Communications and Navigation
German Aerospace Center (DLR)
Oberpfaffenhofen, Germany
korbinian.frank | matthias.roeckl@dlr.de
Tom Pfeifer
°
°
Telecommunications Software & Systems Group (TSSG)
Waterford Institute of Technology
Waterford, Ireland
t.pfeifer@computer.org
High level
Activity
Interaction
Situation
Location
Symbolic Location
Indoor Position
Outdoor Position
Weather
Temperature
Used Services
Service Context
Thermometer
Rain Sensor
Motion Activity
IMU
Availability
Agenda
Task list
Calendar
Requests
Time
Clock
Day of
the week
Season
Number
of Persons
In Vicinity
Noise Level
Ambient Microphone
Activity
of Person
Nearby
Breaking Bayesian Networks (BNs) for Context Inference from Sensor Networks into Bayeslets is a proven
approach for optimising performance in resource constraint ubiquitous computing environments. Automatic
selection and composition of Bayeslets faces the challenge that the related costs (inference time, memory
consumption) grow exponentially with the number of components. To minimise the BN to be evaluated
dynamically, this research investigates optimisation approaches which evaluate the added value of using a
particular Bayeslet versus its cost.
User A
High level
Activity
Availability
Agenda
Interaction
Situation
Time
Motion
Behaviour
Environment
Location
User B
High level
Activity
Availability
Agenda
Interaction
Situation
Time
Motion
Behaviour
Environment
Location
User C
High level
Activity
Availability
Agenda
Interaction
Situation
Time
Motion
Behaviour
Environment
Location
A Bayesian Context Inference Network (left) that is broken into Bayeslets (right)
Inference
should
only
consider
those
Bayeslets
that
significantly
add
value
.
As Bayeslets are heterogeneous, for every linkable candidate the following has to be determined:
How much new information can the linkable Bayeslet provide?
How costly is the integration?
Dynamic Composition of Bayeslets provides solutions for both problems by one of the following criteria:
Net Normalized Mutual Information:
Net Value of Information:
NetVoI
takes
into
account
all
available
information
at
request
time,
is
therefore
advantageous
over
NetNI
.
However
the
design
of
the
utility
function
to
reduce
uncertainty
in
the
target
random
variable
of
the
Bayeslet
is
challenging
.
Only
a
realistic
evaluation
will
finally
allow
us
to
rate
the
performance
of
both
criteria
and
to
select
the
best
one
.
Term
Cost
evidence
given
actions
the
for
Utility
Expected
Maximum
Bayeslet
linked
a
from
evidence
external
and
evidence
given
actions
the
for
Utility
Expected
Maximum
Expected
)
|
(
)
|
(
)
(
)
(
;
|
)
|
(
,
,
e
A
A
e
i
e
e
C
A
MEU
A
MEU
P
NetVoI
e
e
i
e
This
work
received
funding
from
the
EC’s
7
th
Framework
Programme
[FP
7
/
2007
-
2013
]
under
grant
agreement
no
.
215098
of
the
Persist
Collaborative
Project
and
from
the
Irish
HEA
through
the
PRTLI
cycle
4
project
“Serving
Society
:
Management
of
Future
Communication
Networks
and
Services”
.
),
1
1
(
|
|
log
)
)
(
)
|
(
(log
)
(
|
|
log
)
:
(
)
:
(
b
n
X
X
P
Y
X
P
E
Y
C
X
Y
X
I
Y
X
NetNI
a
X
X,Y
being Bayeslets,
a≥
1
constant
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Preparing document for printing…
0%
Σχόλια 0
Συνδεθείτε για να κοινοποιήσετε σχόλιο