Systems, Man & Cybernetics - April 2016 - 19

and the QoINF values associated
Choosing a QoINF Function
with different context variables.
for Multiple Sensors
A QoINF function
For example, the context measure
A QoI NF f u nc t ion ex pl icit ly
explicitly relates the
activity state of an individual may
relates the quality of a context
be computed with an inferencing
measure to the sensors (and their
quality of a context
accuracy of 0.90 (i.e., with a 10%
tolerance ranges) that contribute
measure to the
error rate) using a respiratory senthe actual data. Similar work has
sensors (and their
sor but only with 0.80 accuracy
used decision fusion rules based
using a low-quality ECG sensor.
on counting policy using a Poistolerance ranges)
However, by combining the data
son sensor distribution model [6]
that contribute the
available from both sensors, we
or by exploiting statistical depencan achieve an inferencing accuradencies (and independencies) of
actual data.
cy of 0.98. Of course, this mapping
sensors [3]. If the QoINF function
itself will be a function of the tolerwas completely arbitrary, the conance ranges associated with each
text optimizer would have the
sensor. For example, if the tolerance range for the respiramathematically intractable task of performing an
tory sensor degrades to 0.20 and the ECG sensor to 0.15, it
exhaustive search of all possible combinations of senis likely that the inferencing accuracy based on the combisors and tolerance ranges. For a mathematically tractanation of these two sensors will drop from 0.98 to 0.90. We
ble approach (which is also supported well by our
describe a precise approach for expressing such composempirical results), we assume that the estimation error
ite QoINF functional models in the "Choosing a QoINF
for each sensor is statistically independent of the others
Function for Multiple Sensors" section.
[11]. We can then define the QoINF value for a particular
combination of sensors i (with the ith sensor having its
own tolerance range qi) by
A Quality-Aware Context Architecture
Based on the preceding observations, we now present the
|i|
high-level functional components of a QoINF-aware conQoINF (i) = 1 - % {1 - qoinf (i)},
(2)
i=1
text-determination service, i.e., the matchmaking functionality (Figure 3). External applications subscribe to specific
where QoINF(i) is defined in (1). This definition satisfies all
context measures and indicate minimally acceptable
the following observations about valid QoINF measures: 1)
QoINF values. The context optimizer determines the best
its value is within (0,1) and 2) QoINF is nondecreasing in
(least-cost) combination of sensors and their tolerance
the size of i (i.e., incorporating data from an additional senranges that can meet the specified QoINF requirement. The
sor cannot degrade the inference quality) and degrades
transmitted sensor data are received by the context estimawith increasing qi.
tor, which continuously updates the application on the
value inferred for the requested context measure(s).
Context Optimization: Selecting
The rest of this article focuses on the logic of the context
Sensor Settings
optimizer. We describe how we can determine the subset of
We now focus on explaining our second contribution, i.e.,
sensors and their associated tolerance ranges that best satdescribing a process by which the context optimizer can
isfy the varying context requirements of multiple subscribdetermine the best set of sensors and their tolerance
ing applications at the minimum cost.
While many other measures of cost can be
considered, we have explicitly focused on
QoINF-Aware Answers
QoINF-Aware Queries
minimizing the sum of the transmission
(Cvalue, QoINFvalue)
(Ctype, QoINFmin)
costs associated with each individual sensor (as wireless transmissions cost is one of
the most significant energy burdens in senqi , θ
Context
Context
QoINF
Context
sor-based contextual applications). From
Modeler
Optimizer
Function
Estimator
past work [2], cos t i (q i) (the average transmission energy overhead associated with
Send Tolerance Ranges
Collect Sensor Streams
sensor si) is proportional to both the num...
...
ber of wireless hops (hi) utilized to transport the samples to the context estimator
s2q2
smqm
snqn
s1q1
...Sensors...
and the tolerance range qi
cos t i (q i) ?

hi
.
q 2i

Figure 3. an architecture for QoInF-aware context determination.

Ap ri l 2016

IEEE SyStEmS, man, & CybErnEtICS magazInE

19



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - April 2016

Systems, Man & Cybernetics - April 2016 - Cover1
Systems, Man & Cybernetics - April 2016 - Cover2
Systems, Man & Cybernetics - April 2016 - 1
Systems, Man & Cybernetics - April 2016 - 2
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Systems, Man & Cybernetics - April 2016 - Cover3
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