Systems, Man & Cybernetics - April 2016 - 24

employing semantic reasoning
parameters and the true user
over contexts [13] or a hierarchical
con text, perhaps obtained
Similarly, there
context inference model [9], [10]) to
from explicit user feedback or
will be statistical
automatically detect such correlaimplicit user actions (e.g., [4]).
tions and concurrency constraints
◆ How does one distinguish
dependencies across
and exploit them in selecting and
and combine between tolercontexts; for example,
tasking available sensors.
ance ranges and sensor
it is unlikely for a
errors? In our model, the tolRelated Work
erance range is not an intrinperson to be agitated
The tradeoff between communisic characteristic of a sensor,
with high BP to be
cation overhead (cost) and the
but it is determined by the conquality of fused data has been
text optimizer: e.g., if q i = ! 10
also simultaneously in
studied with respect to the effect
and the last reported value is
the sleeping state.
of the tolerance ranges on the rel120, the true value of the senative frequency of sink-initiated
sor must lie in the interval
fetching (data pull) versus source(110, 130). Sensing errors (e.g.,
initiated refreshes (data push) [7].
errors in sampling and calibraThe focus, however, has been on snapshot queries and
tion) are, on the other hand, intrinsic to a sensor and
not long-running subscription queries [8]. Temporal cornot application specific. For instance, if a sensor has
relations across successive samples have also been
an error of !2, a reading of 120 could correspond to a
exploited to reduce communication overhead of snapground truth of (118, 122). One way to view the relashot queries [1]; this approach used training data to
tionship between these two variables is to note that,
parameterize a jointly normal density function. The colgiven q and e and a last reported value of v, the
lective adaptive precision setting algorithm [2] is
ground truth of the sensed attribute should lie
designed for long-running aggregation queries (such as
between (v - q - e, v + q + e) . For our approach to
{min, max}) and computes the optimal set of tolerance
work with sensors from different manufacturers and
ranges for a given set of sensors that minimizes commuwith different error characteristics, the context optinication overhead. Unlike such work, which focused
mizer must be able to automatically derive and compurely on structured-query-language-like aggregation
bine these two independent parameters. One practical
queries over a preordained set of sensors, our goal is not
approach to this issue may be to have different senonly to support generalized context queries but also to
sors automatically publish their error ranges in a
simultaneously find the best subset of available sensors
standard format [e.g., using the SensorML format
and their associated tolerance ranges. An energy man(http://www.opengeospatial.org/standards/sensorm1)]
agement framework for wireless sensor networks that
so that our framework can automatically incorporate
simultaneously considers QoINF requirements with
these values. However, as it is well known that senenergy constraints was presented in [5] that views the
sors will deviate from these nominal values over time,
consumption of energy versus QoINF gains game theowe need more research to establish how such deviaretically and can decide to provide lower QoINF if the
tions can be automatically detected under our q i cost of data acquisition is too high; in contrast, we focus
based reporting approach.
on health-care-related environments, where the QoINF
◆ How do we extend our QoINF-based model to
requirement is considered to be inviolable.
consider concurrent and correlated context? In
our formulation thus far, we have implicitly assumed
Conclusions
that a context metric takes on only a single value at a
We motivate the need for a formal framework for enertime (e.g., a wellness management application assumes
gy-efficient determination of physiological context in
that the user is in only one of [sitting, walking, runpervasive health-care deployments, specifically using
ning] states at any instant) and that the different conthe scenario of remotely monitored assisted living. To
text attributes are mutually uncorrelated (e.g., the
this end, we introduce a formal framework for reasondetermination of a person's walking context is uncoring about the inherent tradeoffs between quality of conrelated to her agitated with high BP context). In practext and the cost of acquiring it, followed by the use of
tice, if activity is defined to include both watching TV
this formalization to derive two heuristic algorithms for
and talking on the phone, it is possible for an individucomputing the context inference supporting structure.
al to be engaged simultaneously in both. Similarly,
The key idea is to express the accuracy of context inferthere will be statistical dependencies across contexts;
ence through a QoINF function that captures the
for example, it is unlikely for a person to be agitated
dependence of context estimation accuracy on both the
with high BP to be also simultaneously in the sleeping
set of sensors selected to support context acquisition
state. A more advanced framework is needed (perhaps
24

IEEE SyStEmS, man, & CybErnEtICS magazInE A pri l 2016


http://www.opengeospatial.org/standards/sensorm1

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

Systems, Man & Cybernetics - April 2016 - Cover1
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Systems, Man & Cybernetics - April 2016 - Cover3
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