Database Reference
In-Depth Information
Applications to Healthcare:
In recent years, numerous medical sen-
sor devices can be used in order to track the personal health of
individuals, or make other predictions about their lifestyle [41, 65,
84, 119, 121, 122, 150]. This can be used for emergency response,
long term predictions about diseases such as dementia, or other life
style influence analysis of factors such as eating habits and obesity.
Social sensing applications provide numerous research challenges from
the perspective of analysis. We list some of these challenges below:
Since the collected data typically contains sensitive personal data
(eg. location data), it is extremely important to use privacy-
sensitive techniques [61, 133] in order to perform the analysis.
A recent technique called
PoolView
[61] designs privacy-sensitive
techniques for collecting and using mobile sensor data.
Sensors, whether wearable or embedded in mobile devices, are typi-
cally operated with the use of batteries, which have limited battery
life. Certain kinds of sensor data collection can drain the battery
life more quickly than others (eg. GPS vs. cell tower/WiFi lo-
cation tracking in a mobile phone). Therefore, it is critical to
design the applications with a careful understanding of the un-
derlying tradeoffs, so that the battery life is maximized without
significantly compromising the goals of the application.
The volume of data collected can be very large. For example, in
a mobile application, one may track the location information of
millions of users simultaneously. Therefore, it is useful to be able
to design techniques which can compress and eciently process
the large amounts of collected data.
Since the data are often collected through sensors which are error-
prone, or may be input by individuals without any verification,
this leads to numerous challenges about the
trustworthiness
of the
data collected. Furthermore, the goals of privacy and trust tend
to be at odds with one another, because most privacy-preservation
schemes reduce the fidelity of the data, whereas trust is based on
high fidelity of the data.
Many of the applications require
dynamic
and
real time
responses.
For example, applications which trigger alerts are typically time-
sensitive and the responses may be real-time. The real-time as-
pects of such applications may create significant challenges, con-
sidering the large number of sensors which are tracked at a given
time.
