Database Reference
In-Depth Information
sourcing is generally achieved through sensors which are closely attached
to humans, either in wearable form, or in their mobile phones. Some
examples of integration of social and sensor networks are as follows:
A variety of applications can be created to collect real time in-
formation from large groups of individuals in order to harness the
wisdom of crowds
in a variety of decision processes. For example,
the
Google Latitude
application [184] collects mobile position data
of uses, and uses this in order to detect the proximity of users with
their friends. This can lead to significant events of interest. For
example, proximity alerts may be triggered when two linked users
are within geographical proximity of one another. This may itself
trigger changes in the user-behavior patterns, and therefore the
corresponding sensor values. This is generally true of many ap-
plications, the data on one sensor can influence data in the other
sensors. Numerous other GPS-enabled applications such as
City
sense
,
Macrosense
,and
Wikitude
[185, 195, 191] serve as gps-based
social aggregators for making a variety of personalized recommen-
dations. The approach has even been used for real-time grocery
bargain hunting with the
LiveCompare
system [46].
Vehicle Tracking Applications:
A number of real-time automotive
tracking applications determine the important points of congestion
in the city by pooling GPS data from the vehicles in the city. This
can be used by other drivers in order to avoid points of congestion
in the city. In many applications, such objects may have implicit
links among them. For example, in a military application, the
different vehicles may have links depending upon their unit mem-
bership or other related data. Two classic examples of vehicular
applications in the context of participatory sensing are the
CarTel
[88] and
GreenGPS
[64] systems.
Trajectory Tracking:
In its most general interpretation, an actor
in a social network need not necessary be a person, but can be any
living entity such as an animal. Recently, animal tracking data is
collected with the use of radio-frequency identifiers. A number of
social links may exist between the different animals such as group
membership, or family membership. It is extremely useful to uti-
lize the sensor information in order to predict linkage information
and vice-versa. A recent project called
MoveBank
[186] has made
tremendous advances in collecting such data sets. We note that
a similar approach may be used for commercial product-tracking
applications, though social networking applications are generally
relevant to living entities, which are most typically people.
