SOCIAL SENSING - Managing and Mining Sensor Data - page 237

gate behavior of self-selected communities or the external environ-
ment in which these communities function. Examples may include
understanding tra c conditions in a city, understanding environ-
mental pollution levels, or measuring obesity trends. Sensors in
the possession of large numbers of individuals enable exploiting
the crowd for massively distributed data collection and processing.
Recent literature reports on several efforts that exploit individuals
for data collection and processing purposes such as collection of ve-
hicular GPS trajectories as a way for developing street maps [78],
collectively locating items of interest using cell-phone reports, such
as mapping speed traps using the Trapster application [190], use
of massive human input to translate documents [145], and the de-
velopment of protein folding games that use competition among
players to implement the equivalent of global optimization algo-
rithms [21].
The above trends are enabled by the emergence of large-scale data
collection opportunities, brought about by the proliferation of sensing
devices of every-day use such as cell-phones, piedometers, smart energy
meters, fuel consumption sensors (standardized in modern vehicles), and
GPS navigators. The proliferation of many sensors in the possession of
the common individual creates an unprecedented potential for build-
ing services that leverage massive amounts data collected from willing
participants, or involving such participants as elements of distributed
computing applications. Social networks, in a sensor-rich world, have
become inherently multi-modal data sources, because of the richness of
the data collection process in the context of the network structure. In
recent years, sensor data collection techniques and services have been in-
tegrated into many kinds of social networks. These services have caused
a computational paradigm shift, known as crowd-sourcing [23, 47], re-
ferring to the involvement of the general population in data collection
and processing. Crowd-sourcing, arguably pioneered by programs such
as SETI, has become remarkably successful recently due to increased
networking, mobile connectivity and geo-tagging [1]. We note that the
phenomenon of crowd-sourcing is not exclusive to sensor data, but is also
applied to other tagging and annotation processes, in which the knowl-
edge is sourced from a social network of users. A classic example of a
crowd-sourcing application is the Amazon Mechanical Turk [192], which
allows users to submit data records for annotation at the payment of a
fee for annotation purposes. Thus, the Amazon Mechanical Turk serves
as an intermediary for crowd-sourcing of annotations for data records.
In the case of social sensing whichisalsooftenreferredtoas people-
centric sensing [6, 26, 123] or participatory sensing [24], this crowd-