Database Reference
In-Depth Information
The sensing abilities of miniaturized devices and smartphones have
also increased considerably in recent years. For example, the one of the
earliest systems, which is referred to as a
sociometer
[33, 34], a small
wearable device is constructed, which can detect people nearby, provide
motion information and accelerometers, and also has microphones for
detection of speech information. In addition, the device has the flexi-
bility to allow for the addition of other kinds of sensors such as GPS
sensors and light sensors. These sensors can be used in order to detect
implicit links between people, and the corresponding community behav-
ior. The aim of collecting a large number of such interactive behaviors is
to be able to effectively model interactions, between different users, and
then model the dynamics of the interaction with the use of the collected
information.
Since the work in [33], much of these sensing capabilities are now avail-
able in commodity hardware such as mobile phones. For example, the
Virtual Compass
system [18] uses the sensors available in mobile phones
in order to sense the interactions between different actors. Virtual Com-
pass is a peer-based relative positioning system that uses multiple radios
to detect nearby mobile devices and places them in a two-dimensional
plane. It uses different kinds of scanning and out-of-band coordination
to explore tradeoffs between energy consumption, and the latency in
detecting movement. Methods are designed for using different kinds of
sensor signals in
Virtual Compass
in order to reduce the energy footprint.
More details may be found in [18].
3. Data Collection, Architectural and System
Design Challenges
The aforementioned monitoring and social computing opportunities
present a need for a new architecture that encourages data sharing and
eciently utilizes data contributed by users. The architecture should
allow individuals, organizations, research institutions, and policy mak-
ers to deploy applications that monitor, investigate, or clarify aspects
of
socio-physical
phenomena; processes that interact with the physical
world, whose state depends on the behavior of humans in the loop.
An architecture for social data collection should facilitate distillation
of concise actionable information from significant amounts of raw data
contributed by a variety of sources, to inform high-level user decisions.
Such an architecture would typically consist of components that sup-
port (i) privacy-preserving sensor data collection, (ii) data model con-
struction, and (iii) real-time decision services. (iv) effective methods
for recruitment, and (v) energy ecient design. For example, in an ap-
