Database Reference
In-Depth Information
have some quality issues because of the conversion process from volt-
ages to measured values, and other kinds of noise. Nevertheless, from a
comparative point of view, wireless sensor networks do have a number
of advantages in terms of the quality, range, privacy and security of the
data collected and transmitted, and are likely to play a significant role
in the internet of things.
3.4 Mobile Connectivity
A significant number of objects in the internet of things, such as mo-
bile phones can be connected by 3G and WiFi connectivity. However,
the power usage of such systems is quite high. Such solutions are of
course sometimes workable, because such objects fall within the
social
sensing
paradigm, where each mobile object belongs to a participant
who is responsible for maintaining the battery and other connectivity
aspects of the sensing object which is transmitting the data. In such
cases, however, the privacy of the transmitted data (eg. GPS location)
becomes sensitive, and it is important to design privacy preservation
paradigms in order to either limit the data transmission, or reduce the
fidelity of the transmitted data. This is of course not desirable from the
data analytics perspective, because it reduces the quality of the data
analytics output. Correspondingly, the user-trust in the data analytics
results are also reduced.
Since mobile phones are usually designed for communication-centric
applications, they may only have certain sensors such as GPS, accelerom-
eters, microphones, or video-cameras, which are largely user centric.
Also they may allow direct human input into the sensor process. Never-
theless, they do have a number of limitations in not being able to collect
arbitrarily kinds of sensed data (eg. humidity). Therefore, the applica-
bility of such devices is often in the context of user-centric applications
such as social sensing [8], or working with other smart devices in the
context of a broader smart infrastructure.
Since such connectivity has high power requirements, it is important
to make the data collection as energy ecient as possible. A salient
point to be kept in mind is that data collection can sometimes be per-
formed with the use of multiple methods in the same devices (eg. ap-
proximate cell phone tower positioning vs. accurate GPS for location
information). Furthermore, tradeoffs are also possible during data
trans-
mission
between timeliness and energy consumption (eg. real-time 3G
vs. opportunistic WiFi). A variety of methods have been proposed in
recent years, for calibrating these different tradeoffs, so that the energy
eciency is maximized with significantly compromising the data-centric
