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and mainly consists of sensor networks. The network layer is responsible for
information transmission and processing, where close transmission may rely on
sensor networks, and remote transmission shall depend on the Internet. Finally, the
application layer support specific applications of IoT.
According to the characteristics of IoT, the data generated from IoT has the
following features:
Large-Scale Data
: in IoT, masses of data acquisition equipments are distributedly
deployed, which may acquire simple numeric data (e.g., location) or complex
multimedia data (e.g., surveillance video). In order to meet the demands of
analysis and processing, not only the currently acquired data, but also the
historical data within a certain time frame should be stored. Therefore, data
generated by IoT are characterized by large scales.
Heterogeneity
: because of the variety data acquisition devices, the acquired data
is also different and such data features heterogeneity.
Strong Time and Space Correlation
: in IoT, every data acquisition device are
placed at a specific geographic location and every piece of data has a time stamp.
The time and space correlations are important properties of data from IoT. During
data analysis and processing, time and space are also important dimensions for
statistical analysis.
Effective Data Accounts for Only a Small Portion of the Big Data
: a great
quantity of noises may occur during the acquisition and transmission of data
in IoT. Among datasets acquired by acquisition devices, only a small amount of
abnormal data is valuable. For example, during the acquisition of traffic video,
the few video frames that capture the violation of traffic regulations and traffic
accidents are more valuable than those only capturing the normal flow of traffic.
3.1.3
Internet Data
Internet data consists of searching entries, Internet forum posts, chatting records,
and microblog messages, among others, which have similar features, such as high
value and low density. Such Internet data may be valueless individually, but through
exploitation of accumulated big data, useful information such as habits and hobbies
of users can be identified, and it is even possible to forecast users' behavior and
emotional moods.
3.1.4
Bio-medical Data
As a series of high-throughput bio-measurement technologies are innovatively
developed in the beginning of the twenty-first century, the frontier research in
the bio-medicine field also enters the era of big data. By constructing smart,
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