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Fig. 2.2
Illustration of the IoT architecture
2.2.2
Relationship Between IoT and Big Data
In the IoT paradigm, an enormous amount of network sensors are embedded into
devices in the real world. Such sensors deployed in different fields may collect
various kinds of data, such as environmental data, geographical data, astronomical
data, and logistic data. Mobile equipments, transportation facilities, public facilities,
and home appliances could all be data acquisition equipment in IoT.
The big data generated by IoT has different characteristics compared with general
big data because of the different types of data collected, of which the most classical
characteristics include heterogeneity, variety, unstructured feature, noise, and rapid
growth. Although the current IoT data is not the dominant part of big data, by 2030,
the quantity of sensors will reach one trillion and then the IoT data could be the
most important part of big data, according to the forecast of HP. A report from
Intel pointed out that big data in IoT has three features that conform to the big data
paradigm: (a) abundant terminals generating masses of data; (b) data generated by
IoT is usually semi-structured or unstructured; (c) data of IoT is useful only when it
is analyzed.
At present, the data processing capacity of IoT has fallen behind the collected
data and it is extremely urgent to accelerate the introduction of big data technologies
to catch up with the development of IoT. Many operators of IoT realize the
importance of big data since the success of IoT is hinged upon the effective
integration of big data and cloud computing. The widespread deployment of IoT
will also bring many cities into the big data era.
There is a compelling need to adopt big data for IoT applications, while the
development of big data is already legged behind. It has been widely recognized
that these two technologies are inter-dependent and should be jointly developed.
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