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In-Depth Information
needs of the application [30, 84, 91, 117]. Examples of specific meth-
ods include energy-timeliness tradeoffs [91], adaptive sampling [84], and
application-specific collection modes [117]. We note that the impact of
such collection policies on data management and processing applications
is likely be significant. Therefore, it is critical to design appropriate data
cleaning and processing methods, which take such issues of data quality
into consideration.
4. Data Management and Analytics
The key to the power of the internet of things paradigm is the abil-
ity to provide real time data from many different distributed sources to
other machines, smart entities and people for a variety of services. One
major challenge is that the underlying data from different resources are
extremely heterogeneous, can be very noisy, and are usually very large
scale and distributed. Furthermore, it is hard for other entities to use
the data effectively, without a clear description of what is available for
processing. In order to enable effective use of this very heterogeneous
and distributed data, frameworks are required to describe the data in
a suciently intuitive way, so that it becomes more easily usable i.e.,
the problem of semantic interoperability is addressed. This leads to un-
precedented challenges both in terms of providing high quality, scalable
and real time analytics, and also in terms of intuitively describing to
users information about what kind of data and services are available in
a variety of scenarios. Therefore, methods are required to clean, man-
age, query and analyze the data in the distributed way. The cleaning
is usually performed at data collection time, and is often embedded in
the middleware which interfaces with the sensor devices. Therefore, the
research on data cleaning is often studied in the context of the
things-
oriented vision
. The issues of providing standardized descriptions and
access to the data for smart services are generally studied in the context
of standardized web protocols and interfaces, and description/querying
frameworks such as offered by
semantic web
technology. The idea is
to reuse the existing web infrastructure in an intuitive way, so the het-
erogeneity and distributed nature of the different data sources can be
seamlessly integrated with the different services. These issues are usually
studied in the context of the
web of things
and the
semantic web
visions.
Thus, the end-to-end data management of IoT technology requires the
unification and collaboration between the different aspects of how these
technologies are developed, in order to provide a seamless and effective
infrastructure.
