Database Reference
In-Depth Information
issues are discussed in detail in Chapter 9. The chapter also discusses
the issues of mining the different kinds of GPS- and content-based data
generated in such applications.
Much of the data in social sensing applications often contains GPS
trajectory data. Mobile data has a number of characteristics, which can
be exploited in order to create more ecient methods for clustering, clas-
sification, anomaly detection, and pattern mining. Therefore, we have
included a chapter which discusses algorithms for mobile data analysis
in detail. Chapter 10 provides a detailed discussion of a wide variety of
indexing and mining algorithms in the context of mobile data.
RFID Data and the Internet of Things
The trend towards ubiq-
uitous and embedded sensing has lead to a natural focus on machine-
to-machine (M2M) paradigms in sensor processing. These paradigms
use small RFID sensors to collect data about many smart objects. The
data generated from such applications can be shared by different devices
for heterogeneous fusion and inference, especially if the devices are con-
nected to the internet. A number of issues also arise about how such
devices can be effectively discovered and used by different network par-
ticipants. Chapter 11 provides an overview on RFID applications for
collecting such data. Issues about how such data can be used in the
context of the internet of things are discussed in Chapter 12.
Software Bug Tracing in Sensor Networks
Most of the afore-
mentioned chapters provide application-specific insights on the basis of
the collected data. Sensors also produce diagnostic data, which can be
used in order to determine diagnostic bugs within the sensor software.
Thus, this kind of mining process can be used in order to improve the
performance of the underlying sensor network. A survey of methods
and algorithms for software bug tracing in sensor networks is provided
in Chapter 13.
Healthcare Applications
Sensor data has found increasing applica-
tion in the health care domain. A wide variety of Intensive Care Unit
(ICU) applications use sensors such as ECG, EEG, blood pressure mon-
itors, respiratory monitors, and a wide variety of other sensors in order
to track the condition of the patient. The volume of such data is ex-
tremely large and the inferences from such data need to be performed
in a time-critical fashion. Chapter 14 provides an overview of sensor
mining applications in the context of health-care data.
