Wireless Sensor Network (WSN) comprises a combination of autonomous devices
that are used in sensor nodes for checking the static and dynamic condition of external
stimuli like force, echo, heat and temperature. It is responsible for transferring the
data over the network to central place. It is applicable in collection of data through
surroundings and includes a huge number of sensor nodes and one or more sensed
devices to perform a task. As we know, it is a combination of wireless sensing and data
networking. WSNs are networks that are constructed autonomous and distributed,
but work in an organized way with tiny sensors. These sensors have sensing capability
that are used for checking, tracing and recognition of environmental as well as physical
surroundings. There are miscellaneous WSN application domains present in different
arenas such as medical applications, sensor and robots, reconfigurable sensor networks, highway monitoring, etc. In this paper, we mainly concentrate on the application of
WSNs in various kinds of commercial as well as noncommercial areas.
A WSN is a combination of two things, one is wireless sensing and the other data
networking. Both seem to be same, but in wireless sensing, sensed data is required and
in data networking, this sensed data works for collecting, storing and checking
information regarding the external stimuli.
Sensor nodes are a collection of tiny devices that are producing a computable
response from physical or nonphysical environmental circumstance by different
deviations. Sensor nodes are low power-consumption devices which consist of one or
more sensors, processor, power supply, radio, an actuator and memory (Estrin et al.,
1999). There are four elementary constituents in a sensor network: first one is an assembly
of distributed or localized sensors; the second one is an interconnecting network that is
usually but not always wireless-based network; the third one is a central point of information
clustering; and the fourth is a set of computing resources at the central point or beyond to
control data association, event trending, status inquiring and data mining (Figure 1)
(Kazem et al., 2007).
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