wsn and big data

1. Wireless Sensor Networks
Wireless sensor networks (WSN),are spatially
distributed autonomous sensors to monitor physical
or environmental conditions, such as temperature,
sound, pressure, etc. and to cooperatively pass their
data through the network to a main location. The
more modern networks are bi-directional, also
enabling control of sensor activity. The development
of wireless sensor networks was motivated by
military applications such as surveillance; today
such networks are used in many industrial and
consumer applications, such as industrial process
monitoring and control, machine health monitoring,
and so on.

2. • The WSN is built of "nodes" – from a few to
several hundreds or even thousands, where
each node is connected to one (or sometimes
several) sensors. Each such sensor network
node has typically several parts: a radio
transceiver with an internal antenna or
connection to an external antenna, a
microcontroller, an electronic circuit for
interfacing with the sensors and an energy
source, usually a battery

5. characteristics
1. Power consumption constraints for nodes
using batteries or energy harvesting
2. Chance to cope with node failures
3. Mobility of nodes
4. Heterogeneity of nodes
5. Scalability to large scale of deployment
6. Simplicity of use

6. Advantages
1. Network setups can be carried out without fixed
infrastructure.
2. Suitable for the non-reachable places such as
over the sea, mountains, rural areas or deep
forests.
3. Flexible if there is random situation when
additional workstation is needed.
4. Implementation pricing is cheap.
5. It avoids plenty of wiring.
6. It might accommodate new devices at any time.
7. It's flexible to undergo physical partitions.
8. It can be accessed by using a centralized monitor.

7. Disadvantages
1)Less secure because hackers can enter the access point and
obtain all the information.
2. Lower speed as compared to a wired network.
3. More complicated to configure compared to a wired
network.
4. Easily troubled by surroundings (walls, microwave, large
distances due to signal attenuation, etc).
5. It is easy for hackers to hack it we couldn’t control
propagation of waves.
6. Comparatively low speed of communication.
7. Gets distracted by various elements like Blue-tooth.
8.still Costly (most importantly).

8. Big Data Analytics
• Big Data are high volume, high velocity, and/or
high-variety information assets that require
new forms of processing to enable enhanced
decision making, insight discovery and process
optimization

9. Sources of Big Data
• Black Box Data: This is the data generated by airplanes,
including jets and helicopters. Black box data includes flight
crew voices, microphone recordings, and aircraft
performance information.
• Social Media Data: This is data developed by such social
media sites as Twitter, Facebook, Instagram, Pinterest, and
Google+.
• Stock Exchange Data: This is data from stock exchanges about
the share selling and buying decisions made by customers.
• Power Grid Data: This is data from power grids. It holds
information on particular nodes such as usage information.
• Transport Data: This includes possible capacity, vehicle
model, availability, and distance covered by a vehicle.
• Search Engine Data: This is one of the biggest sources of big
data. Search engines have vast databases where they get
their data.

11. Characteristics