Wireless sensors are used for various purposes now days. One of the best examples is temperature sensing at various geographical locations. This presentation is based on how to reduce energy consumption while using wireless sensors.

1. PREPARED BY-ANIKET CHOUDHURY
1st Sem ME CE LJIET

2. • INTRODUCTION
• COMPONENT OF SENSOR NODE
• ARHITECTURE OF SENSOR NODE
• GENERAL APPROACHES TO ENERGY CONSERVATION
• ENERGY CONSERVATION SCHEMES
• DUTY CYCLING

3. CONT…

4. CONT…
DATA PREDICTION[1]
•Data prediction techniques build a model describing the
sensed phenomenon, so that queries can be answered
using the model instead of the actually sensed data.
•There are two instances of a model in the network, one
residing at the sink and the other at source nodes.
•The model at the sink can be used to answer queries
without requiring any communication, thus reducing
the energy consumption.
•Source nodes is used to ensure the model effectiveness.

5. CONT…
Stochastic approaches[1]
• Techniques belonging to the first class derive a
stochastic characterization of the phenomenon, i.e. in
terms of probabilities and/or statistical properties.
• Two main approaches of this kind are the following.
On the one hand, it is possible to map data into a

6. random process described in terms of a probability
density function (pdf). Data prediction is then
obtained by combining the computed pdfs with
the observed samples.
• On the other hand, a state space representation of
the phenomenon can be derived, so that
forthcoming samples can be guessed by filtering
out a non-predictable component modeled as
noise.

7. CONT…
TIME SERIES FORECASTING[1]
•A typical method to represent time series is given by
Moving Average (MA), Auto-Regressive (AR) or a Auto-
Regressive Moving Average (ARMA) models.
•These models are quite simple, but they can be used in
many practical cases with good accuracy.
• More sophisticated models have been also developed
(as ARIMA and GARCH), but their complexity does not
make them suitable for WSN.

8. CONT…
ALGORITHMIC APPROCH
• Finally, the last class of data prediction techniques
relies on a heuristic or a state-transition model
describing the sensed phenomenon.
• Such algorithmic approaches drive methods or
procedures to build and update the model on the
basis of the chosen characterization.

9. CONT…

10. • An emerging class of applications is actually
sensing constrained. This is in contrast with the
general assumption that sensing in not relevant
from the energy consumption standpoint.
• In fact, the energy consumption of the sensing
subsystem not only may be relevant, but it can
also be greater than the energy consumption of
the radio or even greater than the energy
consumption of the rest of the sensor node . This
can be due to many different factors.

11. ADAPTIVE SAMPLING[1]
• As measured samples can be correlated, adaptive
sampling techniques exploit such similarities to
reduce the amount of data to be acquired from the
transducer. For example, data of interest may change
slowly with time.
• In this case, temporal correlations (i.e. the fact that
subsequent samples do not differ very much
between each other) may be exploited to reduce the
number of acquisitions.

12. • A similar approach can be applied when the
investigated phenomenon does not change
sharply between areas covered by neighbouring
nodes. In this case, energy due to sampling (and
communication) can be reduced by taking
advantage from spatial correlations between
sensed data.
• Clearly, both temporal and spatial correlations
may be jointly exploited to further reduce the
amount of data to be acquired.

13. HIERARCHICAL SAMPLING[1]
• The hierarchical sampling approach assumes
that nodes are equipped with different types
of sensors. As each sensor is characterized by
a given resolution and its associated energy
consumption, this technique dynamically
selects which class to activate, in order to get
a trade off between accuracy and energy
conservation.

14. MODEL-BASED SAMPLING[1]
• Model-based active sampling takes an approach
similar to data prediction. A model of the sensed
phenomenon is built upon sampled data, so that
future values can be forecasted with a certain
accuracy. Model-based active sampling exploits
the obtained model to reduce the number of data
samples, and also the amount of data to be
transmitted to the sink – even though this is not
their main goal.

15. CONT…

16. CONT…
• Solar cell works on the principle of photovoltaic
effect[2]. Sunlight is composed of photons, or
"packets" of energy.
• These photons contain various amounts of
energy corresponding to the different
wavelengths of light.
• When a photon[2] is absorbed, the energy of the
photon is transferred to an electron in an atom of
the cell.

17. CONT…
The basic component of a solar cell is pure silicon.

18. CONT…

19. CONT…
Specifications Of Charger[3]
• Uses high-efficiency mono crystalline silicon.
• Solar panel: 5.5V/1000mA
• Output voltage: 5.5V
• Output current: 300-550 mA
• Time taken to charge mobile phone using the
charger : about 60mintues for typical mobile.

20. CONT…
BLOCK DIAGRAM:

21. CONT…
• In the above diagram it is given that the charger is
connected to the 5V regulator which in turn is
connected to the solar panel.
• The charger is connected to the cells all the time
which powers the sensors.
• By doing this the cells of the sensors will be
charged constantly.

22. CONT…
• This will help the cells of the sensors to last long
before being dead.
• There are various advantages of using solar
panels which are as follows:
• Solar chargers will offer more Battery life as high
voltages are not developed.
• Adaptability is high.
• Flexibility of Solar mobile charger is high.

23. CONT…
• After reviewing 5 listed papers we can say that
the battery life of the wireless sensors can be
improved by Data Driven Approach and by
connecting solar panels, so that the battery of the
sensor can be continuously charged.
• How ever as ever coin has two sides there are
some flaws in these methods as solar panels can
increase the cost of the panel.
• Solar panels are inefficient during winters and
monsoon time.

24. CONT…
1. DATA DRIVEN PERFORMANCE EVOLUATION OF WIRELESS
SENSOR NETWORK.
2. SOLAR BATTERY CHARGER FOR NIMH BATTERY.
3. SOLAR POWERED BATTERY CHARGING SYSTEM.
4. SOLAR POWER FOR WIRELESS SENSOR NETWORK.
5. SOLAR POWERED WIRELESS SENSOR NETWORK.