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Untitled Prezi

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Shreya Animesh

on 23 April 2014

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~Ms. Shreya Animesh,
~Mr. Preetesh Shivam,
~Mr. Raghunandana Alse Airody,
~Ms. Pallavi R
CSE, Sir MVIT
Power Management in Wireless
Sensor Networks Using Data
Reduction Approaches:
A Survey

Introduction
Energy Conservation Schemes
Conclusion
Sensor Subsystems and Network Subsystems
Bring about TWO levels of data reduction

First at the node level by using Data Compression techniques

Second Data compression can be done at the Sink using Matrix Completion method
WSN which are battery powered, present a challenge of
long term sustainability
. So power management is an important concern.
It is noticed that data transmission uses more power than data processing
A Wireless Sensor Network (WSN) consists of:
Nodes
Sink
Network
Power Management
Power management in sensor networks can be done in two ways:
Energy Harvesting
Battery Management
Energy Harvesting
Advantages:
Battery Management
We can reduce the energy consumption of the nodes as much as possible and by using energy aware system which reduces the consumption of battery power.

It can be done at two levels:
Sensor Subsystems
Network Subsystems
Varies with time
Nodes at remote places
Disadvantages
Sensor Subsystem
When the power management is at the sensor level it's called sensor subsystem.We look at the
individual node
instead of the whole network of sensors.
Architecture of a Sensor Node
no need to replace batteries or change sensor nodes when battery drains
Network Subsystem
Power management at the network level. Here we concentrate on the whole network of sensors, so whatever considerations we take it'll be for a group of sensors rather than individual sensors.
Data Reduction
It is the process in which the large entity of the collected data is converted to the smaller useful entity so that at a later stage the same data can be retrieved without any loss.
Techniques of Data Reduction
DATA DRIVEN APPROACHES
•Data Reduction
•Energy Efficient Data Acquisition
In Network Processing
Network is subdivided into clusters which consists of nodes and an aggregator
Nodes send their data to the aggregator
aggregator, processes similar data and sends it to the Sink
Data Compression
In this method each node compresses the received data and then transmits it when needed.

Some of the algorithms that are used for data compressions are:
•Lossless compression
•Deterministic compression techniques

Used in:
Time sensitive phenomena like change in temperature
Matrix Completion
Sink activates some nodes
These nodes accumulate data randomly and send it to the sink
Sink uses sophisticated algorithms and estimates the data for the whole network
Data Prediction
These models are sampled through any of the following techniques:
This approach mainly depends on the previously sampled models
•Stochastic Approaches
•Time series forecasting
•Algorithmic approaches
Comparison
In Network
processing
Data
Compression
Matrix
completion
Data Prediction
Accuracy
Communication
overload on node
Communication overload on sink
Processing overload
very high
very high
low
high
high
high
Less compared to
in network
Slightly more than
in network
Moderate
Moderate
Slightly less than
data compression
high
Moderate
Dependent on the
input to the node
Dependent on the
input to the node
Dependent on the
input to the node
References
Wireless Sensor Networks ,Salvatore La Malfa
A. Majumdar and Rabab K. Ward, “A MATRIX COMPLETION APPROACH TO REDUCE ENERGY CONSUMPTION INWIRELESS SENSOR NETWORKS” in IEEE- Data Compression Conference (DCC), 2010, pp: 542
Data Reduction in Low Powered Wireless Sensor Networks, Qutub Ali Bakhtiar, Kia Makki and Niki Pissinou
A Simple Algorithm for Data Compression in Wireless Sensor Networks, Francesco Marcelloni, Member, IEEE, and Massimo Vecchio, Member, IEEE
Energy Conservation in Wireless Sensor Networks: a Survey, Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella
Thank you
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