Quantised Compressive Sampling using Genetic Algorithm in Dense Wireless Sensor Networks
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Date
2014
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Punjab Agricultural University, Ludhiana
Abstract
Due to increasing exchange of data in services such as internet, e-mail and data file transfer;
wireless networking has gone through an exponential growth. The wireless senor network is
made of small or large number of tiny nodes which have limited battery power. Source node
can easily route the data to destination if it has sufficient battery power. If the destination
node is far away from the source node, then the source node should have large battery power
to transmit data. But after few transmissions a threshold level comes, when that source node is
dead and no node is present for transmission. And the overall lifetime of network will
decrease. In wireless sensor network, the nodes have limited battery capacity and limited
initial energy that is consumed at different rates. The lifetime of network is defined as the
time until the first node fails. Some of the major challenges in wireless sensor networks are to
minimize the energy consumption, end- to- end delay and to increase the throughput of sensor
node to enhance the lifetime of sensor network. The routing protocols have great impact on
the lifetime of sensor network. During research work, the objective has been confined to
increase the lifetime of the network using the HUFFMAN’s data compression technique and
then Genetic Algorithm is used further to transmit data to the Base Station. It has been found
that network lifetime is highly enhanced in terms of increased number of rounds. The data
compression is being applied on the cluster head which aggregates the data being collected
from all other sensor nodes. So in this way the big amount of redundant data is being removed
at the cluster head level. The simulation has been performed in MATLAB software. The
work is being performed in the homogeneous environment. The balance in the energy
consumption is also achieved by performing this data compression technique. Because it
reduces the extra load on the cluster head which makes its life much longer as compare to
earlier one. In the future, the work will be focused to consider heterogeneous network and
mobility of sink can also be introduced to investigate the performance of proposed technique.
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Keywords
Wireless Sensor Networks, biological phenomena, developmental stages, protocols (treaties), area, fruits, selection, harvesting, manpower, environment, marketing, WSN, genetic algorithm