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High Level Parameter Analysis in WSN
Based on WMCL Algorithm
PRESENTED BY,
M. Abinaya
M. Shafina
GUIDED BY,
Mr. M. Vasim Babu, M.E(Ph.D.,)
Asst. Professor
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ABSTRACT
In this project we propose an energy efficient algorithmcalled WMCL combine With AODV to analyze high level
parameter like RSSI,TOA,Error probability, Packet
transmission ratio.
Achieve both high sampling efficiency and high localizationaccuracy in various developments.
Our method can further reduce the size of a sensor nodes
bounding-box by a factor of up to 87 percent , and We achieve
localization accuracy by a factor of up to 98 percent.
Uses the estimated position information of sensor nodes to
improve localization accuracy.
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EXISTING WORK
In this method localization algorithms for mobile sensornetworks are usually based on the Sequential Monte Carlo
(SMC) method.
They either suffer from low sampling efficiency or require
high beacon density to achieve high localization accuracy.
Although papers can be found for solving the above
problems separately, there is no solution which addresses both
problem.
They dont consider RSSI,TOA.Node density of sensor
network.
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Power level and Range calculation are not mentioned.,
Minimum error rectification hence range free problem
can be occur.
Path loss and interferences cannot be considered.
Particularly RSSI cannot be computed or mentioned
here.
LIMITATIONS OF EXISTING ALGORITHM
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PROPOSED WORK
We describe our proposed modified WMCL algorithm
combine with AODV.
We propose a set of algorithms which achieve both high
energy efficiency and high localization accuracy.
The results from our simulations and graphs validate the
effectiveness of our proposed algorithms.
Our method Improve of localization accuracy and reducethe energy consumption.
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Algorithm Description
Minimal transmit power(MTP): Choose
the node with the minimal transmit power
Maximal energy-efficiency index (MEI)
Define the energy efficiency index of thek-th relay as the ratioof Ek to Pk,d andselect the relay with the maximalindex,
i.e.,
That is, the node whose transmit power
occupies the least portion of its current
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Minimal outage probability(MOP): Inthis scheme, we select the node with the
smallest outage probability after it ischosen to transmit. We apply the strategyto the case
with the discrete power level by choosing
where 1k is an N1 column vectorwhose k-th elementis one and othersare equal to zero and the maximal powerconstraint is assumed infinity to have anode selection at hi h residual ener .
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Start Setup phase
The nodes whose sensing area coverage is covered byneighbours forms temporary cluster heads
Select the desired number of CH for a round CH broadcast hello message
Clusters are formed depending on signal strength a nodereceives from different CHs
Nodes broadcast location, range and area they cover via hellomessage.
Nodes build a table of their neighbours depending on the hellomessage they receive from neighbours.
The temporary cluster heads and cluster heads forms the toplayer of communication
The sensor nodes forms the bottom layer of communication
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Procedure to control the Transmissionpower (T, R) based on Makov process
Step: 1 Node Connected
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AODV/MAC PROTOCOL:
Ad-hoc On Demand Vector protocol.
It uses table driven model and hance updating the routing
table contents for managing the network efficiently.
It may handle congestion even for the random
eploynmaent of large number of nodes.
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The state transition diagram ofan energy-consuming process.
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Other possible methods to improve AODV
congestion handling:
A route may predict when congestion is about
to occur and try to avoid it by reduce the
transmission rate.
Each node maintains a routing table thatcontains information about reaching destination
nodes.
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Existing algorithmConcentrate on sampling efficiency and also having high
computational cost.
Proposed algorithm
Here we concentrate on all high level parameters such as
RSSI, Throughput,AOA,TOA, etc.,
Low computational cost
COMPARATIVE STATEMENT
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Throughput Analysis:
Throughtput level is
more than the
existing system.
Here we achieved thethroughtput value of
35000 as the peak
value.
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In the case of
simulation time Vs
throughtput graph
varies linearly. Thus we achieved 53
Mbps through our
algorithm.
Simulation time :
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RSSI Analysis:
By using our algorithm
we maintained the sensor
energy level constant up
to 100m while existing
method does not.
This can dramatically
increases the life time of
the sensors.
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Total Remaining Energy:
This energy is
approximately greater
than exsisting system in
the range of 1000 nano
joules.
These remaining energy
will help the sensor to
manage the multihop
system efficiently.
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End To End Delay:
The minimization
average end to end delay
of the sensor nodes can
leads to energy efficient
algorithm.
Here we reduce the end
to end delay than the
Existing method.
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Average Remaining Energy:
We can save energy
more than the exsisting
system in the order of
25 nano joules
comparitively.
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Sensor Life Time:
We analyse the life time
of the sensor as the
special case.while
existing algorithm
cannot.
Constantly maintain the
life time even when the
number of roundsincrease.
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Localization Accuracy:
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PDR Analysis:
The efficiency of the
network depends upon
the delivery of packets at
particular time.
Hence our algorithm
have hiher PDR ratio
comparitively.
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Error Probability Analysis:
The error probability canbe achieved to 0.3 while
existing system achieves
in the range of 0.7
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SIMULATION RESULTS
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CONCLUSION
In this paper, we present an approach does not requireadditional hardware on the nodes and works even when
the movement of seeds.
By using WMCL With AODV/MAC protocol we canhandle the congestion efficiently.
We are also mentioned the graph model, which helps to
compare the existing with the proposed work.
Reduces the computational and communicational cost.
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