clustering in wireless sensor networks with compressive sensing
Post on 11-Apr-2017
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CLUSTERING IN WIRELESS SENSOR NETWORKS USING
COMPRESSIVE SENSING
Wireless sensor network
• Sensor nodes distributed for observing physical and environmental conditions
Clustering for data routing•Clustering is a data aggregation method
Clustering in wireless sensor networks
Compressive sensing
• Mathematical technique• Works on the logic of : ‘information’ bandwidth<‘total’ bandwidth• States that it is possible to acquire same
amount of data from fewer measurements than we take conventionally.
• Applicable as WSN data is largely of sparse nature.
Data sensed by the sensor nodes
M<<N samples are transmitted
Y=φx projections are transmitted Obtain x by using
l-1 minimalization
Accurate recovery of signals
Compressive ratio = M/N
Analysis and comparison
We compare routing using• Clustering with no CS• clustering with Hybrid CS• SPT with no CS• SPT with hybrid CS Parameters of comparison• No. of transmissions vs. no. of nodes• Reduction ratio of transmissions
Clustering with Hybrid CS
Simulation of the project
TOPOLOGY FORMATION
CLUSTER FORMATION
SINK
2
0 m
10 m
Clustering in simulationCLUSTER HEAD’S
SINK AT (0,0)
ResultsWHEN THE COMPRESSIVE RATIO IS 10
if no. of measurements =1/10 (no. of nodes )
Reduction Ratio Method 1 Method 2
60 Clustering with hybrid CS
Clustering without CS
50 Clustering with hybrid CS
SPT without CS
30 Clustering with hybrid CS
SPT with Hybrid CS
WHEN COMPRESSIVE RATIO IS 5
if no. of measurements =1/5 (no. of nodes )
Reduction Ratio Method 1 Method 2
50 Clustering with hybrid CS
Clustering without CS
40 Clustering with hybrid CS
SPT without CS
20 Clustering with hybrid CS
SPT with Hybrid CS
Results• Our method of using CS with clustering in WSN
can significantly reduce data transmissions compared with conventional data collection methods of Clustering without CS,SPT without CS,SPT with CS.
• Using clustering with hybrid CS minimize data transmissions and help maximize lifetime of network with the resource constrained sensor nodes.
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