Data Density Correlation Degree Clustering Method for
Data Aggregation in WSN
ABSTRACT:
One data
aggregation method in a wireless sensor network (WSN) is sending local
representative data to the sink node based on the spatial-correlation of
sampled data. In this paper, we highlight the problem that the recent spatial
correlation models of sensor nodes’ data are not appropriate for measuring the
correlation in a complex environment. In addition, the representative data are
inaccurate when compared with real data. Thus, we propose the data density
correlation degree, which is necessary to resolve this problem. The proposed
correlation degree is a spatial correlation measurement that measures the
correlation between a sensor node’s data and its neighboring sensor nodes’
data. Based on this correlation degree, a data density correlation degree (DDCD)
clustering method is presented in detail so that the representative data have a
low distortion on their correlated data in a WSN. In addition, simulation
experiments with two real data sets are presented to evaluate the performance
of the DDCD clustering method. The experimental results show that the resulting
representative data achieved using the proposed method have a lower data
distortion than those achieved using the Pearson correlation coefficient based
clustering method or the α-local spatial clustering method. Moreover, the shape
of clusters obtained by DDCD clustering method can be adapted to the
environment.
EXISTING SYSTEM:
In the study of
data aggregation methods, the spatial- correlation model between sensor nodes’
data is an important foundation that relates to the accuracy of the aggregated
data and the energy consumption of sensor nodes. Some researchers have
systemically discussed spatial-correlation models based on geographic locations
of sensor nodes or statistic features of sensor nodes’ data. The assumption of
spatial-correlation models based on sensor nodes’ locations is that the close
sensor nodes are more correlated than the distant ones. Thereby, the spatial
correlation degree function is modeled to be nonnegative and decrease monotonically
with the distance between sensor nodes.
DISADVANTAGES
OF EXISTING SYSTEM:
·
much
rude data should be sent to the sink node, and these models have high
computational complexity.
·
the
sensor nodes are usually deployed in some harsh environment, with the sensing
distortion of sensor nodes, noise between sensor nodes, located terrain of
sensor nodes and communication condition uncertain in practice. The neighboring
sensor nodes may be uncorrelated.
·
the
spatial location of sensor node is not accurate in general, making it hard to
accurately model the spatial correlation of sensor nodes based on the locations
of sensor nodes.
PROPOSED SYSTEM:
In our work, a
data density correlation degree (DDCD) was proposed to measure the spatial
correlation of sampled data and try to resolve the drawbacks in existing
spatial correlation models. And the DDCD clustering method was also presented
in the WSN. With the DDCD clustering method, sensor nodes which are in the same
cluster have a high correlation degree, while those belonging to different
clusters have a low correlation degree. Furthermore, the time complexity of the
DDCD clustering algorithm is O(n). The message complexity is O(Kn). Where the K
is the maximum degree of the sensor network topology graph.
ADVANTAGES
OF PROPOSED SYSTEM:
·
Achieves
performance superior to existing protocols in terms of energy efficiency,
packet delivery ratio (PDR), and latency.
·
The
Rainbow mechanism allow guarantee packet delivery in realistic deployment.
·
simulation
results also show better performance than that of two recent proposals for
routing around dead ends.
SYSTEM
ARCHITECTURE:
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7/LINUX.
Ø Implementation : NS2
Ø NS2 Version : NS2.2.28
Ø Front
End : OTCL (Object Oriented
Tool Command Language)
Ø Tool : Cygwin (To simulate in Windows OS)
REFERENCE:
Fei Yuan, Yiju
Zhan, and Yonghua Wang, “Data Density Correlation Degree Clustering Method for Data
Aggregation in WSN”, IIEEE SENSORS JOURNAL, VOL. 14, NO. 4, APRIL 2014.
