Wednesday, August 13, 2014

Data Density Correlation Degree Clustering Method for Data Aggregation in WSN

Data Density Correlation Degree Clustering Method for Data Aggregation in WSN

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.

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.

·        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.

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.

·        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.



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.


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)

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.