Ternary Hierarchical Clustering with Data De-Duplication in Sensor Networks
Journal Title: International Journal on Computer Science and Engineering - Year 2013, Vol 5, Issue 7
Abstract
Wireless Sensor Networks are resource constrained application specific adhoc networks. All the previous works have concentrated on the power aware techniques such as data aggregation, coordination of node operations for effective routes and query processing etc. All these techniques have helped to collect and communicate the field data of numerous applications at the cost of degrading the quality of the data. This tradeoff between resource consumption and the quality of information flow will in turn decrease the utility of sensed data. In this paper, we proposed a new approach for gathering and communicating the sensed data with the help of clustering and De-Duplication technique. We have explained the significance of de-duplication technique by considering the multi-media data transmission in wireless sensor networks.
Authors and Affiliations
Sreeram Indraneel , Kurra Raja Sekhara Rao
Design and Implementation of Neural Processor for Parsing Manufacturing Query Language
Practically, all the approaches employed for parsing with natural languages use some or other type of neural network architecture and some typical statistical function for obtaining a parsing decision. In parsing with ne...
Information and Communication Technology (ICT) Revolution: Its Environmental Impact and Sustainable Development
Our world’s resources, and even the planet itself, are rapidly decaying faster than we can imagine. While many people debate the causes the effects are obvious to all: climate change, problem in the animal world, health...
Quantitative evaluation of Segmentation algorithms based on level set method for ISL datasets
The enormous potential research efforts have been taken for sophisticated and natural human computer interaction using gestures. This work has got motivated from long ago as 1980’s since sign language is the only communi...
Unsupervised Hybrid Classification for Texture Analysis Using Fixed and Optimal Window Size
For achieving better classification results in texture analysis, it is to combine different classification methods. Though there are existing methods which have been using fixed window size that resulted lack of classifi...
A New Method for Finding an Optimal Solution for Transportation Problems
In this paper a new method named ASM-Method is proposed for finding an optimal solution for a wide range of transportation problems, directly. A numerical illustration is established and the optimality of the result yiel...