Efficient clustering-based data aggregation techniques for wireless sensor networks

In wireless sensor network applications for surveillance and reconnaissance, large amounts of redundant sensing data are frequently generated. It is important to control these data with efficient data aggregation techniques to reduce energy consumption in the network. Several clustering methods were utilized in previous works to aggregate large amounts of data produced from sensors in target tracking applications (Park in A dissertation for Doctoral in North Carolina State University, 2006). However, such data aggregation algorithms show effectiveness only in restricted environments, while posing great problems when adapting to other various situations. To alleviate these problems, we propose two hybrid clustering based data aggregation mechanisms. The combined clustering-based data aggregation mechanism can apply multiple clustering techniques simultaneously in a single network depending on the network environment. The adaptive clustering-based data aggregation mechanism can adaptively choose a suitable clustering technique, depending on the status of the network. The proposed mechanisms can increase the data aggregation efficiency as well as improve energy efficiency and other important issues compared to previous works. Performance evaluation via mathematical analysis and simulation has been made to show the effectiveness of the proposed mechanisms.