The problem we are trying to solve is to design energy-efficient and high data quality node clustering protocols that utilize both system data and application-specific sensor data, and are applicable to a wide range of wireless sensor network applications. In this work, we develop data-centric node clustering protocols by techniques of data mining. The analysis and comparison of both energy-centric and data-centric node clustering protocols on real-world datasets show that by incorporating application-specific sensor data into node clustering is effective in both prolonging network lifetime and assuring data quality.Having probability of connectivity equal to or greater than 0.9 means a stable connection. On average, there are 3.6 one-hop neighbors per node. Therefore, the average degree of a node is 3.6. This average degree value might not beanbsp;...
|Title||:||Node Clustering by Data Mining for Wireless Sensor Networks|
|Publisher||:||ProQuest - 2008|