关键词: 频繁模式, 模式增长, 字典树, 关联规则, 传感器节点

Abstract: Wireless sensor networks with high node density and wide node distribution, longterm monitoring produce a huge amount of data, so how to process the large data streams in sensor networks efficiently and find interesting knowledge in these streams become a new challenge．This paper proposes a novel node association rule mining algorithm PLTSTREAM for exploiting the inherent correlations between sensor readings．This algorithm can help users to manage data efficiently during aggregation, classification, prediction, query, understanding and decisionmaking．The experimental results show that the proposed method can reduce the overhead of computation and communication energy in the information processing procedure effectively. Our algorithm can also shorten the data query response time and thus prolong the network lifetime.

Key words: frequent pattern；pattern growth；lexicographic tree；association rules；sensor node