A pattern query strategy based on semi-supervised machine learning in distributed WSNs

作者：Li Fengqi^*; Zhang Xuechao; Qiu Tie; Yang Nanhai

来源：Journal of Information and Computational Science, 2014, 11(18): 6447-6459.

DOI：10.12733/jics20105009

• Classification accuracy
• Distributed wireless sensor networks
• Global consistency
• High energy consumption
• Label propagation
• Pattern query
• Time-series data
• Training dataset

Hardware of Distributed WSNs is limited, which turns into a big challenge. In recent years, there are some pattern-based query strategies have been proposed. But compared to the methods based on machine learning, these methods need high energy consumption in the calculation of distance. In this paper, we propose a pattern-based query strategy based on semi-supervised machine learning in wireless sensor network. The strategy uses historical time series data streams for offline machine learning. First of all, we find out the fixed length subsequences and screen out the suitable data sequences as training dataset and sample dataset. And then, because labeled data is less in practical application, we do label propagation with graph-based semi-supervised machine learning algorithm, Local and Global Consistency (LGC), to increase the number of labeled data sequences. Next, we use the supervised learning algorithm Support Vector Machine (SVM) to train a classifier. Finally, for a new data sequence, we can directly get its category through the classifier. Experimental results show that the performance of our strategy is almost better than other pattern-based query strategies on classification accuracy and energy consumption.

• 出版日期2014