Frequent Itemset Mining-Based Spatial Subclustering Algorithm

doi:10.13190/j.jbupt.2015.s1.005

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (s1): 20-23.doi: 10.13190/j.jbupt.2015.s1.005

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Frequent Itemset Mining-Based Spatial Subclustering Algorithm

WANG Qian¹, GAO Zhi-peng¹, QIU Xue-song¹, WANG Xing-bin²

1. State Key Laboratory of Network and Switching Technology, Beijing Universityof Posts and Telecommunications, Beijing 100086, China;
2. Military Representative Office in Chengdu of Information Department of PLA General Staff, Sichuan Chengdu 610041, China

Received:2014-07-20 Online:2015-06-28 Published:2015-06-28

Abstract

Abstract:

The cluster-based periodic data collection for wireless sensor networks, the sensor nodes deployed in monitoring region periodic transmit data to sink nodes were considered. The spatial correlation in collected data increases the redundancy of data and network energy consumption. An algorithm was proposed to prolong the lifetime of wireless sensor network and ensure the fidelity of collected data. A frequent itemset mining-based spatial subclustering algorithm was proposed. Analysis and experiment show that the improved algorithm can achieve more energy savings, extend the wireless sensor networks lifetime and guarantee the fidelity of collected data.

Key words: wireless sensor networks, data collection, frequent itemset mining, spatial correlation

CLC Number:

TP393

WANG Qian, GAO Zhi-peng, QIU Xue-song, WANG Xing-bin. Frequent Itemset Mining-Based Spatial Subclustering Algorithm[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(s1): 20-23.

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Frequent Itemset Mining-Based Spatial Subclustering Algorithm

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