Two-phase PT-Topk Query Processing Algorithm for Uncertain IOT Data in Dam Safety Monitoring. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Mao, Yingchi; Zhong, Haishi; Chen, Hao; Li, Xiaofang
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 4(2017)
• Page Start: 581
• Page End: 588
• Publication Date: 2017-10-02
• Subjects: PT-Topk queries -- x-tuple rule -- Uncertain IOT data -- Hierarchical sensor networks; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2017.1316070
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: Uncertain data has become ubiquitous due to the development of Internet of Things (IOT) for collecting data in an imprecise way, such as in the dam safety monitoring applications. Efficient Top-k processing of uncertain data is an important requirement in the field of dam safety monitoring. In order to reduce energy consumption and query response time in the applications of IOTs, an uncertain data PT-Top k query processing scheme was studied in a hierarchical structural sensor network. Based on the x -tuple Rule of uncertain data, adopting intra-cluster and inter-cluster two phases query processing, a distributed Two-Phase PT-Top k Query Processing approximation algorithm (TPQP) was proposed. In the intra-cluster phase and inter-cluster phase, the local and global pruning upper bounds can be computed respectively. The data ranked lower than the two bounds cannot be forwarded to the sink node. Therefore, the proposed TPQP algorithm can reduce the transmission cost and shorten the query response time. The extensive experiment results demonstrate that TPQP can significantly reduce the transmission cost against the centralized algorithm by 87.51%, and shorten the query response time by 6%-31% and 35%-54% compared to BB and SSB, respectively. Meanwhile, TPQP can obtain the error rate below 5.5% in the different probability p and ranking number k .