Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86818
Title: Parameterized Spatio-Textual Publish/Subscribe in Road Sensor Networks
Authors: Li, Yanhong
Huang, Ziqing
Zhu, Rongbo
Li, Guohui
Shu, Lihchyun
Tian, Shasha
Ma, Maode
Keywords: Spatio-temporal Database
Publish/Subscribe
Issue Date: 2017
Source: Li, Y., Huang, Z., Zhu, R., Li, G., Shu, L., Tian, S., et al. (2017). Parameterized Spatio-Textual Publish/Subscribe in Road Sensor Networks. IEEE Access, 5, 22940-22952.
Series/Report no.: IEEE Access
Abstract: Huge amounts of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in road sensor networks. Publish/subscribe system is one kind of important applications for analyzing and processing these huge mounts of data in road sensor networks, which is required to support millions of subscriptions and filter a message in milliseconds. Since the messages arrive continuously at a high speed, rapid processing of the messages is definitely a challenge. This paper mainly addresses the issue of parameterized spatio-textual publish/subscribe problem in road sensor networks. First, with considering both the network distance and textual similarity of the subscriptions and messages, the road network structure, together with the subscriptions and the messages will be partitioned and organized efficiently, and a combined index structure, called basic indexing architecture, is proposed. Second, several effective pruning techniques which consider both location information and textual information are presented to cut down the processing overhead. Moreover, by employing these pruning techniques into the basic indexing architecture, an more efficient index, called enhanced indexing architecture, is presented. Third, an efficient processing algorithm is designed to improve the scalability. Finally, extensive simulations are conducted to show the efficiency and scalability of the proposed methods in road sensor networks.
URI: https://hdl.handle.net/10356/86818
http://hdl.handle.net/10220/44260
DOI: 10.1109/ACCESS.2017.2765502
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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