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Indexed by:期刊论文

Date of Publication:2014-06-01

Journal:INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS

Included Journals:SCIE、EI、Scopus

Volume:27

Issue:6,SI

Page Number:871-897

ISSN No.:1074-5351

Key Words:search engines; index methods; sensor networks; spatial-temporal databases; internet of things; information retrieval for big data

Abstract:Recent advances on the Internet of Things (IoT) have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real-world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real-time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial-temporal searches and value-based approximate searches, as IoT sampling data are generally from spatial-temporal scenario. To meet these challenges, we propose a Hybrid Real-time Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions' (IoT-SVK Search Engine' or simply IoT-SVKSearch' for short) in this paper. The experiments show that the IoT-SVK search engine has satisfactory performances in supporting real-time, multi-modal retrieval of massive sensor sampling data in the IoT. Copyright (c) 2013 John Wiley & Sons, Ltd.