Multi-Feature Sensor Similarity Search for the Internet of Things

Suyan LIU
Yuanan LIU
Fan WU
Puning ZHANG

IEICE TRANSACTIONS on Communications   Vol.E101-B    No.6    pp.1388-1397
Publication Date: 2018/06/01
Publicized: 2017/12/08
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3221
Type of Manuscript: PAPER
Category: Network
Internet of things,  classification,  multi-feature,  sensor similarity search,  

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The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.