Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76470
Title: A distributed relation detection approach in the internet of things
Authors: Zhu, WP
Lu, HL 
Cui, XH
Cao, JN 
Issue Date: 2017
Publisher: Hindawi Publishing Corporation
Source: Mobile information systems, 2017, 4789814 How to cite?
Journal: Mobile information systems 
Abstract: In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the Distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed treebuilding process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption.
URI: http://hdl.handle.net/10397/76470
ISSN: 1574-017X
EISSN: 1875-905X
DOI: 10.1155/2017/4789814
Rights: Copyright © 2017 Weiping Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Weiping Zhu, Hongliang Lu, Xiaohui Cui, and Jiannong Cao, “A Distributed Relation Detection Approach in the Internet of Things,” Mobile Information Systems, vol. 2017, Article ID 4789814, 14 pages, 2017 is available at https://doi.org/10.1155/2017/4789814.
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