A Distributed Relation Detection Approach in the Internet of Things

Weiping Zhu, Hongliang Lu, Xiaohui Cui, Jiannong Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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 tree-building 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.

Original languageEnglish
Article number4789814
JournalMobile Information Systems
Volume2017
DOIs
Publication statusPublished - 1 Jan 2017

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

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