CEIoT

a framework for interlinking smart things in the internet of things

Ali Shemshadi*, Quan Z. Sheng, Yongrui Qin, Ali Alzubaidi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

In the emerging Internet of Things (IoT) environment, things are interconnected but not interlinked. Interlinking relevant things offers great opportunities to discover implicit relationships and enable potential interactions among things. To achieve this goal, implicit correlations between things need to be discovered. However, little work has been done on this important direction and the lack of correlation discovery has inevitably limited the power of interlinking things in IoT. With the rapidly growing number of things that are connected to the Internet, there are increasing needs for correlations formation and discovery so as to support interlinking relevant things together effectively. In this paper, we propose a novel approach based on Multi-Agent Systems (MAS) architecture to extract correlations between smart things. Our MAS system is able to identify correlations on demand due to the autonomous behaviors of object agents. Specifically, we introduce a novel open-sourced framework, namely CEIoT, to extract correlations in the context of IoT. Based on the attributes of things our IoT dataset, we identify three types of correlations in our system and propose a new approach to extract and represent the correlations between things.We implement our architecture using Java Agent Development Framework (JADE) and conduct experimental studies on both synthetic and real-world datasets. The results demonstrate that our approach can extract the correlations at a much higher speed than the naive pairwise computation method.

Original languageEnglish
Title of host publicationAdvanced data mining and applications
Subtitle of host publication12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12–15, 2016, proceedings
EditorsJinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages203-218
Number of pages16
ISBN (Electronic)9783319495866
ISBN (Print)9783319495859
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event12th International Conference on Advanced Data Mining and Applications, ADMA 2016 - Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume10086
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Advanced Data Mining and Applications, ADMA 2016
CountryAustralia
CityGold Coast
Period12/12/1615/12/16

Keywords

  • Correlation
  • Internet of things
  • Multi-Agent System

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