Correlation management and search for the Internet of Things
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Date
2016
Authors
Shemshadi, Ali
Editors
Advisors
Sheng, Michael
Michalewicz, Zbigniew
Shen, Hong
Michalewicz, Zbigniew
Shen, Hong
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Abstract
The Internet of Things (IoT) is a compelling paradigm, which aims to enable everyday physical things embedded with electronics, software, sensors, and network connectivity to collect and exchange data on the Internet. It is anticipated that by 2020, billions of things get connected to the Internet. Creating future IoT search engines is a key step towards unlocking answering the above question. Future search engines can potentially in revolutionise various applications in different domains. Existing approaches for searching the IoT use simple techniques to obtain a list of things for a query. The state of the art needs to be improved in different aspects. For instance, it is often disregarded that in the context of IoT, we have two types of users including machines and human users. In addition, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is “Does the IoT already exist?”. So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data and user interests. Moreover, existing approaches also disregard the attribute based correlations between things in the real-world. In this dissertation, we review the state of the art in IoT search domain and propose a novel framework to collect and analyse IoT data. Our system is also able to resolve IoT queries based on the knowledge that is acquired from the IoT data sources. Furthermore, we introduce a novel technique to extract the correlations between things. Our framework is capable of using the correlations to improve the quality of search results for both types of users. We investigate the scalability and the effectiveness of our approach using large scale and real-world datasets. Moreover, we investigate two case studies in transport systems in our research. The first case study, challenges the complex problem of taxi ridesharing in the context of smart cities. The second case study, involves a real-time prediction method for flight delays based on the IoT sourced data.
School/Discipline
School of Computer Science
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2016.
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This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals