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Context-Aware Dynamic Discovery and Configuration of ‘Things’ in Smart Environments

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Big Data and Internet of Things: A Roadmap for Smart Environments

Part of the book series: Studies in Computational Intelligence ((SCI,volume 546))

Abstract

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or ‘things’ outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5–10 years. To be able to develop IoT applications, such ‘things’ must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of ‘things’ is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of Things (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.

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Notes

  1. 1.

    We use both terms, ‘objects’ and ‘things’ interchangeably to give the same meaning as they are frequently used in IoT related documentation. Some other terms used by the research community are ‘smart objects’, ‘devices’, ‘nodes’. Each ‘thing’ may have one or more sensors attached to it.

  2. 2.

    It is important to note that the same object can be classified at different levels depending on the context. Further, there is no clear definition to classify objects into different levels of dynamicity. However, our categorization allows us to understand the differences in dynamicity.

  3. 3.

    http://docs.oasis-open.org/ws-dd/ns/dpws/2009/01

  4. 4.

    This is an extended version of an SSN ontology (www.w3.org/2005/Incubator/ssn/ssnx/ssn). The detailed description of our extended ontology is out of the scope of this chapter.

  5. 5.

    In practice, the IoT middleware sends a request to the application store (e.g. Google Play). The application store pushes the plugin to the SmartLink autonomously via the Internet.

  6. 6.

    In small agricultural fields, farmers themselves can carry the SmartLink over the field.

  7. 7.

    http://www.libelium.com/uploads/2013/02/data_frame_guide.pdf

  8. 8.

    Location can be represented in many ways: GPS coordinate (e.g. \(-35.280325\), 149.113166), name of a building (e.g. CSIT building at ANU), name of a city (e.g. Canberra), part of a building (e.g. living room), floor of a building (e.g. 2nd floor), specific part of a room (e.g. kitchen-top).

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Acknowledgments

Authors acknowledge support from SSN TCP, CSIRO, Australia and ICT Project, which is co-funded by the European Commission under seventh framework program, contract number FP7-ICT-2011-7-287305-OpenIoT. The Author(s) also acknowledge help and contributions from The Australian National University.

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Authors and Affiliations

  1. CSIRO Computational Informatics, Canberra, ACT, 0200, Australia

    Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky & Dimitrios Georgakopoulos

  2. Research School of Computer Science, The Australian National University, Canberra, ACT, 0200, Australia

    Charith Perera & Peter Christen

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  1. Charith Perera
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Correspondence to Charith Perera .

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Editors and Affiliations

  1. School of Computer Science and Mathematics, University of Derby, Derby, United Kingdom

    Nik Bessis

  2. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Ciprian Dobre

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Perera, C., Jayaraman, P.P., Zaslavsky, A., Christen, P., Georgakopoulos, D. (2014). Context-Aware Dynamic Discovery and Configuration of ‘Things’ in Smart Environments. In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_9

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