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Sustainability Data and Analytics in Cloud-Based M2M Systems

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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

Recently, cloud computing technologies have been employed for large-scale machine-to-machine (M2M) systems, as they could potentially offer better solutions for managing monitoring data of IoTs (Internet of Things) and supporting rich sets of IoT analytics applications for different stakeholders. However, there exist complex relationships between monitored objects, monitoring data, analytics features, and stakeholders that require us to develop efficient ways to handle these complex relationships to support different business and data analytics processes in large-scale M2M systems. In this chapter, we analyze potential stakeholders and their complex relationships to data and analytics applications in M2M systems for sustainability governance. Based on that we present techniques for supporting M2M data and process integration, including linking and managing monitored objects, sustainability monitoring data and analytics applications, for different stakeholders who are interested in dealing with large-scale monitoring data in M2M environments. We present a cloud-based data analytics system for sustainability governance that includes a Platform-as-a-Service and an analytics framework. We also illustrate our prototype based on a real-world cloud system for facility monitoring and analytics.

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Notes

  1. 1.

    http://www.tendrilinc.com

  2. 2.

    Detailed design and implementation of these conceptual models are out of the scope of this chapter.

  3. 3.

    http://www.niagaraax.com/

  4. 4.

    http://www.franz.com/agraph/allegrograph

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Acknowledgments

This chapter is an extended version of [2]. This work is partially funded by the Pacific Control Cloud Computing Lab (PC3L—pc3l.infosys.tuwien.ac.at). We thank Manu Ravishankar, Saneesh Kumar, Sulaiman Yousuf and Terry Casey for providing useful information and datasets of facilities. We thank our colleagues in PC3L for fruitful discussion on cloud platforms and analytics.

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Authors and Affiliations

  1. Distributed Systems Group, Vienna University of Technology, Vienna, Austria

    Hong-Linh Truong & Schahram Dustdar

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  1. Hong-Linh Truong
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  2. Schahram Dustdar
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Correspondence to Hong-Linh Truong .

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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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Truong, HL., Dustdar, S. (2014). Sustainability Data and Analytics in Cloud-Based M2M Systems. 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_14

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  • DOI: https://doi.org/10.1007/978-3-319-05029-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05028-7

  • Online ISBN: 978-3-319-05029-4

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