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Benchmarking Internet of Things Deployment: Frameworks, Best Practices, and Experiences

  • Franck Le GallEmail author
  • Sophie Vallet Chevillard
  • Alex Gluhak
  • Nils Walravens
  • Zhang Xueli
  • Hend Ben Hadji
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 4)

Abstract

IoT deployments generate data of the real world in an automated fashion without direct user involvement. With increasing scale of these IoT deployments the extraction of the right knowledge about the real world from a vast amount of IoT data and efficient decisions is a challenging endeavor. While solutions to deal with large amounts of IoT data are slowly emerging, potential users of IoT solutions, or policy makers find it difficult to assess the actual usefulness of investing in IoT deployments or selecting adequate deployment strategies for a particular business domain.

Despite the recent hype generated by consultancy companies and IoT vendors about the IoT, there is still a lack of experience in assessing the utility and benefits of IoT deployments as many IoT deployments are still in their early stages. In order to capture such experience quicker and derive best practices for IoT deployments, systematic tools, or methodologies are required in order to allow the assessment of the goodness or usefulness of IoT deployments and a comparison between emerging IoT deployments to be performed.

This chapter addresses the existing gap and proposes a novel benchmarking framework for IoT deployments. The proposed framework is complementary to the emerging tools for the analysis of big data as it allows various stakeholders to develop a deeper understanding of the surrounding IoT business ecosystem in a respective problem domain and the value proposition that the deployment of an IoT infrastructure may bring. It also allows a better decision making for policy makers for regulatory frameworks.

Keywords

Business Model Smart Grid Smart City Business Ecosystem Smart Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Franck Le Gall
    • 1
    Email author
  • Sophie Vallet Chevillard
    • 2
  • Alex Gluhak
    • 3
  • Nils Walravens
    • 4
  • Zhang Xueli
    • 5
  • Hend Ben Hadji
    • 6
  1. 1.Easy Global Market, Business PôleSophia Antipolis CedexFrance
  2. 2.Inno TSDSophia-AntipolisFrance
  3. 3.University of SurreyGuildfordUnited Kingdom
  4. 4.iMinds-SMIT, Vrije Universiteit BrusselVrije Universiteit BrusselElseneBelgium
  5. 5.Chinese Academy of Telecom ResearchBeijingChina
  6. 6.Centre d’Etudes et des Recherches des TélécommunicationsParc TechnologiqueEL GHAZALA ARIANATunisia

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