Crowd-Driven IoT/IoE Ecosystems: A Multidimensional Approach

  • Xenia ZiouvelouEmail author
  • Panagiotis Alexandrou
  • Constantinos Marios Angelopoulos
  • Orestis Evangelatos
  • Joao Fernandes
  • Nikos Loumis
  • Frank McGroarty
  • Sotiris Nikoletseas
  • Aleksandra Rankov
  • Theofanis Raptis
  • Anna Ståhlbröst
  • Sebastien Ziegler
Part of the Internet of Things book series (ITTCC)


During the past few years an astonishing paradigm shift has occurred towards a new participatory value creation model driven by users. Open collaborative innovation practices have emerged in which an increasing number of users mutually collaborate by openly communicating their ideas, sharing best practices, and creating new knowledge across sectors. These online, distributed, crowd-driven networks take advantage of underlying network effects in order to harness the collective power and intelligence of the Crowd. Such novel paradigms fuel an increasing interest in mobile crowdsensing (MCS) methods in the context of IoT/IoE, which leverage the power and the wisdom of the crowd to observe, measure, and make sense of particular phenomena by exploiting user-owned mobile and wearable devices. However, when one examines the design and development of such ecosystems, realises that there is a gap in existing research. While emphasis has been placed upon the technical aspects, the success of such ecosystems is dependent on a number of diverse criteria. This chapter aims to fill this gap by providing a framework, which adopts a holistic approach based on multiple perspectives (namely technical, business, and people perspectives) and facilitates the design and development of crowd-driven ecosystems. This model is examined in the context of a hybrid crowd-driven IoT/IoE ecosystem, IoT Lab, in order to exemplify how these perspectives can be used to promote an ecosystem’s success and detail the challenges faced. This analysis is extended through the introduction of the “Crowd-driven Ecosystem Index (CEI)”, which measures the coverage intensity of each of the key ecosystem parameters, denoting this way the propensity of success of a crowd-driven network.


Wearable Device Business Perspective Innovation Ecosystem Business Ecosystem Personal Data Protection 
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.



The research reported in this paper has been supported by the EU/FIRE IoT Lab project—STREP ICT-610477.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xenia Ziouvelou
    • 1
    Email author
  • Panagiotis Alexandrou
    • 2
  • Constantinos Marios Angelopoulos
    • 3
  • Orestis Evangelatos
    • 3
  • Joao Fernandes
    • 4
  • Nikos Loumis
    • 5
  • Frank McGroarty
    • 1
  • Sotiris Nikoletseas
    • 2
  • Aleksandra Rankov
    • 6
  • Theofanis Raptis
    • 2
  • Anna Ståhlbröst
    • 7
  • Sebastien Ziegler
    • 8
  1. 1.University of SouthamptonSouthamptonUK
  2. 2.Computer Technology Institute & Press DiophantusPatrasGreece
  3. 3.Université de GenèveGenevaSwitzerland
  4. 4.Alexandra Instituttet A/SAarhusDenmark
  5. 5.University of SurreyGuildfordUK
  6. 6.DunavNETNovi SadSerbia
  7. 7.Luleå University of TechnologyLuleåSweden
  8. 8.Mandat InternationalGenevaSwitzerland

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