Service Oriented Middleware for the Internet of Things: A Perspective

(Invited Paper)
  • Thiago Teixeira
  • Sara Hachem
  • Valérie Issarny
  • Nikolaos Georgantas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6994)

Abstract

The Internet of Things plays a central role in the foreseen shift of the Internet to the Future Internet, as it incarnates the drastic expansion of the Internet network with non-classical ICT devices. It will further be a major source of evolution of usage, due to the penetration in the user’s life. As such, we envision that the Internet of Things will cooperate with the Internet of Services to provide users with services that are aware of their surrounding environment. The supporting service-oriented middleware shall then abstract the functionalities of Things as services as well as provide the needed interoperability and flexibility, through a loose coupling of components and composition of services. Still, core functionalities of the middleware, namely service discovery and composition, need to be revisited to meet the challenges posed by the Internet of Things. Challenges in particular relate to the ultra large scale, heterogeneity and dynamics of the Internet of Things that are far beyond the ones of today’s Internet of Services. In addition, new challenges also arise, pertaining to the physical-world aspect that is central to the IoT. In this paper, we survey the major challenges posed to service-oriented middleware towards sustaining a service-based Internet of Things, together with related state of the art. We then concentrate on the specific solutions that we are investigating within the INRIA ARLES project team as part of the CHOReOS European project, discussing new approaches to overcome the challenges particular to the Internet of Things.

Keywords

Service Composition Service Discovery Loose Coupling Discovery Module Pervasive 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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thiago Teixeira
    • 1
  • Sara Hachem
    • 1
  • Valérie Issarny
    • 1
  • Nikolaos Georgantas
    • 1
  1. 1.INRIA Paris-RocquencourtFrance

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