• DocumentCode
    1669637
  • Title

    A Decentralized Locality-Preserving Context-Aware Service Discovery Framework for Internet of Things

  • Author

    Juan Li ; Zaman, Nazia ; Honghui Li

  • Author_Institution
    Comput. Sci. Dept., North Dakota State Univ., Fargo, ND, USA
  • fYear
    2015
  • Firstpage
    317
  • Lastpage
    323
  • Abstract
    Today´s Internet is shifting towards a larger and smarter scenario known as the Internet of Things (IoT). The IoT envisions a multitude of heterogeneous objects and interactions with physical environments. In this environment, locating desirable services is challenging due to the considerable diversity, large number, dynamic behavior, and geographical distribution of the services provided by physical objects. In this paper, we propose a context-aware semantics-based service discovery mechanism - LOCA that can effectively locate services based on the context requirements. The proposed discovery framework is built on a fully distributed peer-to-peer (P2P) architecture which is scalable and robust. Moreover, a key feature of the discovery mechanism is its support of content and path locality. This feature can enhance the integrity of an organization and thus greatly improve the security of the organization. The effectiveness of the proposed framework is demonstrated through comprehensive simulation studies.
  • Keywords
    Internet; Internet of Things; peer-to-peer computing; software reliability; ubiquitous computing; Internet; Internet of Things; IoT; LOCA mechanism; P2P architecture; decentralized locality-preserving context-aware service discovery framework; fully distributed peer-to-peer architecture; geographical service distribution; heterogeneous objects; path locality; Cognition; Context; Context modeling; Ontologies; Peer-to-peer computing; Routing; Semantics; Internet of Things; context-aware; discovery; semantics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Services Computing (SCC), 2015 IEEE International Conference on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7280-0
  • Type

    conf

  • DOI
    10.1109/SCC.2015.51
  • Filename
    7207369