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Integration of FIWARE and IoT based Named Data Networking (IoT-NDN).

Abstract

IoT systems have taken on an essential role in our life. IoT devices are strongly integrated into several sectors such as Smart Healthcare, Smart Cities, Smart Energy, Smart Industry, etc. and deliver important data. Designing, building, and implementing IoT systems are significant challenges because of IoT requirements such as mobility, energy consumption, and limited device memory. To mitigate such challenges, opportunities to test and evaluate IoT systems early in the first development phases are important to reduce cost and effort. Different systems have been proposed to aid such development, aiming at different key challenges. One of these systems is FIWARE, an open source IoT middleware, designed to ease data transportation and big data tasks. It has been established as an ecosystem technology used for optimizing the development of several applications and services in IoT. Key feature is the standardized architecture for gathering context information and managing these contexts in cloud based IoT and big data applications. In this paper, we discuss the integration of FIWARE software and IoT-NDN. IoT-NDN is an IoT system based on the Named Data Networking (NDN) communication paradigm. NDN is a communication protocol developed for the Internet and uses hierarchical names instead of IP addresses to deliver data on the Internet. IoT-NDN is an advanced architecture of NDN, conceding the requirements and limitations of IoT devices. In this paper we present an approach and architecture to integrate FIWARE and IoT-NDN. This integration eases implementation of IoT-NDN in existing applications, since a transparent compatibility between both systems can be achieved.
OriginalspracheEnglisch
TitelProceedings of the 10th International Conference on Sensor Networks (IPSN)
Seitenumfang7
Erscheinungsdatum14.02.2022
Seiten184-190
DOIs
PublikationsstatusVeröffentlicht - 14.02.2022

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Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

DFG-Fachsystematik

  • 4.43-03 Sicherheit und Verlässlichkeit, Betriebs-, Kommunikations- und verteilte Systeme

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