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Efficient edge-cloud publish/subscribe broker overlay networks to support latency-sensitive wide-scale iot applications

  • Van Nam Pham
  • , Van Dung Nguyen
  • , Tri D.T. Nguyen
  • , Eui Nam Huh

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Computing services for the Internet-of-Things (IoT) play a vital role for widespread IoT deployment. A hierarchy of Edge-Cloud publish/subscribe (pub/sub) broker overlay networks that support latency-sensitive IoT applications in a scalable manner is introduced. In addition, we design algorithms to cluster edge pub/sub brokers based on topic similarities and geolocations to enhance data dissemination among end-to-end IoT devices. The proposed model is designed to provide low delay data dissemination and effectively save network traffic among brokers. In the proposed model, IoT devices running pub/sub client applications periodically send collected data, organized as a hierarchy of topics, to their closest edge pub/sub brokers. Then, the data are processed/analyzed at edge nodes to make controlling decisions promptly replying to the IoT devices and/or aggregated for further delivery to other interested edge brokers or to cloud brokers for long-term processing, analysis, and storage. Extensive simulation results demonstrate that our proposal achieves the best data delivery latency compared to two baseline schemes, a classical Cloud-based pub/sub scheme and an Edge-Cloud pub/sub scheme. Considering the similar Edge-Cloud technique, the proposed scheme outperforms PubSubCoord-alike in terms of relay traffic ratio among brokers. Therefore, our proposal can adapt well to support wide-scale latency-sensitive IoT applications.

Original languageEnglish
Article number3
JournalSymmetry
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Bibliographical note

Publisher Copyright:
© 2019 by the authors.

Keywords

  • Broker-based publish/subscribe
  • Distributed pub/sub systems
  • Geolocation awareness
  • IoT applications
  • Latency-sensitive
  • Topic similarity

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