Service Discovery in Social Internet of Things using Graph Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering IoT devices existing in the network and requesting corresponding services from them. As the highly dynamic nature of the IoT environment hinders the use of traditional solutions of service discovery, we aim, in this paper, to address this issue by proposing a scalable resource allocation neural model adequate for heterogeneous large-scale IoT networks. We devise a Graph Neural Network (GNN) approach that utilizes the social relationships formed between the devices in the IoT network to reduce the search space of any entity lookup and acquire a service from another device in the network. This proposed approach surpasses standardization issues and embeds the structure and characteristics of the social IoT graph, by the means of GNNs, for eventual clustering analysis process. Simulation results applied on a real-world dataset illustrate the performance of this solution and its significant efficiency to operate on large-scale IoT networks.

Original languageEnglish (US)
Title of host publicationMWSCAS 2022 - 65th IEEE International Midwest Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665402798
DOIs
StatePublished - 2022
Event65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022 - Fukuoka, Japan
Duration: Aug 7 2022Aug 10 2022

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2022-August
ISSN (Print)1548-3746

Conference

Conference65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022
Country/TerritoryJapan
CityFukuoka
Period08/7/2208/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • graph neural network
  • resource allocation
  • service discovery
  • smart city
  • social internet of things

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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