Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Aymen Hamrouni, Abdullah Khanfor, Hakim Ghazzai, Yehia Massoud*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices' features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

Original languageEnglish (US)
Pages (from-to)107330-107345
Number of pages16
JournalIEEE Access
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the King Abdullah University of Science and Technology (KAUST); and in part by the Ministry of Education, Saudi Arabia, and the Deanship of Scientific Research, Najran University, under Grant NU/RC/SERC/11/6.

Publisher Copyright:
© 2013 IEEE.


  • Community detection
  • graph neural networks
  • service discovery
  • social Internet of Things

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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