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.
|Title of host publication
|MWSCAS 2022 - 65th IEEE International Midwest Symposium on Circuits and Systems, Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Published - 2022
|65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022 - Fukuoka, Japan
Duration: Aug 7 2022 → Aug 10 2022
|Midwest Symposium on Circuits and Systems
|65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022
|08/7/22 → 08/10/22
Bibliographical notePublisher Copyright:
© 2022 IEEE.
- 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