Service Discovery in Social Internet of Things using Graph Neural Networks

TL;DR

This paper introduces a scalable Graph Neural Network-based method for service discovery in large-scale, dynamic Social Internet of Things networks, leveraging social relationships to improve efficiency and accuracy.

Contribution

It proposes a novel GNN approach that utilizes social relationships among IoT devices for efficient service discovery in large, heterogeneous networks.

Findings

Outperforms traditional service discovery methods in large-scale IoT environments.

Effectively leverages social relationships to reduce search space.

Demonstrates high efficiency and scalability on real-world datasets.

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 resource allocation 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.