Service Discovery in Social Internet of Things using Graph Neural Networks
This addresses the challenge of scalable service discovery for IoT networks, which is incremental as it applies GNNs to a specific domain problem.
The paper tackles the problem of service discovery in large-scale, dynamic IoT networks by proposing a Graph Neural Network (GNN) approach that leverages social relationships between devices to reduce search space and improve resource allocation, with simulation results on a real-world dataset demonstrating significant efficiency for large-scale operations.
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.