论文标题
使用图神经网络在社交互联网中发现的服务发现
Service Discovery in Social Internet of Things using Graph Neural Networks
论文作者
论文摘要
THIONT-things（IoT）网络智能地连接了数千个物理实体，为社区提供各种服务。它目睹了指数扩展，这使发现网络中存在的IoT设备并请求相应的服务的过程变得复杂。随着物联网环境的高度动态性质阻碍了传统的服务发现解决方案的使用，我们在本文中，通过提出一个可扩展的资源分配神经模型来解决此问题，足以适合异构的大型IoT网络。我们设计了一种图形神经网络（GNN）方法，该方法利用IoT网络中设备之间形成的社会关系来减少任何实体查找的搜索空间，并从网络中的另一个设备中获取服务。这种提出的资源分配方法超过了标准化问题，并通过GNNS的方式嵌入了社会物联网图的结构和特征，以进行最终的聚类分析过程。对现实世界数据集的仿真结果说明了该解决方案的性能及其在大规模IoT网络上运行的显着效率。
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.
