Service Discovery in Social Internet of Things using Graph Neural Networks

• URL: http://arxiv.org/abs/2205.12711v1
• Date: Wed, 25 May 2022 12:25:37 GMT
• Title: Service Discovery in Social Internet of Things using Graph Neural Networks
• Authors: Aymen Hamrouni, Hakim Ghazzai, and Yehia Massoud
• Abstract summary: 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. We propose a scalable resource allocation neural model adequate for heterogeneous large-scale IoT networks.
• Score: 1.552282932199974
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Survey of Graph Neural Network for Internet of Things and NextG Networks [3.591122855617648]
  Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights. This survey provides a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems.
  arXiv  Detail & Related papers  (2024-05-27T16:10:49Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss [0.0]
  We introduce a simple, efficient, and clustering-oriented model based on unsupervised textbfGraph Attention textbfAutotextbfEncoder for community detection in attributed networks. The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives: reconstruction and community discovery.
  arXiv  Detail & Related papers  (2023-11-21T20:45:55Z)
• Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services [24.10349383347469]
  This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment. It enables IoT devices to not only contribute but also make informed decisions in network management. This setup ensures efficient data handling, advanced privacy and security measures, and responsive to fluctuating network conditions.
  arXiv  Detail & Related papers  (2023-05-09T14:03:22Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection [3.3659034873495632]
  Internet of Things (IoT) devices are constructed to build a huge communications network. These devices are insecure in reality, it means that the communications network are exposed by the attacker. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection.
  arXiv  Detail & Related papers  (2021-04-05T08:51:24Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.