Learning Based Routing Link Scheduling in Heterogeneous Wireless IoT Networks

    •  Wang, Z., Guo, J., Parsons, K., Nagai, Y., Sumi, T., Orlik, P.V., "Learning Based Routing Link Scheduling in Heterogeneous Wireless IoT Networks", IEEE International Conference on Communications (ICC), June 2024.
      BibTeX TR2024-069 PDF
      • @inproceedings{Wang2024jun3,
      • author = {Wang, Zhiyang and Guo, Jianlin and Parsons, Kieran and Nagai, Yukimasa and Sumi, Takenori and Orlik, Philip V.}},
      • title = {Learning Based Routing Link Scheduling in Heterogeneous Wireless IoT Networks},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2024,
      • month = jun,
      • url = {}
      • }
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  • Research Areas:

    Communications, Signal Processing


With the advent of 5G and beyond communication technologies, the consumer Internet of Things (IoT) devices are evolving from the current-generation to the next-generation. Next-generation IoT devices can support multiple communication interfaces and perform more functions. Accordingly, IoT network technologies must adapt to the emerging next- generation IoT devices. Routing is an inevitable technology in multi-hop IoT networks. However, as IoT devices become more and more diverse, IoT networks become more complex. As a result, the routing problem becomes more and more complicated for traditional protocols and mathematical optimization approaches to provide optimal solutions. Machine learning based routing techniques have been recently proposed and can outperform traditional routing methods in complex network environments. To that end, this paper presents a machine learning based routing link scheduling scheme for heterogeneous wireless IoT networks. We formulate the routing link scheduling problem as a combinatorial optimization problem, which is then parameterized for application of machine learning algorithm and the parameterized problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is proposed to update the primal- dual problems. We evaluate the proposed HetGNN model under networks with randomly deployed heterogeneous nodes. Compared with a convolutional neural network (CNN) model and a homogeneous GNN (HomGNN) model, the proposed HetGNN model can improve network throughput, reduce link capacity violation and interference link violation.