# A Graph Convolutional Network-Based Method for Congested Link Identification
###### Abstract  
Accurate and efficient congested link identification is crucial in wireless sensor networks (WSNs). However, in some networks with a centralized management architecture, it is often not feasible to monitor large numbers of internal links directly or even impossible in some heterogeneous networks. Network tomography, the science of inferring the performance characteristics of a network's interior by correlating sets of end-to-end measurements, was put forward to solve this problem. Nevertheless, a network always contains more links than end-to-end paths, making it problematic to find a determined solution. To solve this problem, most of the current methods try to use some additional prerequisites, such as the link congestion probability. However, most existing studies have not considered the congestion caused by node factors and the case of multiple congested links on one path. In this paper, we initially model the issue of link congestion as a Bayesian network model (BSM). Subsequently, we introduce a congestion link identification method based on graph convolutional networks (GCNs), novelly converting the intricate Bayesian network solving problem into a graph node classification task. The simulation results validate the feasibility of our proposed algorithm in identifying congested links and underscore its advantages in scenarios involving node congestion and multiple congested links. network tomography; graph convolutional network; wireless sensor networks; congested link identification 20242024149164 10.3390/2024/14/10/11/2  
the distributed WSN architecture and the rigid resource management model, flexible changes cannot be made quickly according to the requirements of the upstream application. Therefore, the current distributed WSN cannot fully meet the dynamic, on-demand sensing requirements for monitoring the physical world [7]. This also limits the rapid development of WSNs to some extent. [PERSON] et al. found that centralized management outperforms distributed management in WSNs, reflecting streamlined sensor node functions, efficient network management and diagnostics, and optimal network parameter configurations, which they argue better align with future WSN requirements [8]. At present, this view is widely accepted in the academic community. In WSNs, congestion can easily occur due to the bandwidth constraints between nodes and the many-to-one data transmission pattern. In the worst case, data retransmission due to congestion causes additional congestion, which increases the packet loss rate of the network, decreases the operational duration of the sensor nodes, and renders the entire network unavailable. Effective management of network congestion is crucial to prevent issues like packet loss, delay, load imbalance, reduced throughput, and energy inefficiency, thereby maintaining optimal network performance [9]. Accurate and efficient congested link identification is crucial in WSNs. However, it is a well-established fact that acquiring such states through conventional IP-based network monitoring tools, such as the Simple Network Management Protocol (SNMP) and the Internet Control Message Protocol (ICMP), generates a high monitoring overhead. Additionally, this is often impossible due to the large scale, heterogeneity, multiple ownership, and partial observability of networks. Therefore, it is essential to develop novel monitoring techniques which offer low traffic overhead and complexity while ensuring accurate measurements [10]. Network tomography is a technique for inferring the performance and state of a network by performing end-to-end measurements at the network boundaries [11; 12]. This technique primarily deals with additive network metrics commonly utilized in routing, such as link delay and packet loss. By selectively probing a limited set of paths from a small number of monitors positioned at the network's edge, network tomography constructs a linear system capturing the relationship between observed paths and corresponding link metrics.