The robustness of Internet of Things (IoT) communication topologies against internal failures and external perturbations is a fundamental prerequisite for maintaining system stability. This paper studies IoT topology robustness from two perspectives: robustness metric and optimization. Existing robustness metrics, primarily based on the maximum connected subgraph, neglect contributions from other connected subgraphs, thereby inadequately capturing dynamic topological changes. To address this issue, we propose a robustness metric based on topology data reachability, which sensitively reflects the data transmission capability of an IoT topology under arbitrary perturbations. Regarding robustness optimization, most existing methods adopt centralized strategies that rely on global information, resulting in inefficiencies and limited adaptability in decentralized IoT environments. We propose DecTRO, a Decentralized Topology Robustness Optimization method for IoT via multi-agent graph reinforcement learning. To mitigate partial observability, DecTRO employs a scalable graph attention network enhanced with multi-modal sampling, which aggregates cross-agent information and captures spatiotemporal correlations. Furthermore, a topology robustness-oriented node sampling method is introduced to reduce action-space complexity and accelerate convergence, while a decentralized heuristic reward function enables efficient online decentralized learning. Experimental results show that DecTRO achieves up to two orders of magnitude (5–125×) greater improvement in robustness per unit time compared with state-of-the-art baselines, striking a favorable balance between robustness enhancement and computational efficiency.

Due to the highly dynamic nature of UAV swarms, the ad-hoc network of UAV swarms usually experiences frequent changes, which poses significant challenges to maintaining persistent connectivity. Especially, the vulnerable nodes with a topological degree of one in the network topology of a UAV swarm, have the weakest network connectivity and are most susceptible to disconnection. To eliminate the vulnerable nodes, this paper proposes a midpoint guided topology control optimization method. This approach maintains the swarm’s steady-state operation while dynamically adjusting the positions of vulnerable nodes to increase their network topology degrees, thereby eliminating vulnerable nodes and enhancing the robustness and fault tolerance of the ad-hoc network. Furthermore, the validity of the proposed method is proved to be efficient through mathematical analysis, and the experimental results in three-dimensional space consistently demonstrate the superiority of the proposed approach.

Network failures, whether due to random disruptions or malicious attacks, pose significant challenges for uncrewed aerial vehicle (UAV) swarm networks. One critical concern is determining which failed UAVs to recover or replace under limited resource conditions to enhance the robustness of their communication networks. Current research primarily considers static structural characteristics of the network and struggles to uncover deep features that influence network robustness, and the efficiency cannot meet the real-time needs in UAV swarm scenarios. To address these issues, we introduce a Prioritized Recovery strategy for failed nodes based on graph reinforcement learning (PRGRL). This approach integrates a random SAmpling neighbor method with a multihead attention mechanism to create a novel graph convolutional kernel (SAGCK). This kernel is designed to extract global structural information and relative positional information of nodes within the graph. Additionally, we develop a deep policy network (DPN) that explores the intricate relationships between graph-level and node embedding features, enabling the assessment of nodes’ impact on overall robustness. PRGRL’s network parameters are automatically updated and optimized using scalable deep reinforcement learning. Importantly, PRGRL prioritizes the recovery of boundary nodes within connected components to enhance network robustness further. Our experiments, conducted on both simulated and real-world networks, demonstrate that PRGRL outperforms existing methods of robustness enhancement across various recovery ratios, attack strategies, and network sizes while delivering superior real-time performance.

Neural networks are vulnerable to meticulously crafted adversarial examples, resulting in high-confidence misclassifications in image classification tasks. Due to their stealthiness and difficulty in detection, black-box transfer attacks have become a significant focus of defense. In this article, we propose a purification defense based on probabilistic scheduling algorithm of pre-trained models (ProbSched-PTM) to counter diverse transfer attacks. We first quantify the differences among various models based on their output scores and verify the linear negative correlation between adversarial transferability and model difference. Subsequently, guided by the model difference probability, we integrate the negative momentum probability as a regularization factor to construct ProbSched-PTM. It selects the most appropriate substitute model from multiple pre-trained models to generate strong-transferability adversarial examples for training the purification model, which enables the purification model to effectively eliminate diverse adversarial perturbations. The ProbSched-PTM-based purification defense provides robust defense against unseen adversarial attacks from different substitute models. In a black-box attack scenario, utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 94.8% on CIFAR-10 and over 71.2% on Mini-ImageNet, demonstrating state-of-the-art performance.

Due to the high mobility of unmanned aerial vehicles (UAVs), the network topology may change frequently, making persistent connectivity and fault tolerance difficult. Deep reinforcement learning (DRL) offers the opportunity to make proper actions in a large decision space, which could be utilized for the complicated topology control of flying ad-hoc networks. However, how to train and deploy the DRL algorithms on resource-limited and hardware-constrained UAVs to ensure network connectivity and fault tolerance still faces huge challenges. In this work, a topology control method based on positional movement and DRL is proposed, which is suitable for topology construction and topology adjustment. First, a centroid-guided target-driven method is designed to transform arbitrary graphs into 2-connected graphs by connecting each node with its two designated target nodes in a specific order. Then, a topology control method based on the centroid-guided target-driven method and soft actor-critic (CGTD-SAC) is proposed. CGTD-SAC trains agents to keep connectivity with two target agents and keep a safe distance from surrounding agents. CGTD-SAC generates 2-connected topologies in a distributed manner. CGTD-SAC is a tiny algorithm with low computational complexity and less communication overhead. Finally, experiments demonstrate that CGTD-SAC has an excellent ability to obtain network topologies with 2-connectivity, suitable link length, and appropriate number of links.

Finding top-k flows in data streams is a fundamental task in network management. As the line rates continue to increase in a network, it becomes increasingly challenging to find the top-k flows precisely and quickly in real time. Existing algorithms that can achieve high precision suffer from a slow speed. In this paper, we propose a novel sketch, LUSketch, which is much faster than existing algorithms. LUSketch adopts a new strategy called limited-and-imperative-update to significantly improve the insertion speed. The key idea is to significantly reduce the number of update operations by establishing a connection between the sketch and heap part when tracking the top-k flows. Our experiment results show that, while maintaining a high precision, LUSketch achieves an insertion speed approximately 2 to 5 times higher than that of the state-of-the-art.

The cover image is based on the Research Article Filter-Sketch: A two-layer sketch for entropy estimation in the data plane by Jie Lu et al., https://doi.org/10.1049/cmu2.12494.

Abstract Entropy‐based approaches have been shown to aid various network measurement applications such as load balancing, anomaly detection, and traffic classification. Existing methods that assume out‐of‐band detection and/or use switches merely as accelerators require frequency communication between data and control plane, which increase the burden on the network and are no longer sufficient. To track these challenges, the authors design and implement a switch‐native approach for entropy estimation that can run detection function entirely inline on data plane. The authors first present Filter‐Sketch, a two‐layer sketch that supports frequency estimation with small and static memory allocation by separating elephant flow and mice flow. Then, the authors propose a mechanism based on memory‐optimized longest‐prefix match (LPM) to attain entropy at a line rate that completely executes in the programmable data plane. The trace‐driven evaluation shows that Filter‐Sketch achieves higher accuracy than the existing data plane algorithm in entropy estimation, where the relative error decreases by 0.65 on average.