Songwei Zhang

The robust network topology of the Internet of Things (IoT) system facilitates uninterrupted service provisioning when encountering device failures. Traditional topology optimization strategies use link-level algorithms to design robust network topologies for IoT device deployment, ensuring network resilience against failures. These algorithms struggle to provide a robust topology for large-scale networks due to the high complexity and computational cost of optimizing each link individually. To overcome this limitation, we introduce LEGO-Motif, a motif-based IoT topology generation algorithm inspired by preferential attachment (PA) and evolutionary theory. By sequentially integrating network motifs, similar to assembling LEGO bricks, the algorithm efficiently enhances topology robustness while reducing computational overhead. Specifically, we propose a novel metric based on motif density to measure topology robustness; then, guided by this metric, we design a topology generation algorithm that ensures optimal topology with high robustness against cyberattacks throughout its growth, inspired by an evolutionary neural network framework. The LEGO-Motif algorithm introduces novel recombination, PA-based mutation, and pruning operators to enhance optimization performance and reduce running-time costs. Comprehensive case studies and evaluations show that LEGO-Motif outperforms current topology optimization algorithms, achieving more robust network topologies with reduced running time, which offers a promising optimal solution for deploying the IoT topology.

Efficient task offloading is vital for latency-sensitive Industrial IoT (IIoT) systems. Existing deep learning-based approaches, however, face long training time, poor adaptability, and heavy reliance on labeled data. We propose SRCO, a Semi-supervised Reservoir Computing-based Offloading framework that uses a fixed dynamic reservoir and trains only the readout layer, enabling fast model updates with minimal overhead. A semi-supervised strategy further exploits unlabeled data to reduce labeling cost. Experiments show that SRCO improves offloading accuracy by up to 15.6% and reduces training time by up to 59.6% compared with state-of-the-art methods, demonstrating strong efficiency and adaptivity for real-time IIoT applications.

The robustness of Internet of Things (IoT) topologies measures a network structure’s tolerance to random failures, or attacks, which is crucial for stable network communication. Research on optimizing network topology robustness has shifted from empirical rules and heuristics to machine learning, which can extract the features of robust network from topology data, thereby reducing the complexity of traditional topology optimization. However, machine learning approaches typically require a large number of parameters, resulting in high costs associated with parameter tuning and inference. To address these issues, this paper combines parameterized quantum circuits, and proposes a Quantum-Driven efficient Learning Model (QDLM) for enhancing robustness of IoT topology. This model leverages quantum exponential states to significantly reduce the number of training parameters while preserving learning performance. For inputs, QDLM integrates arithmetic encoding and quantum state encoding based on topological adjacency matrix, reducing the number of neurons. In training phase, parameterized quantum rotation gates and controlled quantum gates are used to achieve efficient training. A quantum measurement method is designed to ensure the output topology is a connected graph with the required number of edges. Compared to existing topology learning models, QDLM achieves an order-of-magnitude reduction in training parameters while maintaining topology learning effectiveness.

In resource-constrained and dynamic Industrial Internet of Things (IIoT) environments, ensuring robust and adaptable network topologies remains a significant challenge. Existing reinforcement learning-based approaches tackle topology optimization but face scalability issues due to high computational complexity and latency under strict time constraints. To address these challenges, we propose FRED-ABL (Fast Robustness Enhancement for Dynamic IIoT topology optimization with Adaptive Bayesian Learning), a novel paradigm that delivers lightweight topology solutions within a constrained time frame. FRED-ABL introduces an innovative topology structure compression method leveraging auxiliary continuous coding, enabling lossless representation of network structures as model inputs. It further defines a new robustness performance metric that integrates considerations of node failures and connection capabilities, serving as a comprehensive evaluation function. By developing an adaptive Bayesian learning model, FRED-ABL efficiently maps the relationship between topology structures and robustness metrics, enabling rapid optimization while significantly reducing computational overhead. Extensive experiments demonstrate that FRED-ABL consistently outperforms state-of-the-art methods, delivering superior robustness and optimization efficiency even in large-scale IIoT deployments.

With the scale of the Internet of Things (IoT) system growing constantly, node failures frequently occur due to device malfunctions or cyberattacks. Existing robust network generation methods utilize heuristic algorithms or neural network approaches to optimize the initial topology. These methods do not explore the core of topology robustness, namely how edges are allocated to each node in the topology. As a result, these methods use massive iterative processes to optimize the initial topology, leading to substantial time overhead when the scale of the topology is large. We examine various robust networks and observe that uniform degree distribution is the core of topology robustness. Consequently, we propose a novel UNIformity driven robusT topologY generation scheme (UNITY) for IoT systems to prevent the node degree from becoming excessively high or low, thereby balancing node degrees. Comprehensive experimental results demonstrate that networks generated with UNITY have an “olive-like” topology consisting of a substantial number of medium-degree nodes and possess strong robustness against both random node failures and targeted attacks. This promising result indicates that the UNITY makes a significant advancement in designing robust IoT systems.