The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs' unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.

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 of floading 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 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 introduceLEGO-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.

In the Industrial Internet of Things (IIoT), Protocol Reverse Engineering (PRE) is a technique that analyzes protocol message samples to facilitate the understanding of unknown protocol specifications, enabling intercommunication between heterogeneous devices using industrial control protocols (ICPs). However, capturing message samples from the IloT environment is time-consuming, and the collected samples may lack sufficient diversity to comprehensively cover the protocol specifications, thereby affecting PRE's effectiveness in understanding protocol specifications. To address this limitation, we propose a message expansion method driven by device interaction to efficiently generate message samples that offer comprehensive feature coverage, ultimately enhancing the protocol understanding. Our approach operates in two stages. During the exploration stage, it modifies the initial input messages captured from the network and, through interactions with the device, determines which fields should be excluded from further exploration, thereby narrowing the exploration space. In the expansion stage, we design a neighborhood particle swarm optimization (NPSO) algorithm to thoroughly explore the remaining search space, generating diverse messages and validating them through interaction with the device to comprehensively cover the protocol's structure and functionality. Experimental results show that our method surpasses existing algorithms in both message generation speed and message quality.

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.

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 (FastRobustnessEnhancement forDynamic IIoT topology optimization withAdaptiveBayesianLearning), 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.

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.

With the scale of Internet of Things (IoT) continually expanding, the topology is growing rapidly and the probability of cascading collapse due to node failures or malicious attacks is increasing. The decrease in the Quality of Service (QoS) of IoT could be mitigated by robust topology. Existing optimization strategies usually use heuristic algorithms to enhance topology robustness. However, when the scale of topology is large, these algorithms involve a significant amount of iterative searching for the optimal solution, which is time-consuming and prone to getting stuck into local optimum. To tackle this situation, this study introduces arithmetic encoding and proposes a novel probability-based robust topology generation model that can quickly generate IoT robust topology. We losslessly compress robust topologies using arithmetic encoding and extract their features. Based on the extracting features, we design a unique probability-based topology generation approach that avoids the time overhead of iterative calculations. Experimental results demonstrate that the proposed solution in this paper can construct robust topologies in less time for different network scales.

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

The robustness of scale-free Internet of Things (IoT) topology is seriously affected by malicious attacks. Improving the tolerance to node failures is critical to the stability of IoT systems. Heuristic algorithms, especially genetic algorithms, enhance the stability of network topology through the evolution of population chromosomes. However, the loss of genetic diversity makes the optimization easily fall into local optimum. Although the problem can be alleviated by adjusting population size and genetic probability, the genetic diversity is still not guaranteed in the limited number of iterations. Inspired by the quantum superposition that simultaneously operates on an exponential number of states, we propose a quantum-inspired robust networking model with multiverse co-evolution for the scale-free IoT (Q-Robust). This model designs quantum chromosomes with double-chain structures to represent the connections between all nodes. Then we present the quantum measurement method of quantum chromosomes based on the degree distribution of nodes. Furthermore, this model constructs a primary-secondary quantum multiverse co-evolution mechanism to improve the convergence efficiency of topology evolution. The experimental results show that the topology robustness optimized by Q-Robust is about 60% and 10% higher than the initial topology and the state-of-the-art topology evolution algorithm, respectively.

Fast-advancing mobile communication technologies have increased the scale of the Internet of Things (IoT) dramatically. However, this poses a tough challenge to the robustness of IoT networks when the network scale is large. In this paper, we present DAC-Motif, a distributed co-evolutionary method for optimizing network robustness based on network motifs. Unlike centralized evolutionary optimization approaches, DAC-Motif uses the technique of Divide-And-Conquer (DAC) to divide the large-scale IoT topology into partitions and then merge the self-evolving partitions into a global robust topology. This approach leverages both distributed computing and asynchronous communication mechanisms to mitigate premature convergence and reduce time complexity for large-scale IoT topologies. In our evaluation, DAC-Motif achieves three to four orders of magnitude shorter running time and over 10% robustness improvement compared to other centralized evolutionary algorithms under a scale of around 5,000 IoT devices.

Topology robustness is critical to the connectivity and lifetime of large-scale Internet-of-Things (IoT) applications. To improve robustness while reducing the execution cost, the existing robustness optimization methods utilize neural learning schemes, including neural networks, deep learning, and reinforcement learning. However, insufficient exploration of reinforcement learning agents for topological environments is likely to yield local optima. Moreover, convergence speed is influenced by the sparse reward problem generated while exploring topological environments. To address these problems, this study proposes a self-adaptive robustness optimization method with an evolutionary multi-agent for IoT topology (ROMEM). ROMEM introduces a new multi-agent co-evolution scheme that leverages a non-deterministic strategy to extend the exploration in multi-directions, enabling the reinforcement learning agent to transcend local optima. Furthermore, ROMEM presents a novel distributed training mechanism for multiple agents to accelerate convergence. Experimental results demonstrate that ROMEM can achieve multi-directional collaborative training and outperform other state-of-the-art learning-based robustness optimization methods in terms of convergence efficiency and robustness.

With the rapid scale expansion of the Internet of Things (IoT), the probability of system failure increases. Frequent system failures degrade the quality of service (QoS) of IoT. Existing optimization strategies utilize reinforcement learning (RL) to enhance the robustness of IoT topology. However, due to the increasing scale of the IoT environment, the unbalanced exploration and exploitation of RL agents make it prone to premature convergence at the local optimum. Large-scale action spaces and state spaces lead to a sparse reward problem, which reduces the convergence efficiency of the algorithm. This paper proposes an evolutionary reinforcement learning scheme for IoT robustness to solve the above problems. We design a multi- agent evolution mechanism to provide multiple experiences for RL, which strengthens exploration capability. We present new evolution operators to promote convergence, which combine dis- tillation crossover and Gaussian mutation. Extensive experiments show that our scheme has a strong exploration capability, and the optimization rate of IoT topology robustness reaches 81.15%, which outperforms other robustness optimization algorithms.

The development of the energy storage industry and the higher electricity prices lead to the continuous increase of users’ demand for battery energy storage in industrial manufacture. To monitor the status of the battery and control the running process of the battery, we need a battery management system (BMS) with good performance and complete functions. Previously proposed BMS generally lacks functionality and is not designed for energy storage systems in industrial manufacture. This paper aims to design and implement a BMS for energy storage. The system can collect various data such as battery voltage, temperature, current, smoke, and so on. The functions of calculating status data, detecting faults, passive battery balance, and display are also supported. Moreover, the system optimizes the functions of battery balance and display. Finally, the experimental results show that the BMS achieves a battery voltage measurement error within 2mV, supports a passive balance current of about 100mA, and makes the computing resource allocation more balanced.

Internet of Things (IoT) is growing with various applications linked in, and node failures are becoming more common as a result of malicious strikes and other issues. The cascading collapse induced by local node failures can be mitigated by robust network topology. Existing approaches for fixed topology enhance the robustness of IoT topology by reconstructing device connections. However, using existing techniques necessitates global topology optimization when new nodes are added, which takes time. To tackle this situation, this study introduces an evolutionary algorithm based on neuroevolution that generates robust IoT topology. It provides IoT topology with inherent robustness when adding extra nodes by utilizing unique mutation and crossover operators. What’s more, we establish an adaptive edge density management method to reduce the rise in energy consumption caused by redundant connections when nodes join. Experimental results indicate that the proposed scheme can effectively build robust topology than multiple existing topology optimization methods in less time for diverse network sizes.

The span of Internet of Things (IoT) is expanding owing to numerous applications being linked to massive devices. Subsequently, node failures frequently occur because of malicious attacks, battery exhaustion, or other malfunctions. A reliable and robust network topology can alleviate the cascading collapse caused by local node failures. Existing optimization methods for fixed topologies enhance the robustness of the IoT topology by reconstructing the connections among the devices. However, the application of existing algorithms requires global topology optimizations or local adjustments when new nodes are added, which leads to high computational complexity. To address this problem, based on neuroevolution and network motifs, this study proposes an evolutionary algorithm to generate a robust IoT topology called “Born This Way: a self-organizing evolution scheme with Motif” (BTW-Motif). Using novel mutation and crossover operators, BTW-Motif generates an IoT topology with intrinsic robustness when new nodes are added. We design an adaptive edge density control mechanism to avoid an increase in energy consumption resulting from redundant connections. Specifically, BTW-Motif innovatively introduces network motif as a guide structure which has been proven to have a positive effect on the network robustness. Experiments indicate that BTW-Motif can effectively produce a robust topology. With different network sizes and edge densities, BTW-Motif can generate more robust topologies compared with the existing topology optimization algorithms. And the time consumption for the large-scale topology to achieve similar robustness is reduced by 50%.

The complex factory environment of the Industrial Internet of Things (IIoT) greatly increases the energy consumption of sensor nodes and reduces production profits. Especially, in mines, the terrain will change continuously as the mining progresses. Additionally, the heavy traffic load on a single sink node and the unbalanced load on multiple sink nodes also reduce the battery life. Therefore, how to build an energy-efficient topology based on the unique mine terrain characteristics is a critical issue. To address this problem, this article proposes a 3-D topology evolution scheme with self-adaption for mining areas (3D-TES) to reduce energy consumption. We build the multipeak terrain model according to the characteristics of the mining environment. Blocked by the undulating peaks on the mine, the strength of the node signal is quantified by the slope and aspect. The 3D-TES is then applied to determine the optimal number of sink nodes and find the best data transmission path between sensor nodes and multiple sink nodes. The experimental results show that 3D-TES outperforms the directed angulation toward the sink node model (DASM) in terms of reliability, average path length, and data load on sink nodes.

This paper considers the cyber-attacks that aim to remove nodes or links from network topologies. We particularly focus on one category of such attacks, in which attacks happen by rounds, and in each round, the node with the highest centrality and its adjacent links are removed. Here the centrality can be any centrality measure such as Degree Centrality, Betweenness Centrality, etc. For this attack category, there currently exist two strategies: Initial and Adaptive. In the Initial strategy, node centralities are only calculated initially, while in the Adaptive strategy, node centralities are recalculated after each round of attack. In the literature, it has been shown that the Adaptive strategy is more effective than the Initial strategy for a centrality measure. In this paper, we propose a new strategy called the largest component (LC) strategy which further outperforms the Adaptive strategy in terms of both attack effectiveness and computation complexity. Moreover, we propose the use of current-flow versions of Betweenness Centrality and Closeness Centrality as the centrality measures in the attacks, since they are more granular and supported by the LC strategy. We verify the better performances of the LC strategy by extensive experiments on four kinds of artificial networks and two realworld networks. Our experiments also show that the Currentflow Betweenness Centrality makes attacks the most effective among the five centrality measures studied in this paper.

The research on three-dimensional topology is important for Internet of Thing. Small-world with shorter average path lengths has proven to be an effective model for building evolutional network topologies. In order to build three-dimensional topology in IoT, the ant colony algorithm is used to plan shortcuts in this paper. First, Gaussian integration is used to simulate the ups and downs of terrain in three-dimensional space. Second, a significant number of nodes are randomly deployed on the modeled terrain. Taking into account the information about slope and aspect around the node, the actual sensing range of the node is calculated. Third, a certain percentage of nodes are selected as super sensor nodes. Finally, the ant colony algorithm is used to add shortcuts between super sensor nodes. Extensive experimental results show that an energy-efficient three-dimensional network topology in IoT can be built by the algorithm.

In recent years, many fields have begun to use Internet of Things(IoT) to monitor the environment, especially in mountain terrain. Node failures bring a significant challenge in mountain terrain monitoring. Thus, how to improve the robustness of networks withstand node failures becomes a critical issue. To address this shortcoming, this article proposes a strategy to improve the robustness of IoT topology based on Genetic Algorithm (GA). First, Gauss Integration is used to build a 3D terrain to simulate the mountain terrain. Then, an initial scale-free topology according to the characteristics of IoT in 3D terrain is built. Furthermore, a novel crossover operator and a novel mutation operator are proposed to optimize the robustness of IoT topology in 3D terrain. Our proposed model keeps the initial degree of each node unchanged such that the edges overhead will not increase. The extensive experiment results show that our algorithm can significantly improve the robustness of topology in 3D terrain. Especially, the robustness of topology still keeps a high level in the case of partial node failures.