Industrial cyber-physical systems have been used as the foundational platform for ensuring resilient and robust communication in dynamic industrial environments. In the specific scenario of industrial control systems experiencing abrupt temperature disturbances and cyber infection dynamics, conventional methods relying on static thresholds and SIS models fail to deliver optimal stability. In this paper, we propose the real-time adaptive reconfiguration algorithm (RTARA), which integrates temperature-dependent SIR modeling, spectral graph theory, and an adaptive gossip framework to dynamically adjust communication protocols and trigger network reconfiguration when a critical threshold is breached. Our implementation employs a momentum-based gradient descent update enhanced with a recursive composite operator, achieving rapid convergence as evidenced by an MSE reduction from 0.045 to 0.012, a convergence rate improvement from 0.020 to 0.004, and a decrease in adaptation latency from 2.5 s to 0.5 s. The experimental results demonstrate that our approach significantly enhances network stability, with a stability index increasing from 0.70 to 0.95, thereby confirming its superior performance.

Distributed optimal control algorithms have been used as the foundational framework for adaptive error management in ultra-dense 6G sensor network deployments, enabling efficient data transmission and robust error resilience. In the specific scenario of ultra-dense 6G networks, characterized by high uncertainties in both signal acquisition and communication channels, traditional fixed-threshold schemes fail to mitigate error clustering and achieve rapid convergence. In this paper, we propose a distributed adaptive stability control algorithm (DASCA) based on the distributed Hamilton-Jacobi-Bellman optimal control framework to address these limitations. Our approach integrates an enhanced consensus update protocol with higher-order compensation and adaptive step-size strategies, significantly improving convergence rate and error minimization. Experimental results demonstrate that the DASCA reduces the cost functional by approximately 50% and increases the convergence rate by around 60%, thereby outperforming conventional methods and confirming its efficacy in rapid error mitigation.

Autonomous vehicle fleets have been used as the communication infrastructure basis for information dissemination in cooperative autonomous driving scenarios such as urban intersections, toll plazas, and congested highways. In the specific scenario of highly dynamic and localized environments, existing dissemination methods suffer from scalability, latency, and reliability issues. In this paper, we propose an enhanced wavefront-based information dissemination (WBID) scheme to address the inefficient and unreliable propagation of critical information. The scheme integrates improved diffusion models with a hybrid robust control-based distributed optimization (HRCDO) mechanism and an adaptive multi-hop communication (AMHC) mechanism to jointly regulate spatiotemporal information propagation speed, relay decision robustness, and communication efficiency so that information dissemination can remain low latency, high coverage, and reliable under rapidly varying vehicle density and environmental uncertainty. Experimental results demonstrate that the proposed WBID scheme, utilizing the advanced multi-objective relay optimization algorithm (AMOROA), achieves a 95% information coverage, reduces dissemination time by 40%, and lowers communication overhead by 25% compared to existing methods.

Large-scale distributed communication networks have been widely recognized as the backbone for efficient data dissemination in cloud and IoT systems, serving as the foundation for dynamic routing, load balancing, and synchronization. In the specific scenario of real-time routing, traditional static approaches fail to adapt to variable communication delays, congestion, and node failures, resulting in high average end-to-end delay and suboptimal performance. In this paper, we propose the adaptive routing and fault tolerance protocol (ARFTP), a unified framework that leverages adaptive Susceptible-Infectious-Recovered (SIR) model applications and dynamic gossip algorithms to minimize the average end-to-end delay while ensuring load balancing and fault tolerance in large-scale distributed networks. Our approach employs an adaptive weight mechanism that continuously updates routing decisions based on real-time congestion and delay feedback, integrating recursive load feedback and a backup routing strategy to achieve efficient information dissemination. Experimental results demonstrate that the ARFTP reduces the average end-to-end delay to 0.15 s, increases throughput to 0.92, and significantly improves load balancing and fault tolerance compared to conventional static routing methods.

The Susceptible-Infected-Recovered paradigm (SIR) coupled with decentralized gossip remains central to information flow and misinformation mitigation in heterogeneous urban networks, yet static parameters (β, γ, τ) and limited adaptivity hinder responsiveness under rapidly varying conditions. We propose the stochastic trust-enhanced calibration algorithm (STCA), which unifies dynamic trust updates with adaptive gossip control under a high-order Hamilton-Jacobi-Bellman (HJB) stochastic optimization framework. At its core, the STCA performs iterative calibration of (β, γ, τ) via gradient descent augmented by fractal-inspired performance metrics and recursive coupling operators, yielding robust stability guarantees and real-time adaptation to mobility and load fluctuations. Experiments demonstrate a 50 % reduction in detection time (15 s vs. 30 s), an isolation-efficiency gain of approximately 19 % (0:95 vs. 0:80), and a 50 % increase in communication throughput (1200 units vs. 800 units). These results indicate that jointly enhancing trust dynamics, stochastic control, and adaptive gossip via STCA constitutes a scalable technological advancement for smart-city information dissemination, delivering certificate-oriented stability and performance suitable for next-generation (6G+) deployments.

Entropy-driven network coding has been used as a basis for optimizing data transmission and enhancing resource utilization in Fog-Cloud IoT architectures, including applications in smart cities, industrial automation, environmental monitoring, and healthcare. In Fog-Cloud IoT architectures, conventional data transmission protocols are inefficient because they cannot adapt to dynamic entropy levels, resulting in underutilized bandwidth, increased latency, and higher energy consumption. In this paper, we propose an Enhanced Entropy-Driven Network Coding (E-EDNC) framework to address these problems. Our framework integrates real-time entropy estimation with adaptive coding strategies and employs a hybrid evolutionary-reinforcement learning (HE-RL) algorithm to dynamically optimize coding parameters and scheduling decisions. Experimental results demonstrate that E-EDNC improves bandwidth utilization by 25%, reduces latency by 25%, and decreases energy consumption by 12%, thereby enhancing overall data transmission efficiency and reliability in Fog-Cloud IoT environments.

The Industrial Internet of Things (IIoT) networks serve as the foundational infrastructure for real-time communication and data exchange in smart manufacturing. Predicting the spread of malware within IIoT networks is particularly challenging due to uncertainties in infection and recovery rates, which are influenced by dynamic network conditions and device heterogeneity. In this article, we propose an adaptive fuzzy SIR model that incorporates fuzzy logic and gradient descent optimization to address these uncertainties. Specifically, we integrate fuzzy logic with gradient descent, which introduces an adaptive mechanism to handle uncertain infection and recovery rates in real time. This synergy ensures robust parameter tuning under fluctuating network states, significantly improving malware spread prediction. The proposed model dynamically adjusts infection and recovery rates using fuzzy differential equations and real-time data adaptation, enhancing prediction accuracy and resilience to network fluctuations. Experimental results demonstrate the model’s advantages in improving predictive accuracy, convergence speed, and adaptability, making it a robust solution for securing IIoT networks in smart manufacturing.

Decentralized smart manufacturing systems serve as a robust foundation for optimizing production processes and maintaining stringent quality standards in modern industrial environments. However, in such distributed architectures, uncontrolled and rapid fault propagation presents significant challenges, often resulting in widespread operational disruptions and compromised system integrity. To address these issues, we propose the spectral-adaptive consensus fault mitigation algorithm (SAC-FMA), which efficiently detects, contains, and mitigates fault propagation across decentralized manufacturing networks. Our approach uniquely combines spectral graph theory with adaptive consensus mechanisms to synchronize fault detection and isolation while dynamically adjusting operational parameters to preserve system stability and efficiency. Experimental results demonstrate that SAC-FMA outperforms traditional fault management approaches with a 41% improvement in convergence rate, 67% enhancement in fault containment efficiency, 60% reduction in system instability, 95% maintenance of operational performance, and 40% decrease in communication overhead. These findings highlight SAC-FMA's potential for significantly enhancing resilience and reliability in smart manufacturing environments.

Recent research in smart factory networks has shown that advanced adaptive control are essential for managing the multidimensional uncertainties inherent in communication systems. In dynamic environments where traditional fuzzy controllers suffer from slow convergence and reduced robustness, rapid error decay is critical to ensure system stability. In this article, we propose the adaptive reinforcement fuzzy control algorithm (ARFCA), a novel scheme that integrates advanced Type-2 fuzzy inference, reinforcement learning-based control updates, and temporal memory defuzzification to maximize the convergence rate (CR). Experimental results demonstrate that the proposed ARFCA achieves a CR approximately three times higher and reduces control error by nearly 80% compared to conventional methods, thereby significantly enhancing system reliability and scalability.

Cloud data centers (CDCs) have been used as the basic platforms for data storage in industrial scenarios such as smart manufacturing. However, a lack of effective data storage schemes exacerbates reading latency and replicas' inconsistency in the machine tools of smart factories. In this article, we propose a full-replicas scheme (FRS) to attain the low-latency reading and high data consistency required in smart manufacturing. First, inspired by the susceptible–infectious–recovered epidemic model, the network bandwidth usage generated by the FRS is adjusted by the targeted immunization principle. Then, the final breakout rate is derived as a function of the immunization rate, which can reduce the complexity of target immunization implementation in scale-free networks. Finally, the experimental results confirm our theoretical analysis and show that the FRS provides strong consistency with lower client-side reading latency.