Abstract The rapid expansion of large-scale complex networks has intensified the need to understand how structural resilience and functional diffusion processes interact under perturbations. This paper presents a unified framework that integrates percolation-theoretic robustness metrics with canonical influence... The rapid expansion of large-scale complex networks has intensified the need to understand how structural resilience and functional diffusion processes interact under perturbations. This paper presents a unified framework that integrates percolation-theoretic robustness metrics with canonical influence propagation models, addressing a critical gap in network science where structural stability and information flow are typically analyzed in isolation. We formalize network robustness through the persistence of the giant connected component, global efficiency, and a scalar robustness index, evaluating degradation under both random failures and targeted attacks. Analytical foundations, including the Molloy Reed criterion and kshell decomposition, are coupled with simulation-based protocols to quantify critical percolation thresholds and fragmentation dynamics. Concurrently, we model diffusion using the Independent Cascade and Linear Threshold frameworks, revealing inherent trade-offs between structural resilience and propagation efficiency. Hubs accelerate the spread of information but represent critical vulnerabilities, while high clustering enhances local fault tolerance yet may impede global diffusion. Through empirical analysis of realworld scientific collaboration networks, we demonstrate that optimal network design requires contextaware balancing of redundancy, centrality distribution, and connectivity patterns. The study establishes practical design principles for engineering adaptive architectures that withstand stochastic disruptions and adversarial attacks while maintaining functional diffusivity. Ultimately, this framework provides actionable insights for developing resilient infrastructure, robust social platforms, and efficient communication systems, with proposed future extensions to temporal and multiplex network dynamics. Read More Read Less

Abstract The integration of blockchain technology with Internet of Things (IoT) systems presents significant potential for enhancing security, decentralization, and trust. However, this convergence introduces critical challenges including high computational overhead, scalability limitations, latency constraints in 5G-enabled environments, and... The integration of blockchain technology with Internet of Things (IoT) systems presents significant potential for enhancing security, decentralization, and trust. However, this convergence introduces critical challenges including high computational overhead, scalability limitations, latency constraints in 5G-enabled environments, and the absence of unified frameworks that simultaneously address security, trust management, and energy efficiency. This study proposes a multi layer analytical framework for assessing reliability in IoT-blockchain systems using sensor-driven transaction modeling. The methodology integrates statistical correlation analysis via Point-Biserial coefficients, probabilistic modeling through logistic regression, and comprehensive reliability metrics including Transaction Success Rate (TSR) and Mean Transactions to Failure (MTTF). Analysis of an enhanced IoT dataset augmented with blockchain transaction attributes reveals that, while global linear relationships between sensor values and transaction outcomes remain weak (|r| < 0.07), specific sensor types particularly temperature sensors demonstrate statistically significant elevated failure rates (42.9%, OR = 1.54, p = 0.0087). Logistic regression identifies a critical threshold beyond which failure probability increases substantially, while network and transaction graph analyses highlight the importance of understanding interdependencies among system components. The findings emphasize that transaction reliability is driven by localized, context specific factors rather than broad linear relationships, necessitating targeted interventions including sensor specific calibration, oracle optimization, and smart contract edge case handling. This framework provides actionable insights for enhancing robustness in next-generation 5G-enabled IoT blockchain ecosystems. Read More Read Less

Abstract This study presents a methodological framework for analyzing long-range dependence (LRD) in IoT blockchain time series through fractal analysis. Utilizing a synthetically generated Fractional Gaussian Noise (FGN) dataset that emulates high speed network traffic, the research employs... This study presents a methodological framework for analyzing long-range dependence (LRD) in IoT blockchain time series through fractal analysis. Utilizing a synthetically generated Fractional Gaussian Noise (FGN) dataset that emulates high speed network traffic, the research employs the Hurst exponent as a primary metric to quantify temporal persistence and scaling behavior. Two classical yet robust techniques Rescaled Range (R/S) analysis and Detrended Fluctuation Analysis (DFA) are systematically applied to estimate the Hurst exponent across multiple temporal scales. The results demonstrate strong power law scaling, with linear log log relationships confirming scale invariant dynamics. Quantitative estimates yield Hurst exponents of approximately 0.91 (R/S) and 0.88 (DFA), both significantly exceeding the 0.5 threshold for random walks, thereby indicating strong persistent behavior and long term memory. Corresponding fractal dimension values (1.09 and 1.12) reveal moderate smoothness combined with structured irregularity, highlighting the intrinsic complexity of the underlying processes. The high consistency between R/S and DFA outcomes validates the framework's reliability, while DFA's detrending mechanism ensures robustness to nonstationarity. These findings carry significant implications for IoT blockchain ecosystems, particularly in predictive modeling, multi-scale anomaly detection, and adaptive resource allocation. By capturing selfsimilar traffic patterns, the framework enhances system resilience against congestion and stealthy cyber threats. Future research will extend this univariate approach to multivariate network data and explore realtime fractal monitoring for edge-computing environments. Read More Read Less