Abstract The proliferation of connected devices and data intensive applications is driving a fundamental transformation of communication systems through the convergence of advanced signal processing, artificial intelligence, and cloud edge computing. This paper surveys recent advances in secure... The proliferation of connected devices and data intensive applications is driving a fundamental transformation of communication systems through the convergence of advanced signal processing, artificial intelligence, and cloud edge computing. This paper surveys recent advances in secure and intelligent edge networking, with particular attention to the interplay between quality of service (QoS) metrics, encryption mechanisms, and transmission reliability. We then present an empirical evaluation of the Cross Border Edge Data dataset (15 000 ASEAN sessions) to quantify the joint predictive power of network performance indicators and encryption configurations for Transmission Success Rate. Correlation analysis, multi model regression (Ridge, Gradient Boosting, Random Forest, Extra Trees), permutation importance, and stratified conditional analysis all converge on the same finding: the available predictors exhibit near zero explanatory power (best R² = - 0.0019). The results clarify the practical limits of existing QoS and security features as predictors of transmission outcomes in large scale cross border edge deployments. Read More Read Less

Abstract Programmable Logic Controllers (PLCs) are foundational to modern industrial automation, yet the datadriven characterisation of their input output dynamics at typical process historian sampling rates remains underexplored. This study investigates the dominant input output relationships within a... Programmable Logic Controllers (PLCs) are foundational to modern industrial automation, yet the datadriven characterisation of their input output dynamics at typical process historian sampling rates remains underexplored. This study investigates the dominant input output relationships within a closed loop PLC industrial control system using a high resolution dataset of 100,000 time stamped observations. We employed a sequential analytical pipeline comprising bivariate correlation, multivariable ordinary least squares (OLS) regression, random forest modelling, and dynamic ARX extensions, followed by rigorous residual diagnostics. Our findings reveal that the control loop behaves primarily as a near deterministic static mapping rather than a dynamic system with memory. The PLC output is overwhelmingly governed by a binary emergency stop interlock and a linear gain on the continuous input signal, achieving a predictive R2 of 0.9962 and an RMSE of 0.0974 V. Augmenting the model with lagged variables yielded negligible improvements, and residual analysis confirmed the absence of serial correlation. Discrete events, particularly emergency-stop activations, dictate absolute operating levels and safety transitions, while continuous variables like valve status play secondary roles. These results demonstrate that a compact, linear static surrogate is highly adequate for digital twin construction and anomaly detection at this temporal resolution, bypassing the need for complex dynamic modelling. Read More Read Less

Abstract The increasing deployment of federated learning, edge computing, and Internet of Things (IoT) infrastructures necessitates robust frameworks for evaluating data transmission reliability in secure environments. Traditionally, communication quality is assessed using classical network performance metrics such as bandwidth,... The increasing deployment of federated learning, edge computing, and Internet of Things (IoT) infrastructures necessitates robust frameworks for evaluating data transmission reliability in secure environments. Traditionally, communication quality is assessed using classical network performance metrics such as bandwidth, latency, packet loss, encryption time, and distributed node count. However, it remains unclear whether these conventional indicators adequately capture continuous transmission reliability under complex security, privacy, and trust constraints. This study investigates the predictive utility of five classical network metrics across two distinct outcomes: continuous transmission reliability and categorical transmission status. Using a Network Transmission Dataset comprising 13,000 records, we applied correlation analysis, linear and non-linear regression (Ordinary Least Squares, Random Forest, Gradient Boosting), and classification models (Multinomial Logistic Regression, Random Forest). The results reveal a striking dissociation: classical metrics exhibit near zero correlation with continuous reliability (R2  , failing to explain its variance. Conversely, the same metrics achieve approximately 72% accuracy in classifying categorical transmission status, driven predominantly by packet loss (54.6%) and network latency (30.9%). These findings demonstrate that classical network metrics are fundamentally insufficient for predicting continuous transmission reliability in federated secure environments. Comprehensive reliability assessment frameworks must therefore incorporate multidimensional security, privacy, and trust variables rather than relying exclusively on traditional Quality of Service (QoS) indicators. Read More Read Less