With the advent of advancements in future sixth-generation (6G) communication systems, Internet of Things (IoT) devices, characterized by their limited computational and communication capacities, have become integral in our lives. These devices are deployed extensively to gather vast amounts of data in real-time applications. However, their restricted battery life and computational resources present significant challenges in meeting the requirements of advanced communication systems. Mobile Edge Computing (MEC) has emerged as a promising solution to these challenges within the IoT realm in recent years. Despite its potential, securing MEC infrastructure in the context of IoT remains an open task. This study explores the operational dynamics of a secured IoT-enabled MEC infrastructure, focusing on providing real-time, on-demand, secure computational resources to low-powered IoT devices. It outlines a joint optimization problem to maximize computational throughput, minimize device energy consumption, reduce computational latency, and mitigate security overhead. An optimization algorithm is introduced to address these challenges by jointly allocating resources, thereby optimizing throughput, conserving energy, and meeting latency benchmarks through dynamic system adaptation. The effectiveness of the proposed model and algorithm is demonstrated through comparisons with relevant benchmark schemes, highlighting its efficiency in various scenarios. This work showcases the potential of advancements in encryption to deliver scalable security solutions with reduced resource consumption as the number of devices increases.

Speech recognition and time-series data processing are crucial for applications such as human–computer interaction, assistive technologies, and biometric authentication. However, traditional methods often struggle with noisy data, speaker variability, and high-dimensional feature spaces, which limit their accuracy and interpretability. This study proposes a novel framework that integrates Dynamic Time Warping (DTW) with Multidimensional Scaling (MDS) to improve the visualization and analysis of speech time-series data. The framework consists of four stages: data preparation, preprocessing, DTW distance calculation, and two-dimensional (2D) vector space mapping. Lip regions were extracted from video frames and represented using raw grayscale images, lip-shaped approximations, and hybrid features. DTW is applied to measure temporal similarity, followed by MDS to project the data into a lower-dimensional space for clearer feature distribution and more efficient Cluster Validity Index (CVI) computation. The experimental results show that the proposed approach enhances recognition performance. Among the features tested, V_Δgray achieved the highest speaker-dependent recognition rate of 94.96% (±0.0364 standard deviation), whereas V_shape yielded the best speaker-independent recognition rate of 50.91% (±0.2596 standard deviation). Additionally, syllable- and word-level analyses further confirmed the robustness of V_shape. In conclusion, the DTW–MDS framework improves class separability and interpretability and offers a reliable and efficient method for time-series speech analyses. These findings have significant implications for mobile and wearable speech recognition systems.