In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.

In smart-city air-quality monitoring, millions of IoT sensors generate time-sensitive data that must be processed within strict end-to-end deadlines, under dynamic mobility conditions, and with strong security guarantees. In this study, we present EdgePlus, a unified sixth-generation (6G) framework that couples 1) masked cooperative PPO (CCPPO) enforcing feasibility at sampling time (routing order, slice quotas, resource limits), 2) a constricted particle swarm optimization (PSO) outer loop that escapes local optima under strict SLA gates, 3) a lightweight advisory layer (Nash sharing, Ziegler–Nichols-initialized PID, and leader election) that biases logits without breaking masks, and 4) a two-tier blockchain-lite ledger [edge proof-of-authority (PoA), cloud Byzantine fault-tolerant BFT)] whose reputation and debt signals feedback into state, reward, and masks. In a 100 km2 city-scale simulator (ten clusters; three load regimes of 50 000, 100 000, and 150 000 tasks). Under light load, all methods reach 100% success with seconds-level latencies and no observable penalty from masking or security. At stress (150k), EdgePlus achieves the highest on-time completion rate (82.77%), highest throughput (206.93), lowest violation rate (17.23%), and lowest 15 percentile latency ( $L_{\!p50}$ of 53.23 s). Ablations confirmed that feasibility masks dominate performance: removing them increases violations by 5.5%–10.0% points and tail latency ( $L_{\!p95}$ ) by 26–47 s, while advisory and PSO deliver smaller but consistent gains. The PID controller remains robust under ±20% gain drift (settling $\approx 2.7$ –2.9 s) by design-time Jury certification. In security experiments, baseline confidentiality, integrity, and availability (CIA)/auth attacks are mitigated at 98%–99% and advanced (nonphysical-layer) threats achieve 70%–83% defense with strong efficiency. Edge-tier PoA validation adds a median $\approx 0.39$ ms per partial block for validator sets up to 10 and remains sub-ms with partitioned sub-quorums up to 30, while cloud BFT provides accountable finality off the critical path. In short, EdgePlus delivers a single, coherent scheduling–control–security stack for 6G edge computing, built on a feasibility-first design that preserves reliability, locality, and security at scale.

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments.