Nowadays, in cloud computing, task scheduling is a key factor to enhance the performance of the distributed computing environment. As workloads vary dynamically, traditional scheduling algorithms like RR and HRRN have limitations in achieving optimal performance. To overcome these constraints, this paper proposes a PSO-Optimized Hybrid Scheduler that integrates Particle Swarm Optimization (PSO) with RR, HRRN, and Shortest Remaining Time First (SRTF) scheduling approaches. In this integrated approach, the PSO role is to find the optimal task execution order, the RR role is to ensure fair time-sharing among tasks, and the HRRN role is to prioritize processes to dynamically reduce excessive waiting time and prevent starvation. The SRTF provides short-job bias through the inverse remaining time term. The proposed mechanism is evaluated by using AWT and ATAT as key metrics. A Python-based simulation and a data set of six processes are used for validation and implementation, and a mathematical analysis is also presented to ensure correctness. The proposed PSO-optimized Hybrid Scheduler shows lower waiting and turnaround times than classical RR and HRRN scheduling approaches. In comparison with HRRN, the improvements of the proposed mechanism in AWT, ATAT, ART, and AS are 3.6%, 2.3%, 3.6%, and 7.9%, respectively. The findings are that the proposed mechanism provides an effective, balanced solution for resource scheduling in cloud environments.
High-mobility 6G sensing places where strict requirements on waveform robustness are required because the large Doppler shifts can degrade velocity estimation. This paper presents a refined simulation-based comparison of Orthogonal Time Frequency Space (OTFS) and Orthogonal Frequency Division Multiplexing (OFDM) for monostatic velocity sensing at 28 GHz. The evaluation uses a three-way receiver design: the first is OTFS with a single delay–Doppler pilot. The second is a strong full-grid OFDM radar baseline with known time–frequency symbols and range–Doppler matched processing. Third is a communication-oriented sparse-pilot OFDM receiver that reflects practical joint communication and sensing (JCAS) pilot budgets. All branches share identical carrier frequency, subcarrier spacing, grid size, cyclic-prefix length, power normalization, and Monte Carlo settings (250 trials per point). Velocity accuracy is reported through root-mean-square error (RMSE), success rate within ±10 km/h, multipath ablations, a velocity sweep, and quantitative response-concentration metrics. The results show that OTFS with one delay–Doppler pilot achieves near-constant RMSE of about 0.43 m/s and 100% success rate across −10 dB to 25 dB SNR at 120 km/h. A full-grid OFDM radar baseline attains essentially the same accuracy, demonstrating that OFDM is not intrinsically incapable of high-mobility velocity estimation when dense known pilots are available. So sparse-pilot OFDM collapses under the same Doppler conditions (RMSE ≈ 79 m/s and zero success rate) while OTFS preserves compact energy concentration in the delay–Doppler plane. These findings justify OTFS for pilot-efficient high-mobility sensing and clarify that OFDM under-performance in high-mobility sensing can be primarily baseline- and overhead-dependent rather than an absolute waveform verdict.
Federated Learning (FL) has become a promising distributed machine learning paradigm that allows collaborative model training while maintaining user privacy by keeping sensitive data on local devices. However, existing FL-based next-word prediction systems mainly focus on the model performance and lack robust mechanisms to prevent unauthorized users and compromised devices from joining the training process, raising security and reliability issues. To overcome this limitation, in this paper, a secure homogeneous federated learning framework for next-word prediction is proposed by combining a Long Short-Term Memory (LSTM) model with a dual-factor authentication mechanism. The proposed framework includes a central aggregation server and three homogeneous client devices with the same model architecture and training configurations to ensure stable convergence and consistent learning. The dual-factor authentication mechanism integrates the OTP-based user authentication and device authentication to guarantee that only legitimate users and trusted devices can join in the collaborative training. The main metrics for experimental evaluation were prediction accuracy, convergence speed, and security performance. The proposed framework achieved prediction accuracies of ~100%, ~100%, and ~98% across the three participating clients after 50 training epochs, showing faster convergence and more stable learning than a conventional federated learning baseline. Moreover, the authentication mechanism successfully resists unauthorized access with low computational and communication overhead. The results demonstrate that the proposed framework not only improves the accuracy and security of federated next-word prediction but also enhances the trustworthiness, reliability, and practical deployment of privacy-preserving language prediction systems in distributed edge environments.
Energy preservation remains a crucial challenge in the Heterogeneous Wireless Sensor Networks (HWSNs) deployment. The uneven distribution of primary energy leads to precocious node depletion and network segmentation. Five intelligent routing and clustering optimization strategies tailored for dynamic and heterogeneous environments are presented and comprehensively compared in this paper: the proposed Binary-Chromosome Genetic Algorithm (BC-GA), Grey Wolf Optimization (GWO), Deep Q-Learning (DQL), Distributed Energy-Efficient Clustering (DEEC) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS). The simulations are performed on a 100×100 m² field with 50 randomly deployed nodes having different initial energy levels (0.507- 0.986 J) communicating with a Base Station. Empirical results obtained from five trial Monte Carlo simulations expose excellent performance trade-offs. BC-GA achieves an outstanding Last Node Dead (LND) of 5339.60 ± 157.57 rounds and maximum throughput of 166,219.40 ± 2534.72 packets. Among the classical baselines, PEGASIS achieves an LND of 3095.20 ± 212.68 rounds and throughput of 120,742.00. On the other hand, GWO and DQL have accelerated depletion phases with FND 432.40 ± 62.15 and 568.80 ± 48.30 rounds, respectively, due to extreme pressure on the bottleneck node near the Base Station. The End-to-End (E2E) delay provided by DEEC is the minimum (11.49 ± 0.36 ms) among all methods and the Energy-Delay Product (EDP) is the best (6.09 ± 0.38) which makes DEEC very suitable for latency-critical environments. Those results prove that the evolutionary cluster-head structuring provided top-level load balance and an extension of lifetime for intensive data monitoring.
Face recognition systems are increasingly deployed in security-critical applications, but remain vulnerable to presentation attacks such as printed photographs, replay videos, and three-dimensional masks. Although recent face anti-spoofing methods exploit complementary information from RGB, depth, and near-infrared (NIR) representations, existing approaches primarily focus on feature fusion and do not explicitly model the intrinsic consistency relationships among these representations. This limitation can reduce robustness against sophisticated spoofing attacks and cross-dataset variations. This paper proposes a novel Cross-Modal Consistency Learning (CMCL) framework for robust face anti-spoofing using the original RGB input together with RGB-derived depth and NIR representations. The depth and NIR representations are constructed from RGB inputs during the modality preprocessing stage and subsequently processed together with RGB using independent Swin Transformer encoders. A cross-modal attention fusion module adaptively integrates complementary appearance, geometric, and spectral information, while a consistency learning module explicitly encourages feature coherence for genuine samples and emphasizes cross-modal discrepancies associated with spoof attacks. The consistency module is used only during training and removed during inference, avoiding additional deployment overhead. Extensive experiments are conducted on CASIA-SURF, WMCA, CelebA-Spoof, and MSU-MFSD using intra-dataset, cross-dataset, ablation, feature-space, modality-consistency, and statistical analyses. The proposed CMCL achieves an ACER of 0.70% and accuracy of 99.3% on CASIA-SURF, while achieving an ACER of 2.55% and accuracy of 97.9% on WMCA. The results demonstrate improved spoof detection performance, representation discrimination, and cross-dataset generalization compared with the evaluated state-of-the-art methods. The proposed framework provides a consistency-driven and computationally practical approach for robust face anti-spoofing.
