Hilal Hudan Nuha

Proactive prediction of Service Level Agreement (SLA) satisfaction lets 6G radio access networks act before a link misses its throughput and latency targets. This paper proposes Pseudo Gradient Teaching Learning Based Optimization (PGTLBO), which adds to the teacher step the direction from the worse half to the better half of the ranked population, and uses it to train a multilayer perceptron. All trainers receive the same number of function evaluations, and a random direction control isolates the effect of the direction. On 6G SLA prediction for unseen clients, PGTLBO reaches 0.846 accuracy, tied with standard TLBO (0.844), and outperforms the genetic algorithm, particle swarm optimization, and Harris hawks optimization on all 20 splits. PGTLBO reaches a lower training loss than TLBO in 57 of 80 paired runs and its direction beats a random one, but test accuracy is limited by the data.

Virtual reality (VR) services delivered through UAV-enabled mobile edge computing (MEC) require joint decisions on user association, caching, computing-resource allocation, and UAV placement. This study benchmarks three CVX-free placement solvers, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and gradient-based L-BFGS-B, within the same alternating-optimization framework for N ∈ {15, 18, 21, 24, 27, 30} VR users served by M = 2 UAV base stations (UBSs). The channel rate follows the outage-constrained logistic approximation of an elevation-angle-dependent Rician fading model. To account for stochastic variability, the evaluation uses 30 independent Monte Carlo system realizations for every N , paired across solvers, and includes random and centroid placement baselines. The optimized maximum aggregate UBS task-completion latency increases from approximately 225 ms at N = 15 to 723 ms at N = 30. PSO and GA achieve numerically identical latency values, while L-BFGS-B yields comparable latency at each tested user load, differing by at most 0.714 ms in any paired comparison; after Holm correction, only the N = 15 difference is statistically significant and its median magnitude is only 0.054 ms. Relative to random placement, optimization reduces mean aggregate latency by approximately 21–33%, whereas the gain over centroid placement is modest (about 0.26–0.47%). L-BFGS-B has the lowest average runtime over all loads, although PSO is faster at several individual load levels, and GA is consistently the slowest. The reported objective is an aggregate task-level latency metric inherited from the reference UAV-MEC model and must not be interpreted as per-frame motion-to-photon latency

Traffic congestion and travel time uncertainty remain major challenges to the operational efficiency of Bus Rapid Transit (BRT) systems in urban areas of developing countries. This study proposes an integrated solution for the Trans Metro Bandung (TMB) system by leveraging Internet of Things (IoT)–based GPS data and tree-based ensemble machine learning algorithms. Spatio-temporal data collected from on-board GPS modules are processed to predict traffic congestion levels and estimate travel time across route segments. The performance of Decision Tree, Random Forest, and XGBoost models is evaluated in terms of prediction accuracy, interpretability, and computational efficiency, with particular consideration for deployment on resource-constrained hardware. Experiments conducted on 20,156 data samples show that the Decision Tree model achieves the highest congestion classification accuracy of 96.8%, while Random Forest outperforms other models in travel time regression, achieving an R² value of 0.95 and a root mean square error (RMSE) of 5.80 min. The trained models are successfully deployed on a Raspberry Pi 3B microcontroller for real-time inference, enabling fleet management and travel planning without reliance on cloud connectivity. The results demonstrate that cost-effective and interpretable machine learning solutions can deliver reliable performance in heterogeneous urban infrastructures while providing a replicable framework for medium-sized cities seeking to implement affordable smart transportation systems.

The flourishing of the Internet of Things (IoT) has not only improved our lives in smart homes and healthcare but also made us more susceptible to cyberattacks. Legacy intrusion detection systems are simply overwhelmed by the scale and diversity of IoT traffic, which is why there is a need for more intelligent forensic solutions. In this paper, we present a statistical technique, the Averaging Detection Method (ADM), for detecting attack traffic. Furthermore, the five deep learning models SimpleRNN, LSTM, GRU, BLSTM, and BGRU are compared for malicious traffic detection in IoT network forensics. A smart home dataset with a simulated DoS attack was used for performance analysis of accuracy, precision, recall, F1-score, and training time. The results indicate that all models achieve high accuracy, above 97%. BiGRU achieves the best performance, 99% accuracy, precision, recall, and F1-score, at the cost of high training time. GRU achieves perfect precision and recall (100%) with faster training, which can be considered for resource-constrained scenarios. SimpleRNN trains faster with comparable accuracy, while LSTMs and their bidirectional counterparts are better at capturing long-term dependencies but are computationally more expensive. In summary, deep learning, especially BiGRU and GRU, holds great promise for boosting IoT forensic investigation by enabling real-time DoS detection and reliable evidence collection. Meanwhile, the proposed ADM is simpler and more efficient at classifying DoS traffic than deep learning models.

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of −4.21, compared to 1.22 for standard GWO, −2.81 for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs.

JCM is an open access journal on the science and engineering of communication.

Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens’ sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments’ challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government’s diverse roles in privacy preservation—regulator, enforcer, user, and service provider—and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning method for flooding attack detection in OBS networks, based on a third-order distance function for k-nearest neighbors (KNN3O). The proposed distance is expected to improve detection accuracy due to higher sensitivity with respect to the distance difference between two points. The developed method is compared with seven other machine learning methods, namely standard KNN, KNN with cosine distance (KNNC), multi-layer perceptron (MLP), naive Bayes classifier (NBC), support vector machine (SVM), decision tree (DT), and discriminant analysis classifier (DAC). The methods are further assessed using five metrics: accuracy, precision, recall, F1-score, and specificity. The proposed method achieved an accuracy of 99.3%, outperforming the original KNN, MLP, and SVM, which achieved accuracies of 99%, 76.4%, and 94.7%, respectively. The results show that KNN3O is the best method for flooding attack detection in OBS networks, as it achieves the highest scores in all five metrics.