Wireless sensor networks (WSNs) are a cornerstone of modern Internet of Things (IoT) infrastructure, enabling seamless data collection and communication for many IoT applications. However, the deployment of WSNs in remote or inaccessible locations poses significant challenges in terms of energy efficiency and secure communication. Sensor nodes, with their limited battery capacities, require innovative strategies to minimize energy consumption while maintaining robust network performance. Additionally, ensuring secure data transmission is critical for safeguarding the integrity and confidentiality of IoT systems. Despite various advancements, existing methods often fail to strike an optimal balance between energy efficiency and quality of service (QoS), either depleting limited energy resources or compromising network performance. This paper introduces a novel framework that integrates double reconfigurable intelligent surfaces (RISs) into WSNs to enhance energy efficiency while ensuring secure communication. To jointly optimize both RIS phase shift matrices, we employ a fuzzy deep reinforcement learning (FDRL) framework that integrates reinforcement learning (RL) with fuzzy logic and long short-term memory (LSTM)-based architecture. The RL component learns optimal actions by iteratively interacting with the environment and updating Q-values based on a reward function that prioritizes both energy efficiency and secure communication. The LSTM captures temporal dependencies in the system state, allowing the model to make more informed predictions about future network conditions, while the fuzzy logic layer manages uncertainties by using optimized membership functions and rule-based inference. To explore the search space efficiently and identify optimal parameter configurations, we use the advantage of the multi-objective artificial bee colony (MOABC) algorithm as an optimization strategy to fine-tune the hyperparameters of the FDRL framework while simultaneously optimizing the membership functions of the fuzzy logic system to improve decision-making accuracy under uncertain conditions. The MOABC algorithm enhances convergence speed and ensures the adaptability of the proposed framework in dynamically changing environments. This framework dynamically adjusts the RIS phase shift matrices, ensuring robust adaptability under varying environmental conditions and maximizing energy efficiency and secure data throughput. Simulation results validate the effectiveness of the proposed FDRL-based double RIS framework under different system configurations, demonstrating significant improvements in energy efficiency and secrecy rate compared to existing methods. Specifically, quantitative analysis demonstrates that the FDRL framework improves energy efficiency by 35.4%, the secrecy rate by 29.7%, and RSMA by 27.5%, compared to the second-best approach. Additionally, the model achieves an R² score improvement of 12.3%, confirming its superior predictive accuracy.

Intrusion detection in the Internet of Things (IoT) environments is increasingly critical due to the rapid proliferation of connected devices and the growing sophistication of cyber threats. Traditional detection methods often fall short in identifying multi-class attacks, particularly in the presence of high-dimensional and imbalanced IoT traffic. To address these challenges, this paper proposes a novel hybrid intrusion detection framework that integrates transformer networks with generative adversarial networks (GANs), aiming to enhance both detection accuracy and robustness. In the proposed architecture, the transformer component effectively models temporal and contextual dependencies within traffic sequences, while the GAN component generates synthetic data to improve feature diversity and mitigate class imbalance. Additionally, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm is employed to fine-tune the hyper-parameters of the hybrid model, further enhancing learning stability and detection performance. The model is trained and evaluated on the CIC-IoT-2023 and TON_IoT dataset, which contains a diverse range of real-world IoT traffic and attack scenarios. Experimental results show that our hybrid framework consistently outperforms baseline methods, in both binary and multi-class intrusion detection tasks. The transformer-GAN achieves a multi-class classification accuracy of 99.67%, with an F1-score of 99.61%, and an area under the curve (AUC) of 99.80% in the CIC-IoT-2023 dataset, and achieves 98.84% accuracy, 98.79% F1-score, and 99.12% AUC on the TON_IoT dataset. The superiority of the proposed model was further validated through statistically significant t-test results, lower execution time compared to baselines, and minimal standard deviation across runs, indicating both efficiency and stability. The proposed framework offers a promising approach for enhancing the security and resilience of next-generation IoT systems.

The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false alarm rates and inefficient fault detection. To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. This optimization ensures improved accuracy, faster convergence, and better generalization to unseen data. The proposed DRL-NSABC model is evaluated using four benchmark datasets: smart grid, advanced metering infrastructure (AMI), smart meter, and Pecan Street, widely recognized in anomaly detection research. A comparative analysis against state-of-the-art deep learning (DL) models, including RL, CNN, RNN, the generative adversarial network (GAN), the time-series transformer (TST), and bidirectional encoder representations from transformers (BERT), demonstrates the superiority of the proposed DRL-NSABC. The proposed DRL-NSABC model achieved high accuracy across all benchmark datasets, including 95.83% on the smart grid dataset, 96.19% on AMI, 96.61% on the smart meter, and 96.45% on Pecan Street. Statistical t-tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. By significantly reducing false positives and ensuring rapid anomaly detection with low computational overhead, the proposed DRL-NSABC framework enables efficient real-world deployment in smart power distribution systems without major infrastructure upgrades and promotes cost-effective, resilient power grid operations.

Terahertz (THz) communication is a key technology for sixth-generation (6G) networks, offering ultra-high data rates, low latency, and massive connectivity. However, the THz band faces significant propagation challenges, including high path loss, molecular absorption, and susceptibility to blockage. Reconfigurable intelligent surfaces (RISs) have emerged as an effective solution to overcome these limitations by reconfiguring the wireless environment through passive beam steering. In this work, we propose a novel framework, namely the optimized deep reinforcement learning transformer (ODRL-Transformer), to maximize the sum rate in RIS-assisted THz systems. The framework integrates a Transformer encoder for extracting temporal and contextual features from sequential channel observations, a DRL agent for adaptive beamforming and phase shift control, and a hybrid biogeography-based optimization (HBBO) algorithm for tuning the hyperparameters of both modules. This design enables efficient long-term decisionmaking and improved convergence. Extensive simulations of dynamic THz channel models demonstrate that ODRL-Transformer outperforms other optimization baselines in terms of the sum rate, convergence speed, stability, and generalization. The proposed model achieved an error rate of 0.03, strong robustness, and fast convergence, highlighting its potential for intelligent resource allocation in next-generation RIS-assisted THz networks.