The Internet of Things (IoT) enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks (WSNs). The open architecture and resource constraints of wireless sensor networks (WSNs) make them highly vulnerable to internal security threats caused by malicious or compromised nodes, particularly in Internet of Things (IoT) environments. To address this issue, we proposed Dynamic Trust Evaluation Model (DTEM), designed to provide a secure, scalable, and efficient framework for IoT-based WSNs. The proposed model identifies the role of trust management in routing, data aggregation, and intrusion detection, including trust-based protocols. DTEM incorporates a lightweight elliptic curve cryptography (ECC) mechanism to ensure secure communication, protect trust information from manipulation, and enhance overall system reliability. In addition, machine learning techniques are employed to improve malicious node classification accuracy. Component-wise analysis demonstrates that the dynamic trust evaluation forms the core detection mechanism, while ECC enhances communication security and machine learning improves malicious node classification accuracy. A large-scale network simulation is conducted to evaluate DTEM’s performance under various attack scenarios. Results demonstrate improved malicious node detection accuracy, higher packet delivery ratios, reduced energy consumption, and lower communication overheads. The proposed DTEM framework proves to be a robust and scalable solution for securing IoT-based wireless sensor networks, making it suitable for real-world applications.

Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems, particularly in dynamic and unstructured environments. While recent advancements in deep learning have significantly enhanced road scene classification, simultaneously achieving high accuracy, computational efficiency, and adaptability across diverse conditions continues to be difficult. To address these challenges, this study proposes HybridLSTM, a novel and efficient framework that integrates deep learning-based, object-based, and handcrafted feature extraction methods within a unified architecture. HybridLSTM is designed to classify four distinct road scene categories—crosswalk (CW), highway (HW), overpass/tunnel (OP/T), and parking (P)—by leveraging multiple publicly available datasets, including Places-365, BDD100K, LabelMe, and KITTI, thereby promoting domain generalization. The framework fuses object-level features extracted using YOLOv5 and VGG19, scene-level global representations obtained from a modified VGG19, and fine-grained texture features captured through eight handcrafted descriptors. This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues, which are critical for robust scene understanding. To model spatial arrangements and latent sequential dependencies present even in static imagery, the combined features are processed through a Long Short-Term Memory (LSTM) network, allowing the extraction of discriminative patterns across heterogeneous feature spaces. Extensive experiments conducted on 2725 annotated road scene images, with an 80:20 training-to-testing split, validate the effectiveness of the proposed model. HybridLSTM achieves a classification accuracy of 96.3%, a precision of 95.8%, a recall of 96.1%, and an F1-score of 96.0%, outperforming several existing state-of-the-art methods. These results demonstrate the robustness, scalability, and generalization capability of HybridLSTM across varying environments and scene complexities. Moreover, the framework is optimized to balance classification performance with computational efficiency, making it highly suitable for real-time deployment in embedded autonomous driving systems. Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.

In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is developed using multiple types of data and an advanced deep-learning approach. Firstly, the required signals and sensor data are accumulated from the standard databases. From these signals, the wave features are retrieved. Then the extracted wave features and sensor data are given as the input to recognize the human activity. An Adaptive Hybrid Deep Attentive Network (AHDAN) is developed by incorporating a “1D Convolutional Neural Network (1DCNN)” with a “Gated Recurrent Unit (GRU)” for the human activity recognition process. Additionally, the Enhanced Archerfish Hunting Optimizer (EAHO) is suggested to fine-tune the network parameters for enhancing the recognition process. An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model. The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36, 95.25 for recall, 95.48 for specificity, and 95.47 for precision, respectively. The result proved that the developed model is effective in recognizing human action by taking less time. Additionally, it reduces the computation complexity and overfitting issue through using an optimization approach.