Traffic sign recognition techniques aim to reduce the probability of traffic accidents by increasing road and vehicle safety. These systems play an essential role in the development of autonomous vehicles. Autonomous driving is a popular field that is seeing more and more growth. In this study, a new high-performance and robust deep convolutional neural network model is proposed for traffic sign recognition. The stacking ensemble model is presented by combining the trained models by applying improvement methods on the input images. For this, first of all, by performing preprocessing on the data set, more accurate recognition was achieved by preventing adverse weather conditions and shooting errors. In addition, data augmentation was applied to increase the images in the data set due to the uneven distribution of the number of images belonging to the classes. During the model training, the learning rate was adjusted to prevent overfitting. Then, a new stacking ensemble model was created by combining the models trained with the input images that were subjected to different preprocessing. This ensemble model obtained 99.75% test accuracy on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. When compared with other studies in this field in the literature, it is seen that recognition is performed with higher accuracy than these studies. Additively different approaches have been applied for model evaluation. Grad-CAM (gradient weighted class activation mapping) was used to make the model explainable. Evidential deep learning approach was applied to measure the uncertainty in classification. Results for safe monitoring are also shared with SafeML-II, which is based on measuring statistical distances. In addition to these, the migration test is applied with BTSC (Belgium Traffic Sign Classification) dataset to test the robustness of the model. With the transfer learning method of the models trained with GTSRB, the parameter weights in the feature extraction stage are preserved, and the training is carried out for the classification stage. Accordingly, with the stacking ensemble model obtained by combining the models trained with transfer learning, a high accuracy of 99.33% is achieved on the BTSC dataset. While the number of parameters the single model is 7.15 M, the number of parameters of the stacking ensemble model with additional layers is 14.34 M. However, the parameters of the models trained on a single preprocessed dataset were not trained, and transfer learning was performed. Thus, the number of trainable parameters in the ensemble model is only 39,643.

In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model’s performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes.

Localization, essential in WSN applications, enables sensor nodes to determine their physical positions by referencing anchor nodes. We evaluate broadcast and unicast packet transmissions at the data-link layer for their impact on localization performance. Implemented on the Contiki-NG operating system, the study examines how anchor node density and antenna range affect localization success and the number of required anchor nodes between broadcast-based and unicast-based localization propagation in protocol stack. Results using Cooja simulator, demonstrate the trade-offs between unicast and broadcast transmission approaches, particularly in terms of network overhead, energy consumption, and localization performance. For instance, with an antenna range of 20 meters, achieving a localization ratio of over 90% requires only 20% anchor density with broadcast transmission, whereas unicast transmission requires a 60% anchor density to achieve the same ratio. This demonstrates that broadcast localization can lead to approximately a 33% reduction in hardware costs, offering significant efficiency gains. These findings provide insights into optimal propagation techniques and highlight the advantages of broadcasting in resource-constrained WSN deployments.

Wireless Sensor Networks (WSNs) have emerged as a foundational technology for monitoring and data collection in diverse domains such as environmental sensing, smart agriculture, and industrial automation. Precise node localization plays a vital role in WSNs, enabling effective data interpretation, reliable routing, and spatial context awareness. The challenge intensifies in range-free settings, where a lack of direct distance data demands efficient indirect estimation methods, particularly in large-scale, energy-constrained deployments. This work proposes a hybrid localization framework that integrates the distance vector-hop (DV-Hop) range-free localization algorithm with the Fire Hawk Optimizer (FHO), a nature-inspired metaheuristic method inspired by the predatory behavior of fire hawks. The proposed FHODV-Hop method enhances location estimation accuracy while maintaining low computational overhead by inserting the FHO into the third stage of the DV-Hop algorithm. Extensive simulations are conducted on multiple topologies, including random, circular, square-grid, and S-shaped, under various network parameters such as node densities, anchor rates, population sizes, and communication ranges. The results show that the proposed FHODV-Hop model achieves competitive performance in Average Localization Error (ALE), localization ratio, convergence behavior, computational, and runtime efficiency. Specifically, FHODV-Hop reduces the ALE by up to 35% in random deployments, 25% in circular networks, and nearly 45% in structured square-grid layouts compared to the classical DV-Hop. Even under highly irregular S-shaped conditions, the algorithm achieves around 20% improvement. Furthermore, convergence speed is accelerated by approximately 25%, and computational time is reduced by nearly 18%, demonstrating its scalability and practical applicability. Therefore, these results demonstrate that the proposed model offers a promising balance between accuracy and practicality for real-world WSN deployments.