Research of target localization and tracking is always a remarkable problem in the application of wireless sensor networks (WSNs) technology. There are many kinds of research and applications of target localization and tracking, such as Angle of Arrival (AOA), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The target localization accuracy for TOA, TDOA, and AOA is better than RSS. However, the required devices in the TOA, TDOA, and AOA are more expensive than RSS. In addition, the computational complexity of TOA, TDOA, and AOA is also more complicated than RSS. This paper uses a particle swarm optimization (PSO) algorithm with the received signal strength index (RSSI) channel model for indoor target localization and tracking. The performance of eight different method combinations of random or regular points, fixed or adaptive weights, and the region segmentation method (RSM) proposed in this paper for target localization and tracking is investigated for the number of particles in the PSO algorithm with 12, 24, 52, 72, and 100. The simulation results show that the proposed RSM method can reduce the number of particles used in the PSO algorithm and improve the speed of positioning and tracking without affecting the accuracy of target localization and tracking. The total average localization time for target localization and tracking with the RSM method can be reduced by 48.95% and 34.14%, respectively, and the average accuracy of target tracking reaches up to 93.09%.

The Internet of Things (IoT) system has been a hot topic in recent years. Its operation is a system that stores data in data storage and is completed by the exchange of network information about things. Therefore, the security of information between network transmissions is very important. In recent years, the most likely cause of information security problems has been a distributed denial of service (DDoS) attack. In this paper, we proposed an autonomous defense system that combines edge computing with a two-dimensional convolutional neural network (CNN) to recognize whether the data server in IoT suffers from DDoS attacks and identify the attack mode. The accuracy of trained two-dimensional CNN is up to 99.5% and 99.8% for packet traffic and packet features training, respectively. A field experiment’s results show that the data server in the proposed system can effectively distinguish the difference between the DDoS attacks and the normal transmission to reduce the impact of DDoS attacks on the IoT data storage while it is under attack.

In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). This paper uses an artificial fish swarm algorithm (AFSA) and the received signal strength indicator (RSSI) channel model for indoor target positioning and tracking. The performance of eight different method combinations of fixed or adaptive steps, the region segmentation method (RSM), Hybrid Adaptive Vision of Prey (HAVP) method, and a Dynamic AF Selection (DAFS) method proposed in this paper for target positioning and tracking is investigated when the number of artificial fish is 100, 72, 52, 24, and 12. The simulation results show that using the proposed HAVP total average positioning error is reduced by 96.1%, and the positioning time is shortened by 26.4% for the target position. Adopting HAVP, RSM, and DAFS in target tracking, the positioning time can be greatly shortened by 42.47% without degrading the tracking success rate.

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study presents a systematic benchmark of five deep convolutional neural networks (CNNs)—Visual Geometry Group (VGG)16, VGG19, Residual Network (ResNet)101V2, Xception, and Densely Connected Convolutional Network (DenseNet)121—for rice disease identification using a public leaf image dataset. The models, initialized with ImageNet pre-trained weights, were rigorously evaluated under a unified framework, including 5-fold cross-validation and a challenging out-of-distribution (OOD) generalization test. Our results demonstrate a clear performance hierarchy, with DenseNet121 emerging as the superior model. It achieved the highest OOD accuracy and F1-score (both 85.08%) while exhibiting the greatest parameter efficiency (8.1 million parameters), making it ideally suited for edge deployment. In contrast, architectures with large fully connected layers (VGG) or less efficient feature learning mechanisms (Xception, ResNet101V2) showed lower performance in this specific task. This study confirms the critical impact of architectural design choices, provides a reproducible performance baseline, and identifies DenseNet121 as a robust, efficient, and highly recommendable CNN for practical rice disease diagnosis in precision agriculture.

3D MIMO introduces the vertical dimension of the antenna downtilt angle to make the direction of signal transmission more accurate to improve system capacity. In this paper, we verify the effect of antenna downtilt angle on channel capacity through simulations of four fixed antenna downtilt angles, 90, 96, 99, and 102 degrees under the conditions that the distance between mobile station (MS) and base station (BS) is 250 m, and the heights of antenna in BS and MS are 25 m and 1.5 m, respectively. The simulation results show that the antenna downtilt angle of 96 degrees has a larger channel capacity than the others. In addition, we proposed an adaptive optimization method by applying the Q-learning algorithm to adaptively optimize the antenna downtilt angles to maximize system capacity. The performance of the proposed method is to investigate the Q-learning algorithm with three different discount rates at 0.9, 0.5, and 0.1, and four different propagation distances on 20 × 1 and 60 × 4 MIMO. We demonstrate that there is only a 1% difference between the adaptively optimized antenna downtilt angle and the ideal optimal antenna downtilt angle when the discount rate of Q-learning algorithm is 0.9, and its channel capacity performance can reach more than 99.72% of the ideal optimal one.