The accurate localization of nodes in wireless sensor networks (WSNs) can be jeopardized in certain applications of fields, such as those in security and military. In order to detect the nodes that are captured as malicious anchors, a malicious node detection algorithm named MNDC and its improved version named EMDC are proposed in this paper. The algorithms utilize the density-based spatial clustering to acquire the abnormal clusters, which are further examined via a sequential probability ratio test. The malicious nodes that jeopardize networks are then determined. The conducted simulation results and corresponding analysis demonstrate that the proposed algorithms outperform other state-of-art schemes in terms of detection accuracy and effectiveness.

This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of “excessive strangulation,” and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.

Vehicle mobile Internet of Things uses sensor technology, mobile Internet technology, and intelligent computing technology to effectively monitor and provide comprehensive services for vehicle operation status. It is an important part of building a smart city, making urban transportation more efficient, environmentally friendly, intelligent, and safety. In the vehicle mobile Internet of Things, when in-vehicle sensors are in communication with each other, they are often affected by factors, such as network mobility, transmission range, and signal interference. Therefore, in order to ensure the continuity of communication between nodes and improve the quality of network communication, this paper proposes a vehicle mobile Internet of Things coverage enhancement algorithm (PANM). First, we derive the probabilistic analysis model of communication duration by analyzing the functional relationship of vehicle initial velocity, acceleration, spacing distance, and communication duration. Then, under the premise of ensuring the communication duration, in order to improve the coverage of the vehicle mobile Internet of Things, the network overlap ratio is introduced in the probability analysis model. Finally, we improve the network coverage performance of omnidirectional radiation and fan-shaped radiation communication models of vehicle mobile Internet of Things by limiting the overlap ratio threshold. The experimental simulation results indicate that the PANM algorithm can reduce the packet loss rate of the vehicle network and have a shorter communication delay.

Knowledge graphs (KGs) are structured knowledge bases that represent information as a collection of interconnected entities and relations. Link prediction in KGs aims to infer missing or potential links between entities based on triple facts. Among different link prediction methods, knowledge graph embedding (KGE) has gained widespread popularity, with the goal of learning low-dimensional representations for KGs. However, most present KGE methods struggle to capture both local and global neighborhood information efficiently. Additionally, many hybrid methods have limitations in modeling and capturing interactions between triples. In this article, we propose an entity-relation-guided random walk (ERGRW) method for link prediction in KGs. Unlike conventional approaches that solely focus on entity-based walks, ERGRW creatively introduces relations as objects to walk as well. Inspired by distance-based methods, we design novel random walk rules based on the translation principle within triples. Thus, the ERGRW not only captures local and global neighborhood information but also discovers potential semantic relationships and interactions in the KGs. Furthermore, the encoder–decoder framework of ERGRW is able to learn comprehensive representation and improve link prediction performance. Extensive experiments conducted on four standard datasets demonstrate the superiority of ERGRW for link prediction.

Target tracking is prone to problems such as target loss and identity jump when the target is occluded and the attitude is changed. In order to solve this phenomenon, this paper proposes the adaptive adjustment object detection algorithm under multiple mechanisms based on GAN. This algorithm introduces a gradient penalty mechanism to the discriminator and uses the relative discriminator structure to reconstruct the discriminator, so as to improve the discriminatory ability of the discriminator. Then, through the feedback mechanism, the obtained data is fed back to the generator network in time, and the genetic mechanism is used to speed up the positioning of the key areas of the image. Experimental results show that compared with other existing algorithms, this algorithm can effectively locate and distinguish under different environments. And, the target still maintains a high resolution. When the target is occluded, it can effectively avoid the phenomenon of target loss and identity jump.