Internet Technology Letters最新文献

The paper focused on researching and analyzing computer intelligence network security and preventive measures in the context of the IoT, aiming to improve the security coefficient of the IoT network and reduce IoT network security accidents through computer intelligence technology. Through experiments, we obtained data that demonstrated the effectiveness of computer intelligence in improving IoT security. In several groups of experiments, the maximum number of information leaks in the IoT network using computer intelligence within a month was 10 times smaller than the maximum number in traditional IoT networks, and the minimum number was 8 times smaller. This shows that computer intelligence can prevent information leakage in the IoT. Similarly, in several groups of experiments, the maximum number of data thefts in a month in the IoT network using computer intelligence was 15 times smaller than the maximum number in traditional IoT networks, and the minimum number was 16 times smaller. This demonstrates that computer intelligence can prevent data theft in the IoT. These findings confirm that computer intelligence can improve the security of the IoT network.

With the rapid development of Internet of Things (IoT) technology, electricity collection systems have been widely used in various fields. It can connect various items to the internet and achieve remote monitoring and maintenance of devices. During the operation of the electricity collection system, there may be issues such as misalignment due to various reasons, which can lead to errors in data collection and affect the accuracy and stability of the system. How to timely monitor the operational errors of the system and replace and repair them has become an urgent problem to be solved. The fault location algorithm can accurately diagnose the cause of the fault and provide corresponding repair suggestions, thereby reducing maintenance costs and optimizing the efficiency of the electricity collection system. This article would analyze the monitoring method for the misalignment replacement operation error of the power acquisition system based on IoT engineering analysis, and used fault location algorithms to locate its misalignment. The research results indicated that, under the same other conditions, the total satisfaction score of the X system was 253 points, and the total satisfaction score of the Y system was 141 points. The score of the X system was much higher than that of the Y system. The results indicated that IoT engineering analysis could optimize the monitoring method for operational errors caused by inaccurate replacement of electricity acquisition systems, and there was a positive relationship between the two.

Due to the growth of neural networks, the semantic big data analysis method can classify images at the pixel level, which is very suitable for the needs of IoT. In semantic big data analysis methods, the DeepLab algorithm is an improved and highly accurate algorithm based on enhanced neural networks. However, the DeepLab algorithm does not fully utilize global information, resulting in poor performance for complex scenes. Therefore, this article makes improvements by introducing a global context information module and providing prior information of complex scenes in images. It extracts global information and merges with original features. It improves the expression ability of features. This global context can enhance the accuracy of semantic big data analysis method, and an attention mechanism is designed. The experimental results display that the improved DeepLab semantic big data analysis method based on self-attention and global context module has good average pixel accuracy and average intersection to union ratio performance on the Pascal VOC 2012 dataset. And the improvement effect is significant.

In the digital economy era, efficiently leveraging the vast amount of sensor data generated by the Industrial Internet of Things (IIoT) is essential. This paper presents an innovative semantic annotation method for industrial sensor data, designed to optimize data processing and enhance system efficiency. Our method combines cluster analysis, ontology development, and rule-based reasoning to automatically annotate IIoT sensory data. By utilizing data aggregation and filtering mechanisms, which incorporate the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and a rule engine, we significantly reduce the data volume required for annotation. The Semantic Web Rule Language aids in naming new concepts and properties identified through clustering, contributing further to the automation of data processing. Experimental results, using public datasets, validate the effectiveness of our method, showing a reduction in data volume by about 20% and underscoring its potential in enhancing industrial systems' automation and overall efficiency.

This study integrates multi-node wearable sensor data to improve music performance skills. A window-adding method is used during time-frequency feature extraction. By incorporating kernel functions, we present a generalized discriminant analysis (GDA) method to reduce the high-dimensional sensor features while retaining performance traits. Experiments demonstrate that the proposed GDA approach achieves higher accuracy (92.71%), precision (90.54%), and recall (88.68%) compared to linear discriminant analysis (82.39% accuracy) and principal component analysis (88.56% accuracy) in classifying motions performed by music performers. The integrated analysis of wearable sensor data facilitates comprehensive feedback to strengthen proficiency across various music performance skills.

Recent advances in Semantic IoT data integration have highlighted the importance of multimodal fusion in emotion recognition systems. Human emotions, formed through innate learning and communication, are often revealed through speech and facial expressions. In response, this study proposes a hidden Markov model-based multimodal fusion emotion detection system, combining speech recognition with facial expressions to enhance emotion recognition rates. The integration of such emotion recognition systems with Semantic IoT data can offer unprecedented insights into human behavior and sentiment analysis, contributing to the advancement of data integration techniques in the context of the Internet of Things. Experimental findings indicate that in single-modal emotion detection, speech recognition achieves a 76% accuracy rate, while facial expression recognition achieves 78%. However, when state information fusion is applied, the recognition rate increases to 95%, surpassing the national average by 19% and 17% for speech and facial expressions, respectively. This demonstrates the effectiveness of multimodal fusion in emotion recognition, leading to higher recognition rates and reduced workload compared to single-modal approaches.

The Internet of Things (IoT) technology can currently enable devices and systems in various fields to achieve interconnectivity, intelligence, and automation, which is significant for improving daily life. It connects objects through the Internet, achieving information exchange and sharing, bringing many conveniences to humanity, and improving the efficiency and quality of various industries. However, precisely because everything is interconnected, most IoT systems have high data throughput, which leads to issues such as reduced operational efficiency of IoT systems. Therefore, this article used digital twin (DT) technology to aggregate multi-source heterogeneous data of the IoT, overcoming the problems of diversity and differences in massive data, and thus accelerating the system's data processing. Moreover, in the end of this article, an experiment was conducted on the IoT system of a certain university. Taking the system running 10 times as an example, the packet loss rate of the experimental group using DT technology was only 3.48%, while the packet loss rate of the control group running alone was 4.36%. This indicates that DT technology has improved the performance of the IoT system. This study highlights the role of digital twin technology in solving the low operational efficiency, diverse data, and data differences in data aggregation of the Internet of Things. It plays a significant role in improving the operational efficiency of the Internet of Things and improving the performance of the Internet of Things system.

With the development of information technology, network data security issues have received widespread attention. Under traditional wired communication networks, it not only has high installation and maintenance costs, requires a lot of manpower and resources, but also has low security performance, which is not significant in improving network data security. How to improve network data security, prevent data from being maliciously leaked, stolen, and effectively transmitted in the network, is a major challenge facing today's society. Wireless communication technology has brought new ideas to improve network data security. This article analyzed the role of wireless communication technology in improving network data security and selected 30 enterprises as the research objects. This article studied the role of traditional wired communication technology and wireless communication technology in network data security protection and compared several indicators based on virus detection rate, defense rate, data transmission speed, and customer satisfaction. The experimental results showed that the average detection rate of Trojan viruses based on wireless communication technology was 80.8%, and the average defense rate against viruses using antivirus software was 58%. In the case of virus attacks, the average transmission speed was 2.4 s, and the average satisfaction of users with wireless communication technology in virus defense was 6.9. It indicates that wireless communication technology has significantly improved virus detection rate, defense rate, data transmission speed, and customer satisfaction in the mode of improving network data security. This technology has significant significance and value for users.

It is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing-based Beetle Swarm Optimisation Algorithm (SA-BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA-BSO combines simulated annealing with the beetle search process. Through inter-swarm communication in every generation, SA-BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA-BSO's efficacy. Statistics show that SA-BSO-based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.