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Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network 利用金字塔扩张注意卷积神经网络增强大规模 5G MIMO-OFDM 系统的信道预测能力
Internet Technology Letters Pub Date : 2024-05-07 DOI: 10.1002/itl2.532
C. R. Rathish, Balakrishnan Manojkumar, Lakshmanaperumal Thanga Mariappan, Panchapakesan Ashok, Udayakumar Arun Kumar, Krishnan Balan
{"title":"Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network","authors":"C. R. Rathish, Balakrishnan Manojkumar, Lakshmanaperumal Thanga Mariappan, Panchapakesan Ashok, Udayakumar Arun Kumar, Krishnan Balan","doi":"10.1002/itl2.532","DOIUrl":"https://doi.org/10.1002/itl2.532","url":null,"abstract":"In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Safety protection using artificial intelligence internet of things for preschool education 利用人工智能物联网为学前教育提供安全保护
Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.537
Yun Tan, Shuangyuan Mo
{"title":"Safety protection using artificial intelligence internet of things for preschool education","authors":"Yun Tan, Shuangyuan Mo","doi":"10.1002/itl2.537","DOIUrl":"https://doi.org/10.1002/itl2.537","url":null,"abstract":"With the rapid development of social economy and information technology, safety protection in daily life has become more and more important. Although the awareness of safety has increased, the children's safety is still not paid enough attention. Children still may suffer accidental injuries, especially in developing countries. Children spend most of time at school in a day. Thus, it has become an emergent challenge to guarantee children's safety at school. In order handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) safety protection system for preschool education. The AIoT safety protection system consists of three parts: camera, Raspberry Pi, and monitoring computer. The camera captures the images of classroom scene during preschool education. The Raspberry Pi analyzes the images from camera to determine the unsafe behaviors of children, in which a YOLOv8 model is deployed. The monitoring computer receives the alarms from Raspberry Pi. The camera, Raspberry Pi, and monitoring computer are connected using wireless sensor network. The experiments show the behavior recognition model can correctly identify most of dangerous behaviors of children in classroom. The simulation result demonstrates the AIoT safety protection system can find the dangerous behaviors in time.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Performance evaluation of a new Kalman filter based peer‐to‐peer tracking scheme for indoor environment 基于卡尔曼滤波器的新型室内环境点对点跟踪方案的性能评估
Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.529
S. Chattaraj, Amartya Chakraborty, Biplab Das
{"title":"Performance evaluation of a new Kalman filter based peer‐to‐peer tracking scheme for indoor environment","authors":"S. Chattaraj, Amartya Chakraborty, Biplab Das","doi":"10.1002/itl2.529","DOIUrl":"https://doi.org/10.1002/itl2.529","url":null,"abstract":"Peer‐to‐peer tracking through smartphone sensor data is in demand due to its usefulness in location‐based services. A person carrying a smartphone device could be tracked by another smartphone through real time signal processing. Due to the distortion of GPS signals in indoor environment, Kalman filter based data fusion techniques are popularly applied to integrate various sensor data. Such an approach suffers failure in the absence of external aiding and thus entails peer tracking only through the smartphone's navigation sensor data. In this context, accurate estimation of heading error between the leaders and followers' trajectory is very much crucial. The present work demonstrates one novel Kalman filter‐based measurement matching approach for accurate estimation of the aforesaid heading error. Less than 1 meter of accuracy in the final position estimation has been achieved through this method which is comparable with other state of the art techniques as reported in literatures. Moreover, the system does not depend on any external aiding which makes it adaptable to any unknown indoor location.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Air‐ground integrated assisted proactive eavesdropping 空地一体化辅助主动窃听
Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.536
Xianming Wang, Heng Zhang, Yan Ren, Feiran Xu, Chenglong Gong
{"title":"Air‐ground integrated assisted proactive eavesdropping","authors":"Xianming Wang, Heng Zhang, Yan Ren, Feiran Xu, Chenglong Gong","doi":"10.1002/itl2.536","DOIUrl":"https://doi.org/10.1002/itl2.536","url":null,"abstract":"Benefiting from the rapid development of unmanned aerial vehicle (UAV) technology, UAVs have also received extensive attention in the field of communication. In this letter, we investigate an air‐ground proactive eavesdropping system in which a legitimate ground eavesdropper can actively eavesdrop on suspected ground communication links with the assistance of a UAV. To improve the eavesdropping performance of the system, the optimal trajectory of the UAV and the appropriate power allocation ratio are sought to maximize the eavesdropping rate. A Double‐Dueling DQN (D3QN) based scheme for maximizing the eavesdropping rate is proposed through deep reinforcement learning. The joint optimization of UAV trajectory and power allocation ratio is achieved using the D3QN algorithm. From the numerical results, the optimization scheme can improve the eavesdropping rate of the system.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computer intelligent network security and preventive measures of internet of things devices 计算机智能网络安全与物联网设备的预防措施
IF 0.9
Internet Technology Letters Pub Date : 2024-04-24 DOI: 10.1002/itl2.519
Jianfeng Ye, Li Li, Kaiyan Zheng
{"title":"Computer intelligent network security and preventive measures of internet of things devices","authors":"Jianfeng Ye,&nbsp;Li Li,&nbsp;Kaiyan Zheng","doi":"10.1002/itl2.519","DOIUrl":"10.1002/itl2.519","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fault monitoring method for misalignment replacement operation error of electricity acquisition system based on internet of things engineering evaluation 基于物联网工程评估的电力采集系统错位更换操作错误的故障监测方法
IF 0.9
Internet Technology Letters Pub Date : 2024-04-02 DOI: 10.1002/itl2.521
Yinghui Lu, Jiyang Zhu
{"title":"Fault monitoring method for misalignment replacement operation error of electricity acquisition system based on internet of things engineering evaluation","authors":"Yinghui Lu,&nbsp;Jiyang Zhu","doi":"10.1002/itl2.521","DOIUrl":"10.1002/itl2.521","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A semantic big data analysis method based on enhanced neural networks in IoT 基于增强型神经网络的物联网语义大数据分析方法
IF 0.9
Internet Technology Letters Pub Date : 2024-03-28 DOI: 10.1002/itl2.524
Chongke Wang
{"title":"A semantic big data analysis method based on enhanced neural networks in IoT","authors":"Chongke Wang","doi":"10.1002/itl2.524","DOIUrl":"10.1002/itl2.524","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy 面向工业场景效率优化的语义传感器数据标注方法,助力数字经济发展
IF 0.9
Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.508
Na Tao, Tao Zhang
{"title":"Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy","authors":"Na Tao,&nbsp;Tao Zhang","doi":"10.1002/itl2.508","DOIUrl":"https://doi.org/10.1002/itl2.508","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wearing sensor data integration for promoting the performance skills of music in IoT 整合穿戴式传感器数据,提升物联网音乐表演技能
IF 0.9
Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.517
Xiaochan Li, Yi Shi, Daohua Pan
{"title":"Wearing sensor data integration for promoting the performance skills of music in IoT","authors":"Xiaochan Li,&nbsp;Yi Shi,&nbsp;Daohua Pan","doi":"10.1002/itl2.517","DOIUrl":"https://doi.org/10.1002/itl2.517","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multimodal information fusion method in emotion recognition in the background of artificial intelligence 人工智能背景下情绪识别中的多模态信息融合方法
IF 0.9
Internet Technology Letters Pub Date : 2024-03-12 DOI: 10.1002/itl2.520
Zhen Dai, Hongxiao Fei, Chunyan Lian
{"title":"Multimodal information fusion method in emotion recognition in the background of artificial intelligence","authors":"Zhen Dai,&nbsp;Hongxiao Fei,&nbsp;Chunyan Lian","doi":"10.1002/itl2.520","DOIUrl":"10.1002/itl2.520","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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