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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
IF 0.9
Internet Technology Letters Pub Date : 2024-05-07 DOI: 10.1002/itl2.532
Chirakkal Radhakrishnan 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":"Chirakkal Radhakrishnan Rathish,&nbsp;Balakrishnan Manojkumar,&nbsp;Lakshmanaperumal Thanga Mariappan,&nbsp;Panchapakesan Ashok,&nbsp;Udayakumar Arun Kumar,&nbsp;Krishnan Balan","doi":"10.1002/itl2.532","DOIUrl":"10.1002/itl2.532","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"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
IF 0.9
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,&nbsp;Shuangyuan Mo","doi":"10.1002/itl2.537","DOIUrl":"10.1002/itl2.537","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"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
IF 0.9
Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.529
Suvendu Chattaraj, Amartya Chakraborty, Biplab Das
{"title":"Performance evaluation of a new Kalman filter based peer-to-peer tracking scheme for indoor environment","authors":"Suvendu Chattaraj,&nbsp;Amartya Chakraborty,&nbsp;Biplab Das","doi":"10.1002/itl2.529","DOIUrl":"10.1002/itl2.529","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"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
IF 0.9
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,&nbsp;Heng Zhang,&nbsp;Yan Ren,&nbsp;Feiran Xu,&nbsp;Chenglong Gong","doi":"10.1002/itl2.536","DOIUrl":"10.1002/itl2.536","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"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":"7 4","pages":""},"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
Lightweight facial expression estimation for mobile computing in portable device 用于便携式设备移动计算的轻量级面部表情估算
IF 0.9
Internet Technology Letters Pub Date : 2024-04-24 DOI: 10.1002/itl2.533
Jinming Liu
{"title":"Lightweight facial expression estimation for mobile computing in portable device","authors":"Jinming Liu","doi":"10.1002/itl2.533","DOIUrl":"10.1002/itl2.533","url":null,"abstract":"<p>Facial expression recognition has been studied for many years, especially with the development of deep learning. However, the existing researches still have the following two issues. Firstly, the intensity of facial expression is neglected. Secondly, the deep learning based approaches cannot be directly deployed in the devices with limited resources. In order to tackle these two issues, this paper proposes a lightweight facial expression estimation method using a shallow ordinal regression algorithm, which is deployed in a portable smart device for mobile computing in IoTs. Compared with classification based facial expression recognition methods, ordinal regression considers the intensity of facial expression to achieve better mean absolute error (MAE), which is validated by experiments on several public facial expression datasets. The simulation in portable device also demonstrates its effectiveness for mobile computing.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663796","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
Research on the application of English short essay reading emotional analysis in online English teaching under IoT scenario 物联网场景下英语短文阅读情感分析在在线英语教学中的应用研究
IF 0.9
Internet Technology Letters Pub Date : 2024-04-24 DOI: 10.1002/itl2.535
Xiaoli Zhan
{"title":"Research on the application of English short essay reading emotional analysis in online English teaching under IoT scenario","authors":"Xiaoli Zhan","doi":"10.1002/itl2.535","DOIUrl":"10.1002/itl2.535","url":null,"abstract":"<p>Speech-emotion analysis plays an important role in English teaching. The existing convolutional neural networks (CNNs) can fully explore the spatial features of speech information, and cannot effectively utilize the temporal dependence of speech signals. In addition, it is difficult to build a more efficient and robust sentiment analysis system by solely utilizing speech information. With the development of the Internet of Things (IoTs), online multimodal information, including speech, video, and text, has become more convenient. To this end, this paper proposes a novel multimodal fusion emotion analysis system. Firstly, by combining convolutional networks with Transformer encoders, the spatiotemporal dependencies of speech information are effectively utilized. To improve multimodal information fusion, we introduce the exchange-based fusion mechanism. The experimental results on the public dataset indicate that the proposed multimodal fusion model achieves the best performance. In online English teaching, teachers can effectively improve the quality of teaching by leveraging the feedback information of students' emotional states through our proposed deep model.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661003","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 hybrid approach for malware detection in SDN-enabled IoT scenarios 在支持 SDN 的物联网场景中检测恶意软件的混合方法
IF 0.9
Internet Technology Letters Pub Date : 2024-04-23 DOI: 10.1002/itl2.534
Cristian H. M. Souza, Carlos H. Arima
{"title":"A hybrid approach for malware detection in SDN-enabled IoT scenarios","authors":"Cristian H. M. Souza,&nbsp;Carlos H. Arima","doi":"10.1002/itl2.534","DOIUrl":"10.1002/itl2.534","url":null,"abstract":"<p>Malware presents a significant threat to computer systems security, especially in ARM and MIPS architectures, driven by the rise of the internet of things (IoT). This paper introduces Heimdall, a hybrid approach that integrates YARA signatures and machine learning in programmable switches for efficient malware detection in SDN-enabled IoT environments. The machine learning classifier achieved an accuracy of 99.33% against the IoT-23 dataset. When evaluated in an emulated environment with real malware samples, Heimdall exhibits a 98.44% detection rate and an average processing time of 0.0217 s.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669824","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
Optimization of data analysis models for low-resource Eurasian languages using machine translation 利用机器翻译优化低资源欧亚语言的数据分析模型
IF 0.9
Internet Technology Letters Pub Date : 2024-04-18 DOI: 10.1002/itl2.528
HongYan Chen, Kim Kyung Yee
{"title":"Optimization of data analysis models for low-resource Eurasian languages using machine translation","authors":"HongYan Chen,&nbsp;Kim Kyung Yee","doi":"10.1002/itl2.528","DOIUrl":"10.1002/itl2.528","url":null,"abstract":"<p>This study explores low-resource language data translation models in the realms of multimedia teaching and cyber security. A rapid learning-based neural machine translation (NMT) method is developed based on meta-learning theory. Subsequently, the back translation method is employed to further improve the NMT model for low-resource language data. Results indicate that the proposed low-resource language NMT method based on meta-learning achieves increased Bilingual Evaluation Understudy (BLEU) scores for three target tasks in a supervised environment. This study emphasizes the auxiliary role of meta-learning theory in low-resource language data translation, aiming to enhance the efficiency of translation models in utilizing information from low-resource languages.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687143","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
Heterogeneous network intrusion detection via domain adaptation in IoT environment 在物联网环境中通过域适应进行异构网络入侵检测
IF 0.9
Internet Technology Letters Pub Date : 2024-04-16 DOI: 10.1002/itl2.531
Jun Zhang, Yao Li, Litian Zhang
{"title":"Heterogeneous network intrusion detection via domain adaptation in IoT environment","authors":"Jun Zhang,&nbsp;Yao Li,&nbsp;Litian Zhang","doi":"10.1002/itl2.531","DOIUrl":"10.1002/itl2.531","url":null,"abstract":"<p>Network intrusion detection refers to detect the threaten behaviors in the network to guarantee the network security. Compared with computer network, Internet of Things (IoT) consists of various devices, including computer, smart phone, smart watch, various sensors etc. The data in IoT may be captured from heterogeneous scenes using various devices. The data may follow from different distributions. Most previous works may fail when they are used in heterogeneous scenes of IoT. In order to overcome this issue, this paper designs a heterogeneous network intrusion detection scheme using attention sharing mechanism to implement domain adaptation for the intrusion detection of the data with heterogeneous distributions. The data from heterogeneous IoT devices is projected into the same sharing space via attention sharing to alleviate the bias between the distributions of data from these devices. Thus, the intrusion detection model learnt from the data from a scene can be migrated to another scene. The experiments and simulation demonstrate that the proposed intrusion detection scheme can adapt the changes of IoT scene.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698077","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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