Xuerong Cui, Yue Cai, Juan Li, Lei Li, Bin Jiang, Liya Liu
In recent years, the integrated system for underwater detection and communication (ISUDC) has become one of the important research directions. At present, the efficiency of the existing shared signals carrying communication information is low, and the synchronization performance needs to be further improved. So, this paper proposes an integrated waveform design scheme for ISUDC based on the carrier with good detection performance and binary phase shift differential keying (DBPSK). In this scheme, the signal with better underwater detection performance (e.g. Linear Frequency Modulation, LFM) is selected as the carrier, and DBPSK is used as the modulation mode to modulate the communication symbols into the carrier. However, the correlation of the detection signal will be destroyed. Thus, the down‐frequency detection signal (e.g. ngLFM) is superimposed into the signal to form a shared signal (LFM‐DBPSK‐ng, LDN). The detection performance, synchronization performance and communication performance of the shared signal are analyzed by ambiguity function, cross‐correlation and BER, respectively. In addition, according to the proposed scheme, the shared signal HFM‐DBPSK‐ng (HDN) is generated by using HFM as carrier. The comparative simulation experiments are carried out with the existing LFM‐BPSK signals. Experiments show that both LDN and HDN have better performance compared to the other shared signals, which proves the effectiveness of the integrated waveform design scheme for ISUDC proposed in this paper.
{"title":"An integrated waveform design method for underwater acoustic detection and communication","authors":"Xuerong Cui, Yue Cai, Juan Li, Lei Li, Bin Jiang, Liya Liu","doi":"10.1002/itl2.522","DOIUrl":"https://doi.org/10.1002/itl2.522","url":null,"abstract":"In recent years, the integrated system for underwater detection and communication (ISUDC) has become one of the important research directions. At present, the efficiency of the existing shared signals carrying communication information is low, and the synchronization performance needs to be further improved. So, this paper proposes an integrated waveform design scheme for ISUDC based on the carrier with good detection performance and binary phase shift differential keying (DBPSK). In this scheme, the signal with better underwater detection performance (e.g. Linear Frequency Modulation, LFM) is selected as the carrier, and DBPSK is used as the modulation mode to modulate the communication symbols into the carrier. However, the correlation of the detection signal will be destroyed. Thus, the down‐frequency detection signal (e.g. ngLFM) is superimposed into the signal to form a shared signal (LFM‐DBPSK‐ng, LDN). The detection performance, synchronization performance and communication performance of the shared signal are analyzed by ambiguity function, cross‐correlation and BER, respectively. In addition, according to the proposed scheme, the shared signal HFM‐DBPSK‐ng (HDN) is generated by using HFM as carrier. The comparative simulation experiments are carried out with the existing LFM‐BPSK signals. Experiments show that both LDN and HDN have better performance compared to the other shared signals, which proves the effectiveness of the integrated waveform design scheme for ISUDC proposed in this paper.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"31 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732474","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}
This paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.
{"title":"Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications","authors":"Yunting Li, Xing‐Zhou Li","doi":"10.1002/itl2.526","DOIUrl":"https://doi.org/10.1002/itl2.526","url":null,"abstract":"This paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"172 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752842","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}
Fake news classification emerged as an exciting topic for machine learning and artificial intelligence researchers. Most of the existing literature on fake news detection is based on the English language. Hence, it needs more usability. Fake news detection in low‐resource scare languages is still challenging due to the absence of large annotated datasets and tools. In this work, we propose a large‐scale Indian news dataset for the Hindi language. This dataset is constructed by scraping different reliable fact‐checking websites. The LDA approach is adopted to assign the category to news statements. Various machine‐learning and transfer learning approaches are applied to verify the authenticity of the dataset. Ensemble learning is also applied based on the low false‐positive rate of machine‐learning classifiers. A multi‐modal approach is adopted by combining LSTM with VGG‐16 and VGG‐19 classifiers. LSTM is used for textual features, while VGG‐16 and VGG‐19 are applied for image analysis. Our proposed dataset has achieved satisfactory performance.
{"title":"Fake news detection in the Hindi language using multi‐modality via transfer and ensemble learning","authors":"Sonal Garg, Dilip Kumar Sharma","doi":"10.1002/itl2.523","DOIUrl":"https://doi.org/10.1002/itl2.523","url":null,"abstract":"Fake news classification emerged as an exciting topic for machine learning and artificial intelligence researchers. Most of the existing literature on fake news detection is based on the English language. Hence, it needs more usability. Fake news detection in low‐resource scare languages is still challenging due to the absence of large annotated datasets and tools. In this work, we propose a large‐scale Indian news dataset for the Hindi language. This dataset is constructed by scraping different reliable fact‐checking websites. The LDA approach is adopted to assign the category to news statements. Various machine‐learning and transfer learning approaches are applied to verify the authenticity of the dataset. Ensemble learning is also applied based on the low false‐positive rate of machine‐learning classifiers. A multi‐modal approach is adopted by combining LSTM with VGG‐16 and VGG‐19 classifiers. LSTM is used for textual features, while VGG‐16 and VGG‐19 are applied for image analysis. Our proposed dataset has achieved satisfactory performance.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"792 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782220","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}
Network reliability analysis is vital for ensuring efficient and error‐free communication within networking and communication applications. Binary Decision Diagrams (BDDs) have emerged as a powerful tool for analyzing and optimizing complex network infrastructures. The objective of this research paper is to conduct a comparative analysis of edge‐ordering algorithms for network reliability using BDDs the study aims to evaluate and compare existing algorithms, providing valuable insights for selecting suitable edge‐ordering algorithms that enhance network reliability. The paper concludes that snooker is outperforming among selected algorithms.
{"title":"BDD efficiency: Survey of BDD edge ordering algorithms in network reliability","authors":"Aakash Chauhan, Gourav Verma","doi":"10.1002/itl2.525","DOIUrl":"https://doi.org/10.1002/itl2.525","url":null,"abstract":"Network reliability analysis is vital for ensuring efficient and error‐free communication within networking and communication applications. Binary Decision Diagrams (BDDs) have emerged as a powerful tool for analyzing and optimizing complex network infrastructures. The objective of this research paper is to conduct a comparative analysis of edge‐ordering algorithms for network reliability using BDDs the study aims to evaluate and compare existing algorithms, providing valuable insights for selecting suitable edge‐ordering algorithms that enhance network reliability. The paper concludes that snooker is outperforming among selected algorithms.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"149 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140756351","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}
Detecting unauthorized access, unusual activities, and data is significant for the security of IoT networks as it helps identify malfunctioning, faults, and intrusions. Intrusion detection methods analyze network information to identify potential misuse or intrusion attacks. This research proposes a multi‐objective prairie dog optimization algorithm (MPDA) to improve its ability to deal with feature selection problems. The proposed algorithm is modified by incorporating the concepts of an archive, grid, and non‐dominance. An archive and a grid are used to save intermediate best results and improve the diversity, respectively. The non‐dominance concept is employed to deal with multiple objectives. On the NSL‐KDD, CIC‐IDS2017, and IoTID20 datasets, MPDA achieves fewer features, higher accuracy, and lower false alarm rates. MPDA outperforms simple classifiers and state‐of‐art multiobjective optimization algorithms in intrusion detection.
{"title":"Multi‐objective prairie dog optimization algorithm for IoT‐based intrusion detection","authors":"Shubhkirti Sharma, Vijay Kumar, K. Dutta","doi":"10.1002/itl2.516","DOIUrl":"https://doi.org/10.1002/itl2.516","url":null,"abstract":"Detecting unauthorized access, unusual activities, and data is significant for the security of IoT networks as it helps identify malfunctioning, faults, and intrusions. Intrusion detection methods analyze network information to identify potential misuse or intrusion attacks. This research proposes a multi‐objective prairie dog optimization algorithm (MPDA) to improve its ability to deal with feature selection problems. The proposed algorithm is modified by incorporating the concepts of an archive, grid, and non‐dominance. An archive and a grid are used to save intermediate best results and improve the diversity, respectively. The non‐dominance concept is employed to deal with multiple objectives. On the NSL‐KDD, CIC‐IDS2017, and IoTID20 datasets, MPDA achieves fewer features, higher accuracy, and lower false alarm rates. MPDA outperforms simple classifiers and state‐of‐art multiobjective optimization algorithms in intrusion detection.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221334","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}
In the evolving landscape of sixth‐generation (6G) network technologies, Non‐Orthogonal Multiple Access (NOMA) systems are pivotal for achieving enhanced spectral efficiency and network capacity. However, a significant challenge in NOMA systems is the high Peak‐to‐Average Power Ratio (PAPR), which undermines system efficiency by necessitating high‐power amplifiers (HPAs) to operate in their less efficient, non‐linear range. Addressing this, we introduce a novel hybrid approach, the Selective Mapping‐Circular Transformation Method (SLM‐CTM), which ingeniously amalgamates the strengths of Selective Mapping (SLM) and the Circular Transformation Method (CTM) to mitigate PAPR issues. SLM is renowned for its peak power reduction capabilities without adding to system complexity, whereas CTM is valued for its simplicity and controlled signal distortion. The proposed SLM‐CTM strategy employs a blend of linear and nonlinear techniques to effectively lower PAPR in non‐orthogonal NOMA configurations, thereby reducing high‐power peaks while simultaneously enhancing signal quality. This paper delineates the application of the SLM‐CTM algorithm to evaluate critical NOMA parameters such as Power Spectral Density (PSD), Bit Error Rate (BER), and PAPR. Simulation results highlight the efficacy of SLM‐CTM over conventional SLM, demonstrating a significant throughput improvement of 3.2 dB and a PAPR reduction of 4.6 dB, underscoring the potential of SLM‐CTM in elevating the performance of NOMA systems within 6G network.
{"title":"PAPR reduction using model‐driven hybrid algorithms in the 6G NOMA waveform","authors":"Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong","doi":"10.1002/itl2.515","DOIUrl":"https://doi.org/10.1002/itl2.515","url":null,"abstract":"In the evolving landscape of sixth‐generation (6G) network technologies, Non‐Orthogonal Multiple Access (NOMA) systems are pivotal for achieving enhanced spectral efficiency and network capacity. However, a significant challenge in NOMA systems is the high Peak‐to‐Average Power Ratio (PAPR), which undermines system efficiency by necessitating high‐power amplifiers (HPAs) to operate in their less efficient, non‐linear range. Addressing this, we introduce a novel hybrid approach, the Selective Mapping‐Circular Transformation Method (SLM‐CTM), which ingeniously amalgamates the strengths of Selective Mapping (SLM) and the Circular Transformation Method (CTM) to mitigate PAPR issues. SLM is renowned for its peak power reduction capabilities without adding to system complexity, whereas CTM is valued for its simplicity and controlled signal distortion. The proposed SLM‐CTM strategy employs a blend of linear and nonlinear techniques to effectively lower PAPR in non‐orthogonal NOMA configurations, thereby reducing high‐power peaks while simultaneously enhancing signal quality. This paper delineates the application of the SLM‐CTM algorithm to evaluate critical NOMA parameters such as Power Spectral Density (PSD), Bit Error Rate (BER), and PAPR. Simulation results highlight the efficacy of SLM‐CTM over conventional SLM, demonstrating a significant throughput improvement of 3.2 dB and a PAPR reduction of 4.6 dB, underscoring the potential of SLM‐CTM in elevating the performance of NOMA systems within 6G network.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"34 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263278","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}
In this letter, we propose a control jammer‐assisted framework for improving the physical layer security (PLS) of full‐duplex (FD) ‐ cooperative non‐orthogonal multiple access (FD‐CNOMA) network. We derive analytical expressions for the secrecy outage probabilities (SOPs) of the users for the jammer‐assisted and the no‐jammer scenarios, considering multiple non‐colluding eavesdroppers, residual hardware impairments and imperfect successive interference cancellation conditions. It is demonstrated that the proposed jammer‐assisted framework provides significant reduction of the SOPs experienced by the downlink users in FD‐CNOMA network.
{"title":"On the physical layer security performance of full‐duplex cooperative NOMA system with multiple eavesdroppers, imperfect SIC and hardware imperfections","authors":"T. Nimi, A. V. Babu","doi":"10.1002/itl2.513","DOIUrl":"https://doi.org/10.1002/itl2.513","url":null,"abstract":"In this letter, we propose a control jammer‐assisted framework for improving the physical layer security (PLS) of full‐duplex (FD) ‐ cooperative non‐orthogonal multiple access (FD‐CNOMA) network. We derive analytical expressions for the secrecy outage probabilities (SOPs) of the users for the jammer‐assisted and the no‐jammer scenarios, considering multiple non‐colluding eavesdroppers, residual hardware impairments and imperfect successive interference cancellation conditions. It is demonstrated that the proposed jammer‐assisted framework provides significant reduction of the SOPs experienced by the downlink users in FD‐CNOMA network.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266736","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}
Xuerong Cui, Bin Yuan, Juan Li, Binbin Jiang, Shibao Li, Jianhang Liu
In this letter, we propose a method for underwater acoustic channel estimation that combines image super‐resolution (SR) and is named FCDnNet. FCDnNet consists of two parts: Fast Super Resolution Convolutional Neural Network (FSRCNN) and Complex Denoising Convolutional Neural Network (C‐DnCNN). FSRCNN extracts effective features of pilot channels, uses deconvolution to achieve SR reconstruction, and generates a pre‐estimation channel matrix. C‐DnCNN preserves the relative positions of the real and imaginary parts of the channel, fully utilizing amplitude and phase information, and can more effectively recover the channel matrix from the pre‐estimation matrix. Experimental results show that the normalized mean square error (NMSE) of FCDnNet is at least 13.1–65.2 lower than other channel estimation methods based on deep learning.
在这封信中，我们提出了一种结合图像超分辨率（SR）的水下声道估计方法，并将其命名为 FCDnNet。FCDnNet 由两部分组成：快速超分辨率卷积神经网络（FSRCNN）和复杂去噪卷积神经网络（C-DnCNN）。FSRCNN 提取先导信道的有效特征，使用解卷积实现 SR 重构，并生成预估计信道矩阵。C-DnCNN 保留了信道实部和虚部的相对位置，充分利用了振幅和相位信息，能更有效地从预估计矩阵中恢复信道矩阵。实验结果表明，FCDnNet 的归一化均方误差（NMSE）比其他基于深度学习的信道估计方法至少低 13.1-65.2。
{"title":"Channel estimation for underwater acoustic OFDM based on super‐resolution network","authors":"Xuerong Cui, Bin Yuan, Juan Li, Binbin Jiang, Shibao Li, Jianhang Liu","doi":"10.1002/itl2.496","DOIUrl":"https://doi.org/10.1002/itl2.496","url":null,"abstract":"In this letter, we propose a method for underwater acoustic channel estimation that combines image super‐resolution (SR) and is named FCDnNet. FCDnNet consists of two parts: Fast Super Resolution Convolutional Neural Network (FSRCNN) and Complex Denoising Convolutional Neural Network (C‐DnCNN). FSRCNN extracts effective features of pilot channels, uses deconvolution to achieve SR reconstruction, and generates a pre‐estimation channel matrix. C‐DnCNN preserves the relative positions of the real and imaginary parts of the channel, fully utilizing amplitude and phase information, and can more effectively recover the channel matrix from the pre‐estimation matrix. Experimental results show that the normalized mean square error (NMSE) of FCDnNet is at least 13.1–65.2 lower than other channel estimation methods based on deep learning.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530592","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}
The rapid advancement of health monitoring technologies has led to increased adoption of fitness training applications that collect and analyze personal health data. This paper presents a personalized differential privacy‐based federated learning (PDP‐FL) algorithm with two stages. Classifying the user's privacy according to their preferences is the first stage in achieving personalized privacy protection with the addition of noise. The privacy preference and the related privacy level are sent to the central aggregation server simultaneously. In the second stage, noise is added that conforms to the global differential privacy threshold based on the privacy level that users uploaded; this allows the global privacy protection level to be quantified while still adhering to the local and central protection strategies simultaneously adopted to realize the complete protection of global data. The results demonstrate the excellent classification accuracy of the proposed PDP‐FL algorithm. The proposed PDP‐FL algorithm addresses the critical issue of health monitoring privacy in fitness training applications. It ensures that sensitive data is handled responsibly and provides users the necessary tools to control their privacy settings. By achieving high classification accuracy while preserving privacy, the framework balances data utility and protection, thus positively impacting health monitoring ecosystem and medical systems.
{"title":"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy","authors":"Lifang Shao","doi":"10.1002/itl2.499","DOIUrl":"https://doi.org/10.1002/itl2.499","url":null,"abstract":"The rapid advancement of health monitoring technologies has led to increased adoption of fitness training applications that collect and analyze personal health data. This paper presents a personalized differential privacy‐based federated learning (PDP‐FL) algorithm with two stages. Classifying the user's privacy according to their preferences is the first stage in achieving personalized privacy protection with the addition of noise. The privacy preference and the related privacy level are sent to the central aggregation server simultaneously. In the second stage, noise is added that conforms to the global differential privacy threshold based on the privacy level that users uploaded; this allows the global privacy protection level to be quantified while still adhering to the local and central protection strategies simultaneously adopted to realize the complete protection of global data. The results demonstrate the excellent classification accuracy of the proposed PDP‐FL algorithm. The proposed PDP‐FL algorithm addresses the critical issue of health monitoring privacy in fitness training applications. It ensures that sensitive data is handled responsibly and provides users the necessary tools to control their privacy settings. By achieving high classification accuracy while preserving privacy, the framework balances data utility and protection, thus positively impacting health monitoring ecosystem and medical systems.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"69 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448941","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}
Currently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.
{"title":"A fog‐computing architecture for network public opinion monitoring based on intelligent semantic recognition","authors":"Jing-Zhe Xu","doi":"10.1002/itl2.492","DOIUrl":"https://doi.org/10.1002/itl2.492","url":null,"abstract":"Currently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229242","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}
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