Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2022.09.023
Yueling Liu , Shengteng Jiang , Yichi Zhang , Kuo Cao , Li Zhou , Boon-Chong Seet , Haitao Zhao , Jibo Wei
Context information is significant for semantic extraction and recovery of messages in semantic communication. However, context information is not fully utilized in the existing semantic communication systems since relationships between sentences are often ignored. In this paper, we propose an Extended Context-based Semantic Communication (ECSC) system for text transmission, in which context information within and between sentences is explored for semantic representation and recovery. At the encoder, self-attention and segment-level relative attention are used to extract context information within and between sentences, respectively. In addition, a gate mechanism is adopted at the encoder to incorporate the context information from different ranges. At the decoder, Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery. Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.
{"title":"Extended context-based semantic communication system for text transmission","authors":"Yueling Liu , Shengteng Jiang , Yichi Zhang , Kuo Cao , Li Zhou , Boon-Chong Seet , Haitao Zhao , Jibo Wei","doi":"10.1016/j.dcan.2022.09.023","DOIUrl":"10.1016/j.dcan.2022.09.023","url":null,"abstract":"<div><p>Context information is significant for semantic extraction and recovery of messages in semantic communication. However, context information is not fully utilized in the existing semantic communication systems since relationships between sentences are often ignored. In this paper, we propose an Extended Context-based Semantic Communication (ECSC) system for text transmission, in which context information within and between sentences is explored for semantic representation and recovery. At the encoder, self-attention and segment-level relative attention are used to extract context information within and between sentences, respectively. In addition, a gate mechanism is adopted at the encoder to incorporate the context information from different ranges. At the decoder, Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery. Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864822001985/pdfft?md5=0ea3b96960146fb3a5b4b27faf348756&pid=1-s2.0-S2352864822001985-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47757737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2023.02.014
Yaguang Lin, Xiaoming Wang, Liang Wang, Pengfei Wan
As an ingenious convergence between the Internet of Things and social networks, the Social Internet of Things (SIoT) can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information. Nevertheless, SIoT is characterized by high openness and autonomy, multiple kinds of information can spread rapidly, freely and cooperatively in SIoT, which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion. To this end, with the aim of exploring multi-information cooperative diffusion processes in SIoT, we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper. Subsequently, the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated, and the diffusion trend is predicted. On this basis, to further control the multi-information cooperative diffusion process efficiently, we propose two control strategies for information diffusion with control objectives, develop an optimal control system for the multi-information cooperative diffusion process, and propose the corresponding optimal control method. The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory. Finally, extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model, strategy and method.
{"title":"Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things","authors":"Yaguang Lin, Xiaoming Wang, Liang Wang, Pengfei Wan","doi":"10.1016/j.dcan.2023.02.014","DOIUrl":"10.1016/j.dcan.2023.02.014","url":null,"abstract":"<div><p>As an ingenious convergence between the Internet of Things and social networks, the Social Internet of Things (SIoT) can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information. Nevertheless, SIoT is characterized by high openness and autonomy, multiple kinds of information can spread rapidly, freely and cooperatively in SIoT, which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion. To this end, with the aim of exploring multi-information cooperative diffusion processes in SIoT, we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper. Subsequently, the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated, and the diffusion trend is predicted. On this basis, to further control the multi-information cooperative diffusion process efficiently, we propose two control strategies for information diffusion with control objectives, develop an optimal control system for the multi-information cooperative diffusion process, and propose the corresponding optimal control method. The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory. Finally, extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model, strategy and method.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864823000500/pdfft?md5=ed3412c993d198e5d878f138ec31b3bb&pid=1-s2.0-S2352864823000500-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49179090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2022.09.009
Ahmad Azab , Mahmoud Khasawneh , Saed Alrabaee , Kim-Kwang Raymond Choo , Maysa Sarsour
In network traffic classification, it is important to understand the correlation between network traffic and its causal application, protocol, or service group, for example, in facilitating lawful interception, ensuring the quality of service, preventing application choke points, and facilitating malicious behavior identification. In this paper, we review existing network classification techniques, such as port-based identification and those based on deep packet inspection, statistical features in conjunction with machine learning, and deep learning algorithms. We also explain the implementations, advantages, and limitations associated with these techniques. Our review also extends to publicly available datasets used in the literature. Finally, we discuss existing and emerging challenges, as well as future research directions.
{"title":"Network traffic classification: Techniques, datasets, and challenges","authors":"Ahmad Azab , Mahmoud Khasawneh , Saed Alrabaee , Kim-Kwang Raymond Choo , Maysa Sarsour","doi":"10.1016/j.dcan.2022.09.009","DOIUrl":"10.1016/j.dcan.2022.09.009","url":null,"abstract":"<div><p>In network traffic classification, it is important to understand the correlation between network traffic and its causal application, protocol, or service group, for example, in facilitating lawful interception, ensuring the quality of service, preventing application choke points, and facilitating malicious behavior identification. In this paper, we review existing network classification techniques, such as port-based identification and those based on deep packet inspection, statistical features in conjunction with machine learning, and deep learning algorithms. We also explain the implementations, advantages, and limitations associated with these techniques. Our review also extends to publicly available datasets used in the literature. Finally, we discuss existing and emerging challenges, as well as future research directions.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864822001845/pdfft?md5=ed4d3b9b63f2eacf26dee4cc0d941dd7&pid=1-s2.0-S2352864822001845-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46275302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2022.10.011
Lizong Zhang , Yiming Wang , Ke Yan , Yi Su , Nawaf Alharbe , Shuxin Feng
With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.
{"title":"Behaviour recognition based on the integration of multigranular motion features in the Internet of Things","authors":"Lizong Zhang , Yiming Wang , Ke Yan , Yi Su , Nawaf Alharbe , Shuxin Feng","doi":"10.1016/j.dcan.2022.10.011","DOIUrl":"10.1016/j.dcan.2022.10.011","url":null,"abstract":"<div><p>With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864822002206/pdfft?md5=764e303708401baef55f84c6951af22a&pid=1-s2.0-S2352864822002206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44471127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2023.02.015
Xu Li , Gwanggil Jeon , Wenshuo Wang , Jindong Zhao
The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network, so it has promoted the applications of crowd-sensing services in different fields, but also brings more privacy security challenges, the most commom which is privacy leakage. As a privacy protection technology combining data integrity check and identity anonymity, ring signature is widely used in the field of privacy protection. However, introducing signature technology leads to additional signature verification overhead. In the scenario of crowd-sensing, the existing signature schemes have low efficiency in multi-signature verification. Therefore, it is necessary to design an efficient multi-signature verification scheme while ensuring security. In this paper, a batch-verifiable signature scheme is proposed based on the crowd-sensing background, which supports the sensing platform to verify the uploaded multiple signature data efficiently, so as to overcoming the defects of the traditional signature scheme in multi-signature verification. In our proposal, a method for linking homologous data was presented, which was valuable for incentive mechanism and data analysis. Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.
{"title":"A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing","authors":"Xu Li , Gwanggil Jeon , Wenshuo Wang , Jindong Zhao","doi":"10.1016/j.dcan.2023.02.015","DOIUrl":"10.1016/j.dcan.2023.02.015","url":null,"abstract":"<div><p>The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network, so it has promoted the applications of crowd-sensing services in different fields, but also brings more privacy security challenges, the most commom which is privacy leakage. As a privacy protection technology combining data integrity check and identity anonymity, ring signature is widely used in the field of privacy protection. However, introducing signature technology leads to additional signature verification overhead. In the scenario of crowd-sensing, the existing signature schemes have low efficiency in multi-signature verification. Therefore, it is necessary to design an efficient multi-signature verification scheme while ensuring security. In this paper, a batch-verifiable signature scheme is proposed based on the crowd-sensing background, which supports the sensing platform to verify the uploaded multiple signature data efficiently, so as to overcoming the defects of the traditional signature scheme in multi-signature verification. In our proposal, a method for linking homologous data was presented, which was valuable for incentive mechanism and data analysis. Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864823000482/pdfft?md5=afc680862c04b160114c31f24c23f39f&pid=1-s2.0-S2352864823000482-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42599814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing web-based security applications have failed in many situations due to the great intelligence of attackers. Among web applications, Cross-Site Scripting (XSS) is one of the dangerous assaults experienced while modifying an organization's or user's information. To avoid these security challenges, this article proposes a novel, all-encompassing combination of machine learning (NB, SVM, k-NN) and deep learning (RNN, CNN, LSTM) frameworks for detecting and defending against XSS attacks with high accuracy and efficiency. Based on the representation, a novel idea for merging stacking ensemble with web applications, termed “hybrid stacking”, is proposed. In order to implement the aforementioned methods, four distinct datasets, each of which contains both safe and unsafe content, are considered. The hybrid detection method can adaptively identify the attacks from the URL, and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy, accelerate the training process, and effectively remove the unsafe JScript/JavaScript keywords from the URL. The simulation results show that the proposed hybrid model is more efficient than the existing detection methods. It produces more than 99.5% accurate XSS attack classification results (accuracy, precision, recall, f1_score, and Receiver Operating Characteristic (ROC)) and is highly resistant to XSS attacks. In order to ensure the security of the server's information, the proposed hybrid approach is demonstrated in a real-time environment.
{"title":"Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach","authors":"Muralitharan Krishnan , Yongdo Lim , Seethalakshmi Perumal , Gayathri Palanisamy","doi":"10.1016/j.dcan.2022.09.024","DOIUrl":"10.1016/j.dcan.2022.09.024","url":null,"abstract":"<div><p>Existing web-based security applications have failed in many situations due to the great intelligence of attackers. Among web applications, Cross-Site Scripting (<em>XSS</em>) is one of the dangerous assaults experienced while modifying an organization's or user's information. To avoid these security challenges, this article proposes a novel, all-encompassing combination of machine learning (NB, SVM, k-NN) and deep learning (RNN, CNN, LSTM) frameworks for detecting and defending against <em>XSS</em> attacks with high accuracy and efficiency. Based on the representation, a novel idea for merging stacking ensemble with web applications, termed “hybrid stacking”, is proposed. In order to implement the aforementioned methods, four distinct datasets, each of which contains both safe and unsafe content, are considered. The hybrid detection method can adaptively identify the attacks from the <em>URL</em>, and the defense mechanism inherits the advantages of <em>URL</em> encoding with dictionary-based mapping to improve prediction accuracy, accelerate the training process, and effectively remove the unsafe <em>JScript/JavaScript</em> keywords from the <em>URL</em>. The simulation results show that the proposed hybrid model is more efficient than the existing detection methods. It produces more than 99.5% accurate <em>XSS</em> attack classification results (accuracy, precision, recall, f1_score, and Receiver Operating Characteristic (ROC)) and is highly resistant to <em>XSS</em> attacks. In order to ensure the security of the server's information, the proposed hybrid approach is demonstrated in a real-time environment.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864822001997/pdfft?md5=8bb2753659ffe223edfc629930a19fc5&pid=1-s2.0-S2352864822001997-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47083659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2023.06.010
Rong Ma , Zhen Zhang , Yide Ma , Xiping Hu , Edith C.H. Ngai , Victor C.M. Leung
In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.
{"title":"An improved pulse coupled neural networks model for semantic IoT","authors":"Rong Ma , Zhen Zhang , Yide Ma , Xiping Hu , Edith C.H. Ngai , Victor C.M. Leung","doi":"10.1016/j.dcan.2023.06.010","DOIUrl":"10.1016/j.dcan.2023.06.010","url":null,"abstract":"<div><p>In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864823001165/pdfft?md5=f795291116f0f475cee878b8052f6d78&pid=1-s2.0-S2352864823001165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48421606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2023.05.010
Yating Liu , Xiaojie Wang , Zhaolong Ning , MengChu Zhou , Lei Guo , Behrouz Jedari
Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.
{"title":"A survey on semantic communications: Technologies, solutions, applications and challenges","authors":"Yating Liu , Xiaojie Wang , Zhaolong Ning , MengChu Zhou , Lei Guo , Behrouz Jedari","doi":"10.1016/j.dcan.2023.05.010","DOIUrl":"10.1016/j.dcan.2023.05.010","url":null,"abstract":"<div><p>Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864823000925/pdfft?md5=509ef4c5380bb7b1e25768327aac3153&pid=1-s2.0-S2352864823000925-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54171225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2023.03.007
Yongfeng Tao , Minqiang Yang , Yushan Wu , Kevin Lee , Adrienne Kline , Bin Hu
With the rapid growth of information transmission via the Internet, efforts have been made to reduce network load to promote efficiency. One such application is semantic computing, which can extract and process semantic communication. Social media has enabled users to share their current emotions, opinions, and life events through their mobile devices. Notably, people suffering from mental health problems are more willing to share their feelings on social networks. Therefore, it is necessary to extract semantic information from social media (vlog data) to identify abnormal emotional states to facilitate early identification and intervention. Most studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as depression. To solve this problem, this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of depression. First, a module with spatio-temporal data is embedded into the transformer encoder, which is utilized to obtain a representation of spatio-temporal features. Second, a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression effectively. Experiments are conducted on the D-Vlog dataset. The results show that the method is effective, and the accuracy rate can reach 70.70%. This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.
{"title":"Depressive semantic awareness from vlog facial and vocal streams via spatio-temporal transformer","authors":"Yongfeng Tao , Minqiang Yang , Yushan Wu , Kevin Lee , Adrienne Kline , Bin Hu","doi":"10.1016/j.dcan.2023.03.007","DOIUrl":"10.1016/j.dcan.2023.03.007","url":null,"abstract":"<div><p>With the rapid growth of information transmission via the Internet, efforts have been made to reduce network load to promote efficiency. One such application is semantic computing, which can extract and process semantic communication. Social media has enabled users to share their current emotions, opinions, and life events through their mobile devices. Notably, people suffering from mental health problems are more willing to share their feelings on social networks. Therefore, it is necessary to extract semantic information from social media (vlog data) to identify abnormal emotional states to facilitate early identification and intervention. Most studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as depression. To solve this problem, this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of depression. First, a module with spatio-temporal data is embedded into the transformer encoder, which is utilized to obtain a representation of spatio-temporal features. Second, a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression effectively. Experiments are conducted on the D-Vlog dataset. The results show that the method is effective, and the accuracy rate can reach 70.70%. This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.</p></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352864823000639/pdfft?md5=292aeeac6a55da512686a76b28ab528a&pid=1-s2.0-S2352864823000639-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42511171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcan.2024.01.002
Lu Sun , Xiaona Li , Mingyue Zhang , Liangtian Wan , Yun Lin , Xianpeng Wang , Gang Xu
Interconnection of all things challenges the traditional communication methods, and Semantic Communication and Computing (SCC) will become new solutions. It is a challenging task to accurately detect, extract, and represent semantic information in the research of SCC-based networks. In previous research, researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification. However, the content of semantic information is quite complex. Although graph convolutional neural networks provide an effective solution for node classification tasks, due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures, the extracted feature information is subject to varying degrees of loss. Therefore, this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network. The Bidirectional Encoder Representations from Transformers (BERT) training word vector is introduced to extract the semantic features in the network, and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network. A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification. We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.
万物互联对传统通信方式提出了挑战,语义通信与计算(Semantic Communication and Computing,SCC)将成为新的解决方案。在基于 SCC 的网络研究中,如何准确检测、提取和表示语义信息是一项具有挑战性的任务。在以往的研究中,研究人员通常使用卷积法提取图的特征信息,并执行相应的节点分类任务。然而,语义信息的内容相当复杂。虽然图卷积神经网络为节点分类任务提供了有效的解决方案,但由于其在表示多种关系模式方面的局限性,以及不能识别和分析高阶局部结构,提取的特征信息会受到不同程度的损失。因此,本文从单层拓扑网络扩展到多层异构拓扑网络。引入变压器双向编码器表征(BERT)训练词向量来提取网络中的语义特征,并结合网络模型表征网络的高阶局部特征模块对现有图神经网络进行改进。提出了一种基于 SCC 网络的多层网络嵌入算法,以完成端到端的节点分类任务。我们在一个真实的多层异构网络上验证了该算法的有效性。
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