Pub Date : 2025-10-01DOI: 10.1016/j.dcan.2025.03.001
Yuanshuo Gang, Yuexia Zhang, Xinyi Wang
This paper proposes the Unmanned Aerial Vehicle (UAV)-assisted Full-Duplex (FD) Integrated Sensing And Communication (ISAC) system. In this system, the UAV integrates sensing and communication functions, capable of receiving transmission signals from Uplink (UL) users and echo signal from target, while communicating with Downlink (DL) users and simultaneously detecting target. With the objective of maximizing the Average Sum Rate (ASR) for both UL and DL users, a composite non-convex optimization problem is established, which is decomposed into sub-problems of communication scheduling optimization, transceiver beamforming design, and UAV trajectory optimization. An alternating iterative algorithm is proposed, employing relaxation optimization, extremum traversal search, augmented weighted minimum mean square error, and successive convex approximation methods to solve the aforementioned sub-problems. Simulation results demonstrate that, compared to the traditional UAV-assisted Half-Duplex (HD) ISAC scheme, the proposed FD ISAC scheme effectively improves the ASR.
{"title":"UAV-assisted full-duplex ISAC: Joint communication scheduling, beamforming, and trajectory optimization","authors":"Yuanshuo Gang, Yuexia Zhang, Xinyi Wang","doi":"10.1016/j.dcan.2025.03.001","DOIUrl":"10.1016/j.dcan.2025.03.001","url":null,"abstract":"<div><div>This paper proposes the Unmanned Aerial Vehicle (UAV)-assisted Full-Duplex (FD) Integrated Sensing And Communication (ISAC) system. In this system, the UAV integrates sensing and communication functions, capable of receiving transmission signals from Uplink (UL) users and echo signal from target, while communicating with Downlink (DL) users and simultaneously detecting target. With the objective of maximizing the Average Sum Rate (ASR) for both UL and DL users, a composite non-convex optimization problem is established, which is decomposed into sub-problems of communication scheduling optimization, transceiver beamforming design, and UAV trajectory optimization. An alternating iterative algorithm is proposed, employing relaxation optimization, extremum traversal search, augmented weighted minimum mean square error, and successive convex approximation methods to solve the aforementioned sub-problems. Simulation results demonstrate that, compared to the traditional UAV-assisted Half-Duplex (HD) ISAC scheme, the proposed FD ISAC scheme effectively improves the ASR.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1628-1638"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.dcan.2025.03.009
Shulei Zeng , Bin Cao , Mugen Peng , Shuo Wang , Chen Sun
The emerging deployment of large-scale Low Earth Orbit (LEO) satellite constellations provides seamless global coverage. However, the increasing number of satellites also introduces significant security challenges, such as eavesdropping and illegal communication behavior detection. This paper investigates covert wireless communication over uplink satellite-terrestrial network, focusing on scenarios with warden satellites. By accounting for shot noise generated by ambient signals from terrestrial interferers, the terrestrial transmitter Alice can effectively hide its signal from warden satellites. Leveraging stochastic geometry, the distributions of distances between transmitter and satellites are analyzed, enabling the assessment of uplink performance and interference within a satellite's coverage area. Approximate expressions for detection error probability and transmission outage probability are derived. Based on the theoretical analysis, an optimal scheme is proposed to maximize covert throughput under the constraint of the average detection error probability of the most detrimental warden satellite. Extensive Monte Carlo simulations experiments are conducted to validate the accuracy of analytical methods for evaluating covert performance.
{"title":"Covert wireless communication over uplink satellite-terrestrial network","authors":"Shulei Zeng , Bin Cao , Mugen Peng , Shuo Wang , Chen Sun","doi":"10.1016/j.dcan.2025.03.009","DOIUrl":"10.1016/j.dcan.2025.03.009","url":null,"abstract":"<div><div>The emerging deployment of large-scale Low Earth Orbit (LEO) satellite constellations provides seamless global coverage. However, the increasing number of satellites also introduces significant security challenges, such as eavesdropping and illegal communication behavior detection. This paper investigates covert wireless communication over uplink satellite-terrestrial network, focusing on scenarios with warden satellites. By accounting for shot noise generated by ambient signals from terrestrial interferers, the terrestrial transmitter Alice can effectively hide its signal from warden satellites. Leveraging stochastic geometry, the distributions of distances between transmitter and satellites are analyzed, enabling the assessment of uplink performance and interference within a satellite's coverage area. Approximate expressions for detection error probability and transmission outage probability are derived. Based on the theoretical analysis, an optimal scheme is proposed to maximize covert throughput under the constraint of the average detection error probability of the most detrimental warden satellite. Extensive Monte Carlo simulations experiments are conducted to validate the accuracy of analytical methods for evaluating covert performance.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1318-1329"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2025.03.002
Tianqi Peng , Bei Gong , Chong Guo , Akhtar Badshah , Muhammad Waqas , Hisham Alasmary , Sheng Chen
Data privacy leakage has always been a critical concern in cloud-based Internet of Things (IoT) systems. Dynamic Symmetric Searchable Encryption (DSSE) with forward and backward privacy aims to address this issue by enabling updates and retrievals of ciphertext on untrusted cloud server while ensuring data privacy. However, previous research on DSSE mostly focused on single keyword search, which limits its practical application in cloud-based IoT systems. Recently, Patranabis (NDSS 2021) [1] proposed a groundbreaking DSSE scheme for conjunctive keyword search. However, this scheme fails to effectively handle deletion operations in certain circumstances, resulting in inaccurate query results. Additionally, the scheme introduces unnecessary search overhead. To overcome these problems, we present CKSE, an efficient conjunctive keyword DSSE scheme. Our scheme improves the oblivious shared computation protocol used in the scheme of Patranabis, thus enabling a more comprehensive deletion functionality. Furthermore, we introduce a state chain structure to reduce the search overhead. Through security analysis and experimental evaluation, we demonstrate that our CKSE achieves more comprehensive deletion functionality while maintaining comparable search performance and security, compared to the oblivious dynamic cross-tags protocol of Patranabis. The combination of comprehensive functionality, high efficiency, and security makes our CKSE an ideal choice for deployment in cloud-based IoT systems.
{"title":"An efficient conjunctive keyword searchable encryption for cloud-based IoT systems","authors":"Tianqi Peng , Bei Gong , Chong Guo , Akhtar Badshah , Muhammad Waqas , Hisham Alasmary , Sheng Chen","doi":"10.1016/j.dcan.2025.03.002","DOIUrl":"10.1016/j.dcan.2025.03.002","url":null,"abstract":"<div><div>Data privacy leakage has always been a critical concern in cloud-based Internet of Things (IoT) systems. Dynamic Symmetric Searchable Encryption (DSSE) with forward and backward privacy aims to address this issue by enabling updates and retrievals of ciphertext on untrusted cloud server while ensuring data privacy. However, previous research on DSSE mostly focused on single keyword search, which limits its practical application in cloud-based IoT systems. Recently, Patranabis (NDSS 2021) <span><span>[1]</span></span> proposed a groundbreaking DSSE scheme for conjunctive keyword search. However, this scheme fails to effectively handle deletion operations in certain circumstances, resulting in inaccurate query results. Additionally, the scheme introduces unnecessary search overhead. To overcome these problems, we present CKSE, an efficient conjunctive keyword DSSE scheme. Our scheme improves the oblivious shared computation protocol used in the scheme of Patranabis, thus enabling a more comprehensive deletion functionality. Furthermore, we introduce a state chain structure to reduce the search overhead. Through security analysis and experimental evaluation, we demonstrate that our CKSE achieves more comprehensive deletion functionality while maintaining comparable search performance and security, compared to the oblivious dynamic cross-tags protocol of Patranabis. The combination of comprehensive functionality, high efficiency, and security makes our CKSE an ideal choice for deployment in cloud-based IoT systems.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1293-1304"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.10.009
Bonan Yin, Chenxi Liu, Mugen Peng
In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.
{"title":"Capacity and delay performance analysis for large-scale UAV-enabled wireless networks","authors":"Bonan Yin, Chenxi Liu, Mugen Peng","doi":"10.1016/j.dcan.2024.10.009","DOIUrl":"10.1016/j.dcan.2024.10.009","url":null,"abstract":"<div><div>In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1029-1041"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.11.010
Xinlin Yuan, Yong Wang, Yan Li, Hongbo Kang, Yu Chen, Boran Yang
Low-light images often have defects such as low visibility, low contrast, high noise, and high color distortion compared with well-exposed images. If the low-light region of an image is enhanced directly, the noise will inevitably blur the whole image. Besides, according to the retina-and-cortex (retinex) theory of color vision, the reflectivity of different image regions may differ, limiting the enhancement performance of applying uniform operations to the entire image. Therefore, we design a Hierarchical Flow Learning (HFL) framework, which consists of a Hierarchical Image Network (HIN) and a normalized invertible Flow Learning Network (FLN). HIN can extract hierarchical structural features from low-light images, while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images. In subsequent testing, the reversibility of FLN allows inferring and obtaining enhanced low-light images. Specifically, the HIN extracts as much image information as possible from three scales, local, regional, and global, using a Triple-branch Hierarchical Fusion Module (THFM) and a Dual-Dconv Cross Fusion Module (DCFM). The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information, whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast. In addition, in this paper, the model was trained using a negative log-likelihood loss function. Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions. The HFL model enhances low-light images with better visibility, less noise, and improved contrast, suitable for practical scenarios such as autonomous driving, medical imaging, and nighttime surveillance. Outperforming them by PSNR = 27.26 dB, SSIM = 0.93, and LPIPS = 0.10 on benchmark dataset LOL-v1. The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.
{"title":"Hierarchical flow learning for low-light image enhancement","authors":"Xinlin Yuan, Yong Wang, Yan Li, Hongbo Kang, Yu Chen, Boran Yang","doi":"10.1016/j.dcan.2024.11.010","DOIUrl":"10.1016/j.dcan.2024.11.010","url":null,"abstract":"<div><div>Low-light images often have defects such as low visibility, low contrast, high noise, and high color distortion compared with well-exposed images. If the low-light region of an image is enhanced directly, the noise will inevitably blur the whole image. Besides, according to the retina-and-cortex (retinex) theory of color vision, the reflectivity of different image regions may differ, limiting the enhancement performance of applying uniform operations to the entire image. Therefore, we design a Hierarchical Flow Learning (HFL) framework, which consists of a Hierarchical Image Network (HIN) and a normalized invertible Flow Learning Network (FLN). HIN can extract hierarchical structural features from low-light images, while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images. In subsequent testing, the reversibility of FLN allows inferring and obtaining enhanced low-light images. Specifically, the HIN extracts as much image information as possible from three scales, local, regional, and global, using a Triple-branch Hierarchical Fusion Module (THFM) and a Dual-Dconv Cross Fusion Module (DCFM). The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information, whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast. In addition, in this paper, the model was trained using a negative log-likelihood loss function. Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions. The HFL model enhances low-light images with better visibility, less noise, and improved contrast, suitable for practical scenarios such as autonomous driving, medical imaging, and nighttime surveillance. Outperforming them by PSNR = 27.26 dB, SSIM = 0.93, and LPIPS = 0.10 on benchmark dataset LOL-v1. The source code of HFL is available at <span><span>https://github.com/Smile-QT/HFL-for-LIE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1158-1172"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.11.012
Shumaila Javaid , Hamza Fahim , Sherali Zeadally , Bin He
Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.
{"title":"From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs","authors":"Shumaila Javaid , Hamza Fahim , Sherali Zeadally , Bin He","doi":"10.1016/j.dcan.2024.11.012","DOIUrl":"10.1016/j.dcan.2024.11.012","url":null,"abstract":"<div><div>Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 937-960"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.12.001
Xuyang Chen , Daquan Feng , Qi He , Yao Sun , Gaojie Chen , Xiang-Gen Xia
Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.
{"title":"Content-aware robust semantic transmission of images over wireless channels with GANs","authors":"Xuyang Chen , Daquan Feng , Qi He , Yao Sun , Gaojie Chen , Xiang-Gen Xia","doi":"10.1016/j.dcan.2024.12.001","DOIUrl":"10.1016/j.dcan.2024.12.001","url":null,"abstract":"<div><div>Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1205-1213"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.12.004
Aditya Kumar, Satish Narayana Srirama
Federated Learning (FL) has become a popular training paradigm in recent years. However, stragglers are critical bottlenecks in an Internet of Things (IoT) network while training. These nodes produce stale updates to the server, which slow down the convergence. In this paper, we studied the impact of the stale updates on the global model, which is observed to be significant. To address this, we propose a weighted averaging scheme, FedStrag, that optimizes the training with stale updates. The work is focused on training a model in an IoT network that has multiple challenges, such as resource constraints, stragglers, network issues, device heterogeneity, etc. To this end, we developed a time-bounded asynchronous FL paradigm that can train a model on the continuous inflow of data in the edge-fog-cloud continuum. To test the FedStrag approach, a model is trained with multiple stragglers scenarios on both Independent and Identically Distributed (IID) and non-IID datasets on Raspberry Pis. The experiment results suggest that the FedStrag outperforms the baseline FedAvg in all possible cases.
{"title":"FedStrag: Straggler-aware federated learning for low resource devices","authors":"Aditya Kumar, Satish Narayana Srirama","doi":"10.1016/j.dcan.2024.12.004","DOIUrl":"10.1016/j.dcan.2024.12.004","url":null,"abstract":"<div><div>Federated Learning (FL) has become a popular training paradigm in recent years. However, stragglers are critical bottlenecks in an Internet of Things (IoT) network while training. These nodes produce stale updates to the server, which slow down the convergence. In this paper, we studied the impact of the stale updates on the global model, which is observed to be significant. To address this, we propose a weighted averaging scheme, FedStrag, that optimizes the training with stale updates. The work is focused on training a model in an IoT network that has multiple challenges, such as resource constraints, stragglers, network issues, device heterogeneity, etc. To this end, we developed a time-bounded asynchronous FL paradigm that can train a model on the continuous inflow of data in the edge-fog-cloud continuum. To test the FedStrag approach, a model is trained with multiple stragglers scenarios on both Independent and Identically Distributed (IID) and non-IID datasets on Raspberry Pis. The experiment results suggest that the FedStrag outperforms the baseline FedAvg in all possible cases.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1214-1224"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.10.008
Sai Li , Liang Yang , Yusheng Sun , Qianfen Jiao
This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication (MIMO-RF/UOWC), aiming to establish sea-based heterogeneous networks. In this setup, the RF links obey κ-μ fading, while the UOWC links undergo the generalized Gamma fading with the pointing error impairments. The relay operates under an Amplify-and-Forward (AF) protocol. Additionally, the attenuation caused by the Absorption and Scattering (AaS) is considered in UOWC links. The work yields precise results for the Average Channel Capacity (ACC), Outage Probability (OP), and average Bit Error Rate (BER). Furthermore, to reveal deeper insights, bounds on the ACC and asymptotic results for the OP and average BER are derived. The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output (SISO)-RF/UOWC AF systems. Various factors affecting the Diversity Gain (DG) of the MIMO-RF/UOWC AF system include the number of antennas/apertures, fading parameters of both links, and pointing error parameters. Moreover, while an increase in the AaS effect can result in significant attenuation, it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.
{"title":"Heterogeneous radio frequency/underwater optical wireless communication relaying systems with MIMO scheme","authors":"Sai Li , Liang Yang , Yusheng Sun , Qianfen Jiao","doi":"10.1016/j.dcan.2024.10.008","DOIUrl":"10.1016/j.dcan.2024.10.008","url":null,"abstract":"<div><div>This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication (MIMO-RF/UOWC), aiming to establish sea-based heterogeneous networks. In this setup, the RF links obey <em>κ</em>-<em>μ</em> fading, while the UOWC links undergo the generalized Gamma fading with the pointing error impairments. The relay operates under an Amplify-and-Forward (AF) protocol. Additionally, the attenuation caused by the Absorption and Scattering (AaS) is considered in UOWC links. The work yields precise results for the Average Channel Capacity (ACC), Outage Probability (OP), and average Bit Error Rate (BER). Furthermore, to reveal deeper insights, bounds on the ACC and asymptotic results for the OP and average BER are derived. The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output (SISO)-RF/UOWC AF systems. Various factors affecting the Diversity Gain (DG) of the MIMO-RF/UOWC AF system include the number of antennas/apertures, fading parameters of both links, and pointing error parameters. Moreover, while an increase in the AaS effect can result in significant attenuation, it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1018-1028"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.dcan.2024.10.019
Zhijun Han , Yiqing Zhou , Yu Zhang , Tong-Xing Zheng , Ling Liu , Jinglin Shi
In covert communications, joint jammer selection and power optimization are important to improve performance. However, existing schemes usually assume a warden with a known location and perfect Channel State Information (CSI), which is difficult to achieve in practice. To be more practical, it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI, which makes it difficult for legitimate transceivers to estimate the detection probability of the warden. First, the uncertainty caused by the unknown warden location must be removed, and the Optimal Detection Position (OPTDP) of the warden is derived which can provide the best detection performance (i.e., the worst case for a covert communication). Then, to further avoid the impractical assumption of perfect CSI, the covert throughput is maximized using only the channel distribution information. Given this OPTDP based worst case for covert communications, the jammer selection, the jamming power, the transmission power, and the transmission rate are jointly optimized to maximize the covert throughput (OPTDP-JP). To solve this coupling problem, a Heuristic algorithm based on Maximum Distance Ratio (H-MAXDR) is proposed to provide a sub-optimal solution. First, according to the analysis of the covert throughput, the node with the maximum distance ratio (i.e., the ratio of the distances from the jammer to the receiver and that to the warden) is selected as the friendly jammer (MAXDR). Then, the optimal transmission and jamming power can be derived, followed by the optimal transmission rate obtained via the bisection method. In numerical and simulation results, it is shown that although the location of the warden is unknown, by assuming the OPTDP of the warden, the proposed OPTDP-JP can always satisfy the covertness constraint. In addition, with an uncertain warden and imperfect CSI, the covert throughput provided by OPTDP-JP is 80% higher than the existing schemes when the covertness constraint is 0.9, showing the effectiveness of OPTDP-JP.
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