Digital Communications and Networks最新文献

{"title":"An efficient conjunctive keyword searchable encryption for cloud-based IoT systems","authors":"Tianqi Peng ,&nbsp;Bei Gong ,&nbsp;Chong Guo ,&nbsp;Akhtar Badshah ,&nbsp;Muhammad Waqas ,&nbsp;Hisham Alasmary ,&nbsp;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}

{"title":"Capacity and delay performance analysis for large-scale UAV-enabled wireless networks","authors":"Bonan Yin,&nbsp;Chenxi Liu,&nbsp;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}

{"title":"Hierarchical flow learning for low-light image enhancement","authors":"Xinlin Yuan,&nbsp;Yong Wang,&nbsp;Yan Li,&nbsp;Hongbo Kang,&nbsp;Yu Chen,&nbsp;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}

{"title":"Joint jammer selection and power optimization in covert communications against a warden with uncertain locations","authors":"Zhijun Han ,&nbsp;Yiqing Zhou ,&nbsp;Yu Zhang ,&nbsp;Tong-Xing Zheng ,&nbsp;Ling Liu ,&nbsp;Jinglin Shi","doi":"10.1016/j.dcan.2024.10.019","DOIUrl":"10.1016/j.dcan.2024.10.019","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1114-1124"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925844","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}

{"title":"Enhancing flexibility and system performance in 6G and beyond: A user-based numerology and waveform approach","authors":"Mohamed S. Sayed ,&nbsp;Hatem M. Zakaria ,&nbsp;Abdelhady M. Abdelhady","doi":"10.1016/j.dcan.2024.10.020","DOIUrl":"10.1016/j.dcan.2024.10.020","url":null,"abstract":"<div><div>A Mixed Numerology OFDM (MN-OFDM) system is essential in 6G and beyond. However, it encounters challenges due to Inter-Numerology Interference (INI). The upcoming 6G technology aims to support innovative applications with high data rates, low latency, and reliability. Therefore, effective handling of INI is crucial to meet the diverse requirements of these applications. To address INI in MN-OFDM systems, this paper proposes a User-Based Numerology and Waveform (UBNW) approach that uses various OFDM-based waveforms and their parameters to mitigate INI. By assigning a specific waveform and numerology to each user, UBNW mitigates INI, optimizes service characteristics, and addresses user demands efficiently. The required Guard Bands (GB), expressed as a ratio of user bandwidth, vary significantly across different waveforms at an SIR of 25 dB. For instance, OFDM-FOFDM needs only 2.5%, while OFDM-UFMC, OFDM-WOLA, and conventional OFDM require 7.5%, 24%, and 40%, respectively. The time-frequency efficiency also varies between the waveforms. FOFDM achieves 85.6%, UFMC achieves 81.6%, WOLA achieves 70.7%, and conventional OFDM achieves 66.8%. The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 975-991"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925846","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}

{"title":"VANETs group message secure forwarding with trust evaluation","authors":"Lijun Wang ,&nbsp;Huajie Hao ,&nbsp;Chun Wang ,&nbsp;Xianzhou Han","doi":"10.1016/j.dcan.2024.11.007","DOIUrl":"10.1016/j.dcan.2024.11.007","url":null,"abstract":"<div><div>Efficient and safe information exchange between vehicles can reduce the probability of road accidents, thereby improving the driving experience of vehicles in Vehicular Ad Hoc Networks (VANETs). This paper proposes a group management algorithm with trust and mobility evaluation to address the enormous pressure on VANETs topology caused by high-speed vehicle movement and dynamic changes in the direction of travel. This algorithm utilizes historical interactive data to mine the fusion trust between vehicles. Then, combined with fusion mobility, the selection of center members and information maintenance of group members is achieved. Furthermore, based on bilinear pairing, an encryption protocol is designed to solve the problem of key management and update when the group structure changes rapidly, ensuring the safe forwarding of messages within and between groups. Numerical analysis shows that the algorithm in the paper ensures group stability and improves performance such as average message delivery rate and interaction delay.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1150-1157"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926847","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}

{"title":"Efficient modulation mode recognition based on joint communication parameter estimation in non-cooperative scenarios","authors":"Xiangdong Huang ,&nbsp;Yimin Wang ,&nbsp;Yanping Li ,&nbsp;Xiaolei Wang","doi":"10.1016/j.dcan.2024.10.016","DOIUrl":"10.1016/j.dcan.2024.10.016","url":null,"abstract":"<div><div>Due to the neglect of the retrieval of communication parameters (including the symbol rate, the symbol timing offset, and the carrier frequency), the existing non-cooperative communication mode recognizers suffer from the generality ability degradation and severe difficulty in distinguishing a large number of modulation modes, etc. To overcome these drawbacks, this paper proposes an efficient communication mode recognizer consisting of communication parameter estimation, the constellation diagram retrieval, and a classification network. In particular, we define a 2-D symbol synchronization metric to retrieve both the symbol rate and the symbol timing offset, whereas a constellation dispersity annealing procedure is devised to correct the carrier frequency accurately. Owing to the accurate estimation of these crucial parameters, high-regularity constellation maps can be retrieved and thus simplify the subsequent classification work. Numerical results show that the proposed communication mode recognizer acquires higher classification accuracy, stronger anti-noise robustness, and higher applicability of distinguishing multiple types, which presents the proposed scheme with vast applicable potentials in non-cooperative scenarios.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1080-1090"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926844","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}

{"title":"Accurate and efficient elephant-flow classification based on co-trained models in evolved software-defined networks","authors":"Ling Xia Liao ,&nbsp;Changqing Zhao ,&nbsp;Jian Wang ,&nbsp;Roy Xiaorong Lai ,&nbsp;Steve Drew","doi":"10.1016/j.dcan.2024.10.017","DOIUrl":"10.1016/j.dcan.2024.10.017","url":null,"abstract":"<div><div>Accurate early classification of elephant flows (elephants) is important for network management and resource optimization. Elephant models, mainly based on the byte count of flows, can always achieve high accuracy, but not in a time-efficient manner. The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks (SDNs) to achieve a better resource efficiency. This paper addresses this situation by combining co-training and Reinforcement Learning (RL) to enable a closed-loop classification approach that divides the entire classification process into episodes, each involving two elephant models. One predicts elephants and is retrained by a selection of flows automatically labeled online by the other. RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase. Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%, and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1091-1102"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925895","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}

{"title":"Energy-saving control strategy for ultra-dense network base stations based on multi-agent reinforcement learning","authors":"Yan Zhen ,&nbsp;Litianyi Tao ,&nbsp;Dapeng Wu ,&nbsp;Tong Tang ,&nbsp;Ruyan Wang","doi":"10.1016/j.dcan.2024.10.015","DOIUrl":"10.1016/j.dcan.2024.10.015","url":null,"abstract":"<div><div>Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network (UDN) and focuses on solving the resulting challenge of increased energy consumption. A base station control algorithm based on Multi-Agent Proximity Policy Optimization (MAPPO) is designed. In the constructed 5G UDN model, each base station is considered as an agent, and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance. To reduce the extra power consumption due to frequent sleep mode switching of base stations, a sleep mode switching decision algorithm is proposed. The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent's action strategy. Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1007-1017"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926835","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}

{"title":"A deep-learning-based MAC for integrating channel access, rate adaptation, and channel switch","authors":"Jiantao Xin ,&nbsp;Wei Xu ,&nbsp;Bin Cao ,&nbsp;Taotao Wang ,&nbsp;Shengli Zhang","doi":"10.1016/j.dcan.2024.10.010","DOIUrl":"10.1016/j.dcan.2024.10.010","url":null,"abstract":"<div><div>With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation, and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switching into DL-MAC, enhancing its functionality from single-channel to multi-channel operations. Specifically, the DL-MAC protocol incorporates a Deep Neural Network (DNN) for channel selection and a Recurrent Neural Network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC. Experimental results demonstrate that DL-MAC exhibits significantly superior performance compared to traditional algorithms in both single and multi-channel environments, and also outperforms single-function designs. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overheads within the evaluation range.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1042-1054"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926841","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}