Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently r...Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).展开更多
In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations whic...In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.展开更多
Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offer...Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.展开更多
With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for opti...With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.展开更多
In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope wit...In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.展开更多
Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the...Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.展开更多
Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(...Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.展开更多
Increasing reliance on large-scale AI models has led to rising demand for intelligent services.The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time,and as a r...Increasing reliance on large-scale AI models has led to rising demand for intelligent services.The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time,and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption.However,finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services.To deal with this,we propose a distributed service caching scheme integrating deep reinforcement learning(DRL)with mobility prediction,which we refer to as DSDM.Specifically,we employ the D3QN(Deep Double Dueling Q-Network)framework to integrate Long Short-Term Memory(LSTM)predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training.Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.展开更多
Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of conge...Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes.Recently,Multi-access Edge Computing(MEC)-enabled heterogeneous networks,which leverage edge caches for proximity delivery,have emerged as a promising solution to all of these problems.Designing an effective edge caching scheme is critical to its success,however,in the face of limited resources.We propose a novel Knowledge Graph(KG)-based Dueling Deep Q-Network(KG-DDQN)for cooperative caching in MEC-enabled heterogeneous networks.The KGDDQN scheme leverages a KG to uncover video relations,providing valuable insights into user preferences for the caching scheme.Specifically,the KG guides the selection of related videos as caching candidates(i.e.,actions in the DDQN),thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN.Extensive simulation results validate the convergence effectiveness of the KG-DDQN,and it also outperforms baselines regarding cache hit rate and service delay.展开更多
In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic natu...In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 61972424 and 62372479in part by the High Value Intellectual Property Cultivation Project of Hubei Province,China,under grant D2021002094+1 种基金in part by JSPS KAKENHI under Grants JP16K00117 and JP19K20250in part by the Leading Initiative for Excellent Young Researchers(LEADER),MEXT,Japan,and KDDI Foundation.
文摘Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09
文摘In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.
文摘Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.
基金supported by the National Natural Science Foundation of China(NSFC)[Grant No.62072469].
文摘With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.
基金supported by National Natural Science Foundation of China(No.61821001)Science and Technology Key Project of Guangdong Province,China(2019B010157001).
文摘In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.
文摘Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.
基金supported by the National Natural Science Foundation of China under grants No.92267104 and 62372242。
文摘Increasing reliance on large-scale AI models has led to rising demand for intelligent services.The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time,and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption.However,finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services.To deal with this,we propose a distributed service caching scheme integrating deep reinforcement learning(DRL)with mobility prediction,which we refer to as DSDM.Specifically,we employ the D3QN(Deep Double Dueling Q-Network)framework to integrate Long Short-Term Memory(LSTM)predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training.Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.
基金supported by the National Natural Science Foundation of China(Nos.62201419,62372357)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LMX0032)the ISN State Key Laboratory.
文摘Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes.Recently,Multi-access Edge Computing(MEC)-enabled heterogeneous networks,which leverage edge caches for proximity delivery,have emerged as a promising solution to all of these problems.Designing an effective edge caching scheme is critical to its success,however,in the face of limited resources.We propose a novel Knowledge Graph(KG)-based Dueling Deep Q-Network(KG-DDQN)for cooperative caching in MEC-enabled heterogeneous networks.The KGDDQN scheme leverages a KG to uncover video relations,providing valuable insights into user preferences for the caching scheme.Specifically,the KG guides the selection of related videos as caching candidates(i.e.,actions in the DDQN),thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN.Extensive simulation results validate the convergence effectiveness of the KG-DDQN,and it also outperforms baselines regarding cache hit rate and service delay.
基金supported by the Liaoning Provincial Education Department Fund,grant number JYTZD2023083.
文摘In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
