IEEE Transactions on Network Science and Engineering最新文献_第3页

A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty 拓扑不确定性下探索性学习辅助社区检测的统一框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-02 DOI: 10.1109/TNSE.2025.3557041

Yu Hou;Cong Tran;Ming Li;Won-Yong Shin

{"title":"A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty","authors":"Yu Hou;Cong Tran;Ming Li;Won-Yong Shin","doi":"10.1109/TNSE.2025.3557041","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3557041","url":null,"abstract":"In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often <italic>uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present <monospace>META-CODE</monospace>, a unified framework for detecting overlapping communities via <italic>exploratory learning aided by <italic>easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, <monospace>META-CODE</monospace> consists of three iterative steps in addition to the initial network inference step: 1) node-level <italic>community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) <italic>network exploration via community-affiliation-based node queries, and 3) <italic>network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority of <monospace>META-CODE</monospace> over benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module in <monospace>META-CODE</monospace>, (c) the effectiveness of node queries in <monospace>META-CODE</monospace> based on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3159-3176"},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492339","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}

引用次数: 0

Attack Detection and Location Using State Forecasting in Multivariate Time Series of ICS 基于状态预测的ICS多变量时间序列攻击检测与定位

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3555764

Guoyan Cao;Yue Wu;Dengxiu Yu;Zhen Wang

引用次数: 0

QECO: A QoE-Oriented Computation Offloading Algorithm Based on Deep Reinforcement Learning for Mobile Edge Computing QECO：一种面向qoe的基于深度强化学习的移动边缘计算卸载算法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556809

Iman Rahmaty;Hamed Shah-Mansouri;Ali Movaghar

{"title":"QECO: A QoE-Oriented Computation Offloading Algorithm Based on Deep Reinforcement Learning for Mobile Edge Computing","authors":"Iman Rahmaty;Hamed Shah-Mansouri;Ali Movaghar","doi":"10.1109/TNSE.2025.3556809","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556809","url":null,"abstract":"In the realm of mobile edge computing (MEC), efficient computation task offloading plays a pivotal role in ensuring a seamless quality of experience (QoE) for users. Maintaining a high QoE is paramount in today's interconnected world, where users demand reliable services. This challenge stands as one of the most primary key factors contributing to handling dynamic and uncertain mobile environments. In this study, we delve into computation offloading in MEC systems, where strict task processing deadlines and energy constraints can adversely affect the system performance. We formulate the computation task offloading problem as a Markov decision process (MDP) to maximize the long-term QoE of each user individually. We propose a distributed QoE-oriented computation offloading (QECO) algorithm based on deep reinforcement learning (DRL) that empowers mobile devices to make their offloading decisions without requiring knowledge of decisions made by other devices. Through numerical studies, we evaluate the performance of QECO. Simulation results reveal that compared to the state-of-the-art existing works, QECO increases the number of completed tasks by up to 14.4%, while simultaneously reducing task delay and energy consumption by 9.2% and 6.3%, respectively. Together, these improvements result in a significant average QoE enhancement of 37.1%. This substantial improvement is achieved by accurately accounting for user dynamics and edge server workloads when making intelligent offloading decisions. This highlights QECO's effectiveness in enhancing users' experience in MEC systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3118-3130"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492445","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}

引用次数: 0

A Blockchain-Assisted Authentication Protocol for RFID-Enabled Supply Chain Management System 基于rfid的供应链管理系统的区块链辅助认证协议

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556736

Tayyaba Tariq;Mohammad S. Obaidat;Wen-Chung Kuo;Khalid Mahmood;Muhammad Faizan Ayub;Mohammed J.F. Alenazi

{"title":"A Blockchain-Assisted Authentication Protocol for RFID-Enabled Supply Chain Management System","authors":"Tayyaba Tariq;Mohammad S. Obaidat;Wen-Chung Kuo;Khalid Mahmood;Muhammad Faizan Ayub;Mohammed J.F. Alenazi","doi":"10.1109/TNSE.2025.3556736","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556736","url":null,"abstract":"In the dynamic domain of supply chain management, integrating Radio-Frequency Identification (RFID) technology with blockchain technology represents a significant leap forward. This integration has created a blockchain-assisted RFID-enabled Supply Chain Management System (SCMS). With its ability to utilize electromagnetic fields to identify and track tags attached to objects, RFID technology has revolutionized product management and tracking within supply chains. SCMS transmits tracking and product management information through public communication channels. However, SCMS’s communication on these channels is vulnerable to various security attacks and privacy challenges. Numerous authentication protocols have recently been proposed to tackle these security and privacy challenges. Unfortunately, most protocols are susceptible to different security attacks, such as tag or reader impersonation, denial of service, physical cloning, desynchronization attacks, etc. Therefore, we have proposed an authentication protocol for an RFID-enabled SCMS that also leverages blockchain technology. The integration of blockchain technology ensures data integrity, immutability and transparency across each department involved in SCMS. In the proposed protocol, we also employ a Physically Unclonable Function (PUF) to secure SCMS against physical cloning attacks. We validate the security of the proposed protocol through both informal and formal analysis. The informal analysis confirms that our protocol significantly enhances security and efficiency. Moreover, a performance analysis of the proposed protocol against various competing existing protocols shows its superior performance. Notably, the proposed protocol substantially reduces computational and communication costs by 24.38% and 8.03%, respectively, which underscores its enhanced performance and resource efficiency.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3108-3117"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492275","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}

引用次数: 0

An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning 基于多任务深度强化学习的自适应服务功能链映射

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556390

Wenting Wei;Qingyi Wang;Huaxi Gu;Danyang Zheng;Ning Zhang;Celimuge Wu

{"title":"An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning","authors":"Wenting Wei;Qingyi Wang;Huaxi Gu;Danyang Zheng;Ning Zhang;Celimuge Wu","doi":"10.1109/TNSE.2025.3556390","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556390","url":null,"abstract":"Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3093-3107"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492430","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}

引用次数: 0

Temporal Graph Reproduction With RWIG 用RWIG再现时间图

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3555974

Sergey Shvydun;Anton-David Almasan;Piet Van Mieghem

引用次数: 0

Advancing Non-Intrusive Load Monitoring: Predicting Appliance-Level Power Consumption With Indirect Supervision 推进非侵入式负荷监测：用间接监测预测电器级电力消耗

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3555618

Jialing He;Junsen Feng;Shangwei Guo;Zhuo Chen;Yiwei Liu;Tao Xiang;Liehuang Zhu

{"title":"Advancing Non-Intrusive Load Monitoring: Predicting Appliance-Level Power Consumption With Indirect Supervision","authors":"Jialing He;Junsen Feng;Shangwei Guo;Zhuo Chen;Yiwei Liu;Tao Xiang;Liehuang Zhu","doi":"10.1109/TNSE.2025.3555618","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3555618","url":null,"abstract":"Deep Neural Networks (DNNs) have made significant progress in addressing the Non-Intrusive Load Monitoring (NILM) task, which aims to disaggregate appliance-level power signals from aggregated meter readings. Despite these advancements, existing DNN-based NILM approaches rely on training with power signals from individual appliances, which are obtained intrusively through sensor installations. This method is not only expensive but also poses a risk of damaging the original circuits. To overcome these limitations, we introduce the State-based Supervised NILM (SS-NILM) problem. Instead of using appliance power signal labels, we leverage on-off state information, which can be collected in a non-intrusive manner. However, solving SS-NILM presents a challenge, as it requires developing a model that maps on-off state labels to the corresponding appliance power signals in an indirectly supervised setting. In this work, we propose a state-based DNN that predicts the power signals of multiple target appliances simultaneously. The model is trained by minimizing the discrepancy between the aggregated prediction and the true aggregated power signal. Additionally, the model predicts the on-off states of appliances, which are used as auxiliary information to improve the accuracy of power signal predictions. Extensive experiments conducted on real-world datasets demonstrate that our model, trained using non-intrusive on-off state information, achieves performance comparable to that of traditional NILM models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2957-2973"},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492213","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}

引用次数: 0

Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation 基因调控神经网络计算在生物膜形成调控中的应用

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3555962

Adrian Ratwatte;Samitha Somathilaka;Sasitharan Balasubramaniam;Megan Taggart;Keerthi M. Nair;Alan O'Riordan;James Dooley

{"title":"Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation","authors":"Adrian Ratwatte;Samitha Somathilaka;Sasitharan Balasubramaniam;Megan Taggart;Keerthi M. Nair;Alan O'Riordan;James Dooley","doi":"10.1109/TNSE.2025.3555962","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3555962","url":null,"abstract":"The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the Artificial Neural Network (ANN), which can be transformed into a Gene Regulatory Neural Network (GRNN). This transformation enables exploration of their natural computing capabilities regarding network reconfigurability and controllability, facilitating dynamic adjustments of gene-gene interaction weights to regulate biological processes. In this paper, we present a control-theoretic model for the GRNN that determines optimal chemical input concentrations, steering the GRNN towards desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This method enhances network robustness by balancing stability and reconfigurability, ensuring responsive weight adjustments in dynamic environments. We develop mathematical models to identify critical genes using a Continuous-Time Markov Chain (CTMC) and derive temporal weight configurations, providing insights into the system's reconfiguration dynamics, while also quantifying stability and reconfigurability. Our findings demonstrate the effectiveness of the control model in mitigating <italic>Clostridioides difficile biofilm formation, outperforming sub-optimal and stochastic perturbation inputs, and highlighting the importance of determining optimal inputs for robust network behavior across diverse complexities.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3002-3014"},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492475","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}

引用次数: 0

An Enhanced Certificateless Blockchain-Assisted Authentication and Key Agreement Protocol for Internet of Drones 无人机互联网增强无证书区块链辅助认证与密钥协商协议

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3556400

Jintian Zhang;Xingxing Chen;Qingfeng Cheng;Xiaofeng Chen;Xiangyang Luo

{"title":"An Enhanced Certificateless Blockchain-Assisted Authentication and Key Agreement Protocol for Internet of Drones","authors":"Jintian Zhang;Xingxing Chen;Qingfeng Cheng;Xiaofeng Chen;Xiangyang Luo","doi":"10.1109/TNSE.2025.3556400","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556400","url":null,"abstract":"The consumer drone market has grown rapidly, making it necessary to integrate drones with the internet to explore low-altitude areas. Internet of Drones (IoD) offers a more potent data stream to connect the global Big Data system, it will also have to deal with the issue of exposure and malicious use of flight trajectory, communication data, and identity information when a cyber attacker unlawfully eavesdrops, gains access to, attacks, or even takes control of drones. Numerous authentication protocols have been developed for the IoD context in order to solve the aforementioned issues. Ali et al. provide a cross-domain communication scheme (henceforth known as the AJ protocol) in IoD environment by utilizing blockchain-assisted authentication to improve the security of IoD data transmission. Nevertheless, this scheme is vulnerable to key compromise impersonation attack and fails to achieve the crucial security attribute of anonymity. In order to address the aforementioned security concerns, we develop an enhanced certificateless authentication and key agreement (CL-AKA) protocol in IoD environment, based on blockchain technology and Chebyshev chaotic mapping. Our protocol can achieve necessary security attributes for IoD and withstand a variety of known attacks, as demonstrated by proof of security under the eCK model and automated validation by the Scyther tool. Additionally, our protocol performs better in terms of computation and communication overhead when compared to similar protocols to achieve lightweight anonymous authentication.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3065-3081"},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492418","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}

引用次数: 0

Opinion Dynamics Considering Matthew Effect With Time Delays and Stubborn Influence in Social Networks 考虑时滞和顽固影响下马太效应的社会网络舆论动态

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3556379

Meng Li;Jinyuan Zhang;Long Jin

{"title":"Opinion Dynamics Considering Matthew Effect With Time Delays and Stubborn Influence in Social Networks","authors":"Meng Li;Jinyuan Zhang;Long Jin","doi":"10.1109/TNSE.2025.3556379","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556379","url":null,"abstract":"In social networks, stubborn individuals are resistant to changing their opinions or positions, affecting the trend of opinion evolution. Communication among individuals inherently involves time delays, which could lead to instability in information dissemination between individuals. To address these gaps in existing works, a new Matthew effect with time delays and stubborn influence (METS) model is proposed. In this paper, stubbornness coefficients are introduced to quantify individuals' adherence to their initial opinions, and a new approach to assess the speed of opinion development is proposed. Additionally, the social network is modeled as a distributed communication system that incorporates time delays to depict the connections between opinions. Furthermore, the <inline-formula><tex-math>$k$</tex-math></inline-formula>-winners-take-all (<inline-formula><tex-math>$k$</tex-math></inline-formula>-WTA) operation is employed as the feedback mechanism of the model to differentiate the winners and losers within the Matthew effect. Then, a thorough analysis of the model's convergence and stability is provided. Besides, numerical experiments demonstrate the flexibility and practicality of the METS model. Finally, extensive simulations are conducted to examine the influence of individual stubbornness on the dynamics of opinion evolution.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3082-3092"},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492407","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}

引用次数: 0