IEEE Systems Journal最新文献_第2页

Architecting Path Selection Method for Incremental Evolution in System-of-Systems 系统-系统中增量进化的架构路径选择方法

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-04-09 DOI: 10.1109/JSYST.2025.3553965

Zhemei Fang;Dazhi Chen;Qi Ju;Jianbo Wang

{"title":"Architecting Path Selection Method for Incremental Evolution in System-of-Systems","authors":"Zhemei Fang;Dazhi Chen;Qi Ju;Jianbo Wang","doi":"10.1109/JSYST.2025.3553965","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3553965","url":null,"abstract":"Architecture design for system-of-systems (SoSs) is a complex challenge due to interdependencies, uncertainties, and the large design space. The evolutionary nature of SoSs necessitates a multistage architecting process, adding further complexity. This article, thus, proposes a deep reinforcement learning based evolutionary architecture path selection method that considers uncertainties and interdependency. The approach employs an architecture framework to guide the design and defines SoS architecture decisions as the addition of systems and the allocation of operational architecture to physical architecture across sequential stages. Capability evaluation leverages a capability-activity-system structure, supported by a functional dependency network analysis method. Utilizing a deep neural network as a functional approximator to predict future SoS capability, the article develops a proximal policy optimization (PPO) algorithm that balances immediate and future needs. Applied to a mosaic warfare-oriented naval antisubmarine SoS, the proposed method outperforms heuristic optimization techniques by achieving higher SoS capability, reduced instability, and fewer violations of budget and intermediate requirements constraints in both deterministic and stochastic scenarios. These results highlight the PPO method's effectiveness in addressing SoS architecting path selection challenges under uncertainty.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"636-647"},"PeriodicalIF":4.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

GA-Optimized Co-Design of Jump-Like FlexRay Protocol and Dynamic Control for NCSs and Its Applications 类跳跃FlexRay协议与ncs动态控制的ga优化协同设计及其应用

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-04-08 DOI: 10.1109/JSYST.2025.3550559

Tao Yu;Hao Xu;Shuping He

{"title":"GA-Optimized Co-Design of Jump-Like FlexRay Protocol and Dynamic Control for NCSs and Its Applications","authors":"Tao Yu;Hao Xu;Shuping He","doi":"10.1109/JSYST.2025.3550559","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3550559","url":null,"abstract":"This article is considered with the co-design problem of jump-like FlexRay protocol (FRP) and dynamic control for a class of discrete-time networked systems. A jump-like FRP is proposed to address the constraints of communication resources as well as nonperiodic denial of service (DoS) attacks in the sensor-to-controller communication network. The proposed novel protocol has the characteristics of traditional FRP time-triggered and event-triggered mechanisms. In addition, such protocol is able to avoid selecting sensors affected by DoS attacks. Subsequently, a set of dynamic output feedback controllers related to the selection of sensor nodes is designed to guarantee the finite-time boundedness of the closed-loop system with the prescribed <inline-formula><tex-math>$H_infty$</tex-math></inline-formula> performance. However, the co-design problem of protocol and dynamic control includes more nonlinear terms, making the problem more challenging to be solved. In order to address the co-design problem and enhance system performance, a genetic-algorithm-based controller design approach has been proposed. Finally, a numerical example and a two-area power system example are given to illustrate the effectiveness of the proposed method.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"670-681"},"PeriodicalIF":4.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

$mu$-Trust: Trustworthy and Transparent Service Composition for Microservice-Based IoT Systems $mu$-Trust：基于微服务的物联网系统的可信和透明的服务组合

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-04-01 DOI: 10.1109/JSYST.2025.3547967

Prajnamaya Dass;Sudip Misra

{"title":"$mu$-Trust: Trustworthy and Transparent Service Composition for Microservice-Based IoT Systems","authors":"Prajnamaya Dass;Sudip Misra","doi":"10.1109/JSYST.2025.3547967","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3547967","url":null,"abstract":"The distributed Internet of Things (IoT) systems facilitate real-time services through the composition of loosely coupled microservices. The composed IoT service is the output of multiple microservices, executed at computationally capable edge or fog nodes, which we consider as the facility nodes (FNs). However, the service composition process in IoT microservice architectures is abstracted from the users that gives freedom to the FNs to act maliciously and provide low-quality IoT services. Further, the service composition needs to be transparent so that the FNs involved in a service cannot repudiate their involvement at a later time. In this article, we propose a novel, lightweight, trustworthy, and verifiable service composition framework for IoT-based systems that adopt microservice architecture. First, we propose a dynamic programming approach to select trustworthy FNs for each user request, while considering the trust scores of the FNs and the delay requirements of the users. Next, we propose a transparent service composition framework that uses lightweight cryptography functions to generate the proof-of-involvement for the FNs in each service. With the help of a trust controller, we verify the proofs generated by the FNs and update the trust scores of the FNs. Considering the user traces from Berlin city in the simulation of urban mobility tool, we show the efficacy of the proposed framework in maximizing user trust and detecting malicious FNs involved in user services. Further, we show that the delay and communication overhead of the proposed framework are very low compared to the state-of-the-art methods.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"404-412"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation NB-IoT系统级仿真框架：关键特性和性能评估

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-29 DOI: 10.1109/JSYST.2025.3569189

Shutao Zhang;Wenkun Wen;Peiran Wu;Hongqing Huang;Liya Zhu;Yijia Guo;Tingting Yang;Minghua Xia

{"title":"System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation","authors":"Shutao Zhang;Wenkun Wen;Peiran Wu;Hongqing Huang;Liya Zhu;Yijia Guo;Tingting Yang;Minghua Xia","doi":"10.1109/JSYST.2025.3569189","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3569189","url":null,"abstract":"Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3 rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3GPP. This article presents a system-level simulation framework for NB-IoT networks to evaluate their performance. The system-level simulator is structured into four parts: Initialization, pregeneration, main simulation loop, and postprocessing. In addition, three essential features are investigated to enhance coverage, support massive connections, and ensure low power consumption. Simulation results demonstrate that the cumulative distribution function curves of the signal-to-interference-and-noise ratio fully comply with industrial standards. Furthermore, the throughput performance explains how NB-IoT networks realize massive connections at the cost of data rate. This work highlights its practical utility and paves the way for developing NB-IoT networks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"577-588"},"PeriodicalIF":4.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Improved GBNN Guided Multirobot Coverage Search Based on Neuronal Connectivity 基于神经元连通性的改进GBNN引导多机器人覆盖搜索

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-28 DOI: 10.1109/JSYST.2025.3567283

Fangfang Zhang;Yongqi Wang;Jianbin Xin;Haijing Wang;Jinzhu Peng;Yaonan Wang

{"title":"Improved GBNN Guided Multirobot Coverage Search Based on Neuronal Connectivity","authors":"Fangfang Zhang;Yongqi Wang;Jianbin Xin;Haijing Wang;Jinzhu Peng;Yaonan Wang","doi":"10.1109/JSYST.2025.3567283","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3567283","url":null,"abstract":"The multirobot coverage search problem in unknown environments has attracted significant attention. However, the existing methods are inefficient in the search process. The aim of the present study is to improve the search efficiency through an enhanced bioinspired neural network method. In this work, a connected Glasius bioinspired neural network (CGBNN) model is introduced to address the lack of consideration for neuronal connectivity and transmission properties in existing studies. The dynamic search environment is represented by the changes in neurons' activity values, which guide the robots in performing the search task. Each robot automatically plans its search path according to the principle of the decreasing gradient of CGBNN activity values until the task is completed. Experimental results demonstrate that the robots can avoid different types of obstacles to complete the coverage search, confirming the effectiveness of the proposed method. Meanwhile, it indicates that the proposed method outperforms others, the coverage rate is improved by 6.90%, 6.22%, and 4.02% compared to the GBNN, A-RPSO, and DMPC algorithms, respectively. In adition, the decision time is less affected by the complexity of the environment, which fulfills the practical demands of real-time decision-making in a large-scale complex environment.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"701-711"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

TL-ConvLSTM: A Transfer-Learning-Based Convolutional LSTM to Identify and Forecast Traffic in the NextG Environments TL-ConvLSTM：一种基于迁移学习的卷积LSTM，用于识别和预测未来环境中的流量

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-28 DOI: 10.1109/JSYST.2025.3569445

Bikash Chandra Singh;Peter Foytik;Rafael Diaz;Sachin Shetty

{"title":"TL-ConvLSTM: A Transfer-Learning-Based Convolutional LSTM to Identify and Forecast Traffic in the NextG Environments","authors":"Bikash Chandra Singh;Peter Foytik;Rafael Diaz;Sachin Shetty","doi":"10.1109/JSYST.2025.3569445","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3569445","url":null,"abstract":"Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called <italic>TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the training of <italic>TL-ConvLSTM by estimating its parameters from the source domain. We then employ the Kolmogorov–Smirnov method to adapt the model within the target domain, fine tuning its weights. To improve the precision of this model adaptation, we systematically explore optimal learning windows. This exploration includes adjusting window size for time-series data and feature dimensions to capture dynamic traffic patterns in a 5G environment. Furthermore, we make use of the Amarisoft 5G testbed in our lab to create a 12-day time-series dataset. This dataset includes various features related to traffic flows and their patterns. We showcase the effectiveness of our approach through a set of experiments.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"358-369"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DAMAGE: Directed Heterogeneous Network Attack Sequence Inference Through Graph Attention Matrix Generation Embedding and Reinforcement Learning 损害：通过图注意矩阵生成嵌入和强化学习的定向异构网络攻击序列推断

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-28 DOI: 10.1109/JSYST.2025.3547491

Hongfu Liu;Chengyi Zeng;Zhen Li;Lina Lu;Jing Chen;Zongtan Zhou

{"title":"DAMAGE: Directed Heterogeneous Network Attack Sequence Inference Through Graph Attention Matrix Generation Embedding and Reinforcement Learning","authors":"Hongfu Liu;Chengyi Zeng;Zhen Li;Lina Lu;Jing Chen;Zongtan Zhou","doi":"10.1109/JSYST.2025.3547491","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3547491","url":null,"abstract":"Distributed heterogeneous multiagent systems (DHMASs) link geographically dispersed agents through networks, harnessing information technology to foster collaboration. Considering the mainstream status of wireless communication in modern multiagent systems and the differences in the performance of interagent communication devices, we believe that it is appropriate to use directed heterogeneous networks (DHNs) to model distributed heterogeneous multiagent systems. This model not only reflects the directionality of interagent communication but also reflects the complexity of communication due to performance differences, thus providing a more accurate framework for understanding and optimizing system behavior. The study of disintegration in DHNs is vital for enhancing the decision-making agility of DHMAS. We introduce <underline>Directed heterogeneous network <underline>Attack sequence inference through graph attention <underline>MAtrix <underline>Generation <underline>Embedding and reinforcement learning (DAMAGE), an algorithm that integrates graph neural networks and reinforcement learning within an inductive reasoning framework. DAMAGE is designed to optimize the generation of disintegration strategies, improving the efficiency of network breakdown processes. Our approach includes a directed network embedding technique with a graph attention matrix generation module, which enhances the utilization of imperfect network structure information. Through ablation studies, we demonstrate that DAMAGE not only increases the effectiveness of network disintegration under perfect topological conditions but also maintains robustness in scenario with imperfect topological information.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"392-403"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems 云系统中qos感知实时作业调度的量子强化学习

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-27 DOI: 10.1109/JSYST.2025.3568752

Shuhong Dai;Nishant Saurabh;Qingle Wang;Jiawei Nian;Shuwen Kan;Ying Mao;Long Cheng

{"title":"Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems","authors":"Shuhong Dai;Nishant Saurabh;Qingle Wang;Jiawei Nian;Shuwen Kan;Ying Mao;Long Cheng","doi":"10.1109/JSYST.2025.3568752","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3568752","url":null,"abstract":"Effective cloud job scheduling is essential for enhancing the performance and operational efficiency of cloud-based services, directly impacting their quality of service (QoS). Among existing methodologies, deep reinforcement learning (DRL) has proven effective in addressing complex, multidimensional optimization challenges in real-time scheduling. With advancements in quantum computing, quantum neural networks (QNNs) are showing unique advantages in information representation and processing. This study is the first to explore quantum reinforcement learning (QRL) for real-time job scheduling in cloud systems. Specifically, we propose a QRL framework that utilizes variational and encoding layers to convert state information into quantum data, repeatedly embedded into a QNN to compute optimal value returns. This approach aims to enhance QoS by improving job execution success rates and reducing average response times with unpredictable job arrivals. We present the detailed design of our approach, and our simulation results demonstrate that the QRL method significantly exceeds established baselines, including those based on DRL, across a range of workload intensities and computational resource configurations. This is particularly evident under high-load conditions, where our approach can achieve 55.2% higher success rates, underscoring its significant potential in cloud job scheduling optimization.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"471-482"},"PeriodicalIF":4.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Permutation-Based Firmware Remote Attestation for Internet-of-Things Edge-Based Network 基于置换的物联网边缘网络固件远程认证

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-27 DOI: 10.1109/JSYST.2025.3550055

Zainab AlJabri;Jemal H. Abawajy

引用次数: 0

Path Planning for Cooperative Aerial Load Transportation in Complex Environments 复杂环境下协同空运路径规划

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2025-03-21 DOI: 10.1109/JSYST.2025.3547065

Peyman Abeshtan;Fariborz Saghafi

引用次数: 0