IEEE Transactions on Network Science and Engineering最新文献

Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation 基于学习自动机的协同MIMO编队优化水下磁感应声传感器网络的能耗和覆盖

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-08-22 DOI: 10.1109/TNSE.2025.3561751

Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno

{"title":"Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation","authors":"Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno","doi":"10.1109/TNSE.2025.3561751","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561751","url":null,"abstract":"Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3527-3540"},"PeriodicalIF":7.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891284","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

Retraction Notice: Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network 撤回通知：区块链和深度学习在数字孪生驱动的工业物联网网络中的安全通信

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-27 DOI: 10.1109/TNSE.2025.3583800

Prabhat Kumar;Randhir Kumar;Abhinav Kumar;A. Antony Franklin;Sahil Garg;Satinder Singh

{"title":"Retraction Notice: Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network","authors":"Prabhat Kumar;Randhir Kumar;Abhinav Kumar;A. Antony Franklin;Sahil Garg;Satinder Singh","doi":"10.1109/TNSE.2025.3583800","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3583800","url":null,"abstract":"The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4329-4329"},"PeriodicalIF":7.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction Notice: A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT 一种基于dqn的边缘IoMT帧聚合和任务卸载方法

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-23 DOI: 10.1109/TNSE.2025.3582063

Xiaoming Yuan;Zedan Zhang;Chujun Feng;Yejia Cui;Sahil Garg;Georges Kaddoum;Keping Yu

{"title":"Retraction Notice: A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT","authors":"Xiaoming Yuan;Zedan Zhang;Chujun Feng;Yejia Cui;Sahil Garg;Georges Kaddoum;Keping Yu","doi":"10.1109/TNSE.2025.3582063","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3582063","url":null,"abstract":"The rapid expansion of wearable medical devices and health data of Internet of Medical Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health care in the foreseeable 6 G era. Healthcare applications and services require ultra reliable, ultra low delay and energy consumption data communication and computing. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies empowered IoMT to deal with huge data sensing, processing and transmission in high QoS. However, traditional frame aggregation schemes in WBAN generate too much control frames during data transmission, which leads to high delay and energy consumption and is not flexible enough. In this paper, a Deep Q-learning Network (DQN) based Frame Aggregation and Task Offloading Approach (DQN-FATOA) is proposed. Firstly, different service data were divided into queues with similar QoS requirements. Then, the length of the frame aggregation was selected dynamically by the aggregation node according to the delay, energy consumption, and throughput by DQN. Finally, the number of tasks offloaded was selected due to the current state. Compared with the traditional scheme, the simulation results show that the proposed DQN-FATOA has effectively reduced delay and energy consumption, and improved the throughput and overall utilization of WBAN.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4330-4330"},"PeriodicalIF":7.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11048693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Fusion Deception Attacks Design and Game Analysis in Multi-Sensor Systems 多传感器系统中最优融合欺骗攻击设计与博弈分析

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-30 DOI: 10.1109/TNSE.2025.3565903

Chuanyi Ning;Fei Hao;Jiping Yang

引用次数: 0

Delay-Oriented Transaction Packaging Ordering in Sharded Blockchains 分片区块链中面向延迟的交易打包排序

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-30 DOI: 10.1109/TNSE.2025.3564740

Yuqi Fan;Xiaoyu Wang;Weili Wu;Dingzhu Du

{"title":"Delay-Oriented Transaction Packaging Ordering in Sharded Blockchains","authors":"Yuqi Fan;Xiaoyu Wang;Weili Wu;Dingzhu Du","doi":"10.1109/TNSE.2025.3564740","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3564740","url":null,"abstract":"In blockchain, miners make profits by packaging transactions. There are a large number of pending transactions in the blockchain, and users often assign high transaction fees to reduce the delay in transaction packaging. However, in sharded blockchains, it requires more computational and communication resources to process cross-shard transactions than intra-shard transactions. As a result, miners are inclined to package intra-shard transactions to maximize their profits when packaging transactions, and cross-shard transactions with higher transaction fees are unable to be processed in time, which degrades the quality of service (QoS) of users. That is, the existing transaction packaging methods lack a clear mechanism to ensure that the QoS of users is directly related to the transaction fees they pay. In this paper, we design a new incentive mechanism, such that the block reward is not a fixed value but is related to the transaction processing delay and the number of cross-shard communications involved. The block reward can encourage miners to process transactions in time. We then study how to maximize the total miners' profit by determining the appropriate transaction packaging order to provide QoS to users. Specifically, we model the problem of transaction packaging ordering in each shard as a delay-oriented transaction processing problem and propose a Transaction Delay Optimization algorithm (TDO). Theoretical analysis proves that TDO is an approximation algorithm for the transaction packaging ordering problem with the approximation ratio of <inline-formula><tex-math>$frac{eC}{({e - 1})C - 2eS_{text{max}}}$</tex-math></inline-formula>, given block capacity <inline-formula><tex-math>$C$</tex-math></inline-formula> and maximum transaction size <inline-formula><tex-math>$S_{text{max}}$</tex-math></inline-formula>. We conduct experiments through simulations. Simulation results show that TDO can effectively provide QoS to users and maximize the profits of miners.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3731-3743"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891074","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

Optimizing Resource Allocation for Dynamic IoT Requests Using Network Function Virtualization 利用网络功能虚拟化优化动态物联网请求的资源分配

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-30 DOI: 10.1109/TNSE.2025.3565736

Tuan-Minh Pham;Thi-Minh Nguyen

{"title":"Optimizing Resource Allocation for Dynamic IoT Requests Using Network Function Virtualization","authors":"Tuan-Minh Pham;Thi-Minh Nguyen","doi":"10.1109/TNSE.2025.3565736","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3565736","url":null,"abstract":"Network Function Virtualization (NFV) is essential for ensuring efficient and scalable Internet-of-Things(IoT) networks. However, optimizing resource allocation in an NFV-enabled IoT (NIoT) system is challenging, particularly when IoT functions are distributed as Virtual Network Functions (VNFs). This paper presents an approach for optimizing function placement in a dynamic NIoT system deployed within a hierarchical edge cloud computing environment. We propose an integer linear programming model and approximation algorithms to maximize the number of satisfied requests while minimizing system costs for a given set of service requests. Additionally, we develop a deep reinforcement learning-based algorithm (RTL) to determine the optimal timing for relocating IoT functions as bandwidth requirements change. Our evaluation measures several key metrics, including deployment cost, end-to-end delay, and request acceptance ratio. The results demonstrate that the approximation algorithms achieve nearly optimal results in significantly less time. The RTL algorithm consistently improves operational costs across various traffic demand scenarios compared to a baseline algorithm. Furthermore, our findings suggest an investment strategy for NIoT service providers to enhance system performance and reduce costs.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3824-3836"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891072","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

UAV Assisted BS Sleep Strategy for Green Communication 面向绿色通信的无人机辅助BS睡眠策略

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-29 DOI: 10.1109/TNSE.2025.3565316

Huan Li;Daosen Zhai;Ruonan Zhang;Lei Liu;Celimuge Wu;Shahid Mumtaz;Mohsen Guizani

{"title":"UAV Assisted BS Sleep Strategy for Green Communication","authors":"Huan Li;Daosen Zhai;Ruonan Zhang;Lei Liu;Celimuge Wu;Shahid Mumtaz;Mohsen Guizani","doi":"10.1109/TNSE.2025.3565316","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3565316","url":null,"abstract":"The evolving mobile communication technology is constantly striving to meet the growing demands for higher transmission rate, greater connection density, and lower end-to-end latency. However, the concomitant multi-fold increase in energy consumption leads to a severe loss of profit for operators and a great challenge for global climate change. To enable green communication, we propose a novel unmanned aerial vehicle (UAV) assisted ground base station (GBS) sleep network architecture, in which most of the communication components of the GBSs with low traffic are shut down, and meanwhile the UAVs are employed as aerial base stations (ABSs) to compensate for the service loss of the sleep GBSs. To further explore the strengths of the proposed architecture, we formulate a joint optimization problem of GBS sleep strategy, ABS trajectory, and ABS transmission power, with the goal to minimize the system energy consumption. For solving the formulated problem, we first relax the integer variables and design an iterative algorithm based on the block coordinate descent (BCD) and sequential convex approximation (SCA) techniques. Then, the iterative algorithm is embedded into the branch and bound (B&B) architecture to get the final mixed integer solution. Considering the high complexity of the B&B algorithm, we especially propose the external polygon contraction algorithm (EPCA) to drastically reduce the computation time for the delay sensitive service. Numerical simulation results demonstrate that the B&B based algorithm is superior to other comparison schemes and the EPCA significantly degrades the computation time with acceptable performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3770-3783"},"PeriodicalIF":7.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891062","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

Goal-Conditioned Resource Allocation With Hierarchical Offloading and Reliable Consensus for Blockchain-Based Industrial Digital Twins 基于区块链的工业数字孪生的分层卸载和可靠共识的目标条件资源分配

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-29 DOI: 10.1109/TNSE.2025.3565554

Kening Zhang;Carman K. M. Lee;Yung Po Tsang

{"title":"Goal-Conditioned Resource Allocation With Hierarchical Offloading and Reliable Consensus for Blockchain-Based Industrial Digital Twins","authors":"Kening Zhang;Carman K. M. Lee;Yung Po Tsang","doi":"10.1109/TNSE.2025.3565554","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3565554","url":null,"abstract":"In the current technological landscape, digital twins (DTs) are critical enablers for enhancing communication efficiency, data processing and on-line monitoring with virtual copies in industry network environments. However, heterogeneous devices and sensitive data breaches intensify challenges in security and management. Rapidly changing business requirements further exacerbate these issues, as traditional algorithms struggle to adapt to dynamic industrial demands. Simultaneously, overloaded edge servers, ultra-reliable low latency communications (URLLC), and limited resources make real-time decision-making even more difficult. Hence, we propose a hierarchical offloading and resource allocation framework for blockchain-based industrial D2D DT (OR-BIDT), which addresses these challenges by providing offloading and allocation strategies that protect data privacy and reliable communication. Then, we propose an R-DPoS consensus mechanism that optimizes node selection by introducing a voting mechanism with transmission reliability and computation frequency to improve the security of block verification. For problems requiring optimization over a goal space rather than the simple linear weighted sum in OR-BIDT, we design a goal-conditioned reinforcement learning (GCRL) approach with locality sensitive hashing-based experience replay (LSHER) to accomplish efficient experience returns. Simulations show that the critical and actor networks of our proposed algorithm converge 71.43% and 14.29% faster than the benchmark method, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3797-3811"},"PeriodicalIF":7.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891071","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

SEEIR: Secure and Efficient Encrypted Image Retrieval Based on Additive Secret Sharing 基于加性秘密共享的安全高效的加密图像检索

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-29 DOI: 10.1109/TNSE.2025.3565408

Yangyu Shen;Haijiang Wang;Jian Wan;Lei Zhang;Jie Huang;Zegang Pan

{"title":"SEEIR: Secure and Efficient Encrypted Image Retrieval Based on Additive Secret Sharing","authors":"Yangyu Shen;Haijiang Wang;Jian Wan;Lei Zhang;Jie Huang;Zegang Pan","doi":"10.1109/TNSE.2025.3565408","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3565408","url":null,"abstract":"Content-based image retrieval (CBIR) leverages convolutional neural networks (CNNS) (e.g., VGG-16) to achieve high accuracy by extracting image feature vectors. While existing schemes employ additive secret sharing (ASS) with a twin-cloud model to delegate tasks like secure feature extraction and encrypted retrieval to cloud servers, they suffer from critical limitations: (1) insecure index structures vulnerable to unauthorized queries and (2) inefficient twin-server communication protocols. To address these issues, we propose SEEIR, a secure and efficient encrypted image retrieval scheme based on ASS. First, SEEIR enhances retrieval security through secure KNN-ASS, a novel method that encrypts index shares across twin clouds to enforce access control. Only users with keys authorized by the data owner can generate valid query vectors, blocking adversarial attempts to compromise sensitive metadata. Second, SEEIR eliminates the twin-server communication overhead by securely merging encrypted index shares into a single cloud server, improving retrieval efficiency. Finally, both theoretical analysis and empirical experiments confirm the security and efficiency of the proposed scheme.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3784-3796"},"PeriodicalIF":7.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security Analysis and Defense of Multi-Encoding Mechanism Against Eavesdropping Attacks 多编码机制的安全分析与防御窃听攻击

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-29 DOI: 10.1109/TNSE.2025.3565302

Jie Wang;Wen Yang;Guanrong Chen;Jiayu Zhou;Wenjie Ding

引用次数: 0