IEEE Transactions on Network Science and Engineering最新文献_第6页

Joint Localization and Clock Synchronization in Cuboid Bounded Diffusive Channel With Absorbing and Reflecting Boundaries 有吸收和反射边界的立方体有界扩散通道中的联合定位和时钟同步

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-27 DOI: 10.1109/TNSE.2024.3450628

Ajit Kumar;Sudhir Kumar

{"title":"Joint Localization and Clock Synchronization in Cuboid Bounded Diffusive Channel With Absorbing and Reflecting Boundaries","authors":"Ajit Kumar;Sudhir Kumar","doi":"10.1109/TNSE.2024.3450628","DOIUrl":"10.1109/TNSE.2024.3450628","url":null,"abstract":"This paper proposes a joint localization and synchronization method in the presence of a 3-D (cuboidal-bounded) channel. Many biologically relevant structures, such as epithelium cell membranes, tissues, and blood vessel networks (particularly capillaries), can be effectively modeled as 3-D systems. Localization and synchronization among nanomachines play an important role in the optimal transmission rate, information exchange, and collaboration among nanomachines. Clock synchronization without localization or localization without clock synchronization affects the accuracy of the system. However, the existing methods consider that nanomachines are already synchronized for localization and vice-versa. Hence, the proposed method considers a combined model for location parameters, clock offset, and clock skew. Unlike the existing method, we consider this combined model in bounded environments, which are relevant for long-range molecular communication where released molecules need to be confined within a certain range to optimize power efficiency. However, deriving an analytical channel characterization for a constrained domain is challenging. We provide an analytical equation for the probability distribution function of the propagation delay of the molecules, taking into account the presence of both single and multiple absorbing walls.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6511-6521"},"PeriodicalIF":6.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191993","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

FedHelo: Hierarchical Federated Learning With Loss-Based-Heterogeneity in Wireless Networks FedHelo：无线网络中基于损失的异质性分层联合学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-26 DOI: 10.1109/TNSE.2024.3447904

Yuchuan Ye;Youjia Chen;Junnan Yang;Ming Ding;Peng Cheng;Haifeng Zheng

{"title":"FedHelo: Hierarchical Federated Learning With Loss-Based-Heterogeneity in Wireless Networks","authors":"Yuchuan Ye;Youjia Chen;Junnan Yang;Ming Ding;Peng Cheng;Haifeng Zheng","doi":"10.1109/TNSE.2024.3447904","DOIUrl":"10.1109/TNSE.2024.3447904","url":null,"abstract":"Hierarchical federated learning (HFL) in wireless networks significantly saves communication resources due to edge aggregation conducted in edge mobile computing (MEC) servers. Taking into account the spatially correlated characteristics of data in wireless networks, in this paper, we analyze the performance of HFL with hybrid data distributions, i.e. intra-MEC independent and identically distributed (IID) and inter-MEC non-IID data samples. We derive the upper bound of the difference between the achieved loss and the minimum one, which reveals the impacts of data heterogeneity and global aggregation frequency on the performance of HFL. On this basis, we propose an algorithm named FedHelo which optimizes the aggregation weights and edge/global aggregation frequencies under the constraints of training delay and clients' energy consumption. Our experiments \u0000<italic>i)</i>\u0000 verify the obtained theoretical results; \u0000<italic>ii)</i>\u0000 demonstrate the performance improvement achieved by FedHelo with the optimal aggregation weights and training/aggregation frequencies, especially in the scenario with high data heterogeneity; and \u0000<italic>iii)</i>\u0000 show the preference for edge aggregation in the scenario with a tight delay or client's energy constraint.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6066-6079"},"PeriodicalIF":6.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191992","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

Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing 基于区块链的移动人群感知任务分配混合可靠用户选择方案

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-26 DOI: 10.1109/TNSE.2024.3449146

Shiwen Zhang;Zhixue Li;Wei Liang;Kuan-Ching Li;Zakirul Alam Bhuiyan

{"title":"Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing","authors":"Shiwen Zhang;Zhixue Li;Wei Liang;Kuan-Ching Li;Zakirul Alam Bhuiyan","doi":"10.1109/TNSE.2024.3449146","DOIUrl":"10.1109/TNSE.2024.3449146","url":null,"abstract":"Mobile Crowd Sensing (MCS) has emerged as a new sensing paradigm due to its cost efficiency, mobility, and expandability. However, user selection for task allocation is a significant challenge in MCS. Most previous studies concentrate on two selection modes, opportunistic and participatory selection. Recent research has proposed a hybrid user selection mode that combines both advantages. However, existing hybrid user selection systems all rely on a centralized architecture, which is vulnerable to malicious attacks, and they do not consider the reliability of users and data availability. Moreover, they cannot ensure the individual rationality of users. To overcome these shortcomings, we propose a blockchain-based hybrid reliable user selection scheme for task allocation in MCS. Specifically, we replace the traditional central server with the blockchain and handle various sensing task operations using smart contracts on the blockchain to ensure system reliability and security. In addition, we design a user reputation calculation algorithm based on semi-Markov and a sensing data anomaly detection algorithm based on Long Short-Term Memory (LSTM) to ensure user reliability and data availability, and also a novel hybrid user selection algorithm, especially in the participatory user selection stage, where we use a user selection algorithm based on reverse auction to ensure the individual rationality of each user. Experimental results demonstrate the effectiveness of the proposed scheme through simulation experiments on GeoLife and sound-sensing public datasets.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6494-6510"},"PeriodicalIF":6.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191994","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

Category-Guided Graph Convolution Network for Semantic Segmentation 用于语义分割的类别引导图卷积网络

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-23 DOI: 10.1109/TNSE.2024.3448609

Zeyuan Xu;Zhe Yang;Danwei Wang;Zhe Wu

{"title":"Category-Guided Graph Convolution Network for Semantic Segmentation","authors":"Zeyuan Xu;Zhe Yang;Danwei Wang;Zhe Wu","doi":"10.1109/TNSE.2024.3448609","DOIUrl":"10.1109/TNSE.2024.3448609","url":null,"abstract":"Contextual information has been widely used to improve results of semantic segmentation. However, most approaches investigate contextual dependencies through self-attention and lack guidance on which pixels should have strong (or weak) relationships. In this paper, a category-guided graph convolution network (CGGCN) is proposed to reveal the relationships among pixels. First, we train a coarse segmentation map under the supervision of the ground truth and use it to construct an adjacency matrix among pixels. It turns out that the pixels belonging to the same category have strong connections, and those belonging to different categories have weak connections. Second, a GCN is exploited to enhance the representation of pixels by aggregating contextual information among pixels. The feature of each pixel is represented by node, and the relationship among pixels is denoted by edge. Subsequently, we design four different kinds of network structures by leveraging the CGGCN module and determine the most accurate segmentation result by comparing them. Finally, we reimplement the CGGCN module to refine the final prediction from coarse to fine. The results of extensive evaluations demonstrate that the proposed approach is superior to the existing semantic segmentation approaches and has better convergence.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6080-6089"},"PeriodicalIF":6.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191995","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

Multi-Client Verifiable Encrypted Keyword Search Scheme With Authorization Over Outsourced Encrypted Data 通过外包加密数据授权的多客户端可验证加密关键字搜索方案

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-21 DOI: 10.1109/TNSE.2024.3445343

Xu Yang;Qiuhao Wang;Saiyu Qi;Ke Li;Jianfeng Wang;Wenjia Zhao;Yong Qi

{"title":"Multi-Client Verifiable Encrypted Keyword Search Scheme With Authorization Over Outsourced Encrypted Data","authors":"Xu Yang;Qiuhao Wang;Saiyu Qi;Ke Li;Jianfeng Wang;Wenjia Zhao;Yong Qi","doi":"10.1109/TNSE.2024.3445343","DOIUrl":"10.1109/TNSE.2024.3445343","url":null,"abstract":"Data outsourcing is a key service of cloud computing. While data encryption ensures confidentiality, it limits the ability to search encrypted data. Recently, ciphertext-policy attribute-based keyword search (CP-ABKS) schemes, which combine symmetric searchable encryption (SSE) and ciphertext-policy attribute-based encryption (CP-ABE), have gained attention. However, most CP-ABKS schemes depend on an independent key management server (KMS) for key distribution, risking key leakage if the KMS is compromised. Additionally, these schemes lack secure update operations and efficient search result verification. To address these issues, we propose VKSA, a verifiable encrypted keyword search scheme with authorization for cloud-based multi-client environments. VKSA features a novel policy-hidden index for proxy-free authorized searches, a state-based secure update strategy for forward and backward security, and a delegated search result verification mechanism to ensure efficient and privacy-preserving verification. We further optimize VKSA for improved computational and enclave-storage efficiency. Security analysis and experiments confirm the security and efficiency of our schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6356-6371"},"PeriodicalIF":6.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191997","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

Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios 用于空中物联网场景中联合服务安置、网络选择和计算卸载的多时间尺度马尔可夫决策过程

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-21 DOI: 10.1109/TNSE.2024.3445890

Swapnil Sadashiv Shinde;Daniele Tarchi

{"title":"Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios","authors":"Swapnil Sadashiv Shinde;Daniele Tarchi","doi":"10.1109/TNSE.2024.3445890","DOIUrl":"10.1109/TNSE.2024.3445890","url":null,"abstract":"Vehicular Edge Computing (VEC) is considered a major enabler for multi-service vehicular 6G scenarios. However, limited computation, communication, and storage resources of terrestrial edge servers are becoming a bottleneck and hindering the performance of VEC-enabled Vehicular Networks (VNs). Aerial platforms are considered a viable solution allowing for extended coverage and expanding available resources. However, in such a dynamic scenario, it is important to perform a proper service placement based on the users' demands. Furthermore, with limited computing and communication resources, proper user-server assignments and offloading strategies need to be adopted. Considering their different time scales, a multi-time-scale optimization process is proposed here to address the joint service placement, network selection, and computation offloading problem effectively. With this scope in mind, we propose a multi-time-scale Markov Decision Process (MDP) based Reinforcement Learning (RL) to solve this problem and improve the latency and energy performance of VEC-enabled VNs. Given the complex nature of the joint optimization process, an advanced deep Q-learning method is considered. Comparison with various benchmark methods shows an overall improvement in latency and energy performance in different VN scenarios.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5364-5379"},"PeriodicalIF":6.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191996","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

UAV-Assisted Heterogeneous Multi-Server Computation Offloading With Enhanced Deep Reinforcement Learning in Vehicular Networks 无人机辅助异构多服务器计算卸载与增强型深度强化学习在车载网络中的应用

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-21 DOI: 10.1109/TNSE.2024.3446667

Xiaoqin Song;Wenjing Zhang;Lei Lei;Xinting Zhang;Lijuan Zhang

{"title":"UAV-Assisted Heterogeneous Multi-Server Computation Offloading With Enhanced Deep Reinforcement Learning in Vehicular Networks","authors":"Xiaoqin Song;Wenjing Zhang;Lei Lei;Xinting Zhang;Lijuan Zhang","doi":"10.1109/TNSE.2024.3446667","DOIUrl":"10.1109/TNSE.2024.3446667","url":null,"abstract":"With the development of intelligent transportation systems (ITS), computation-intensive and latency-sensitive applications are flourishing, posing significant challenges to resource-constrained task vehicles (TVEs). Multi-access edge computing (MEC) is recognized as a paradigm that addresses these issues by deploying hybrid servers at the edge and seamlessly integrating computing capabilities. Additionally, flexible unmanned aerial vehicles (UAVs) serve as relays to overcome the problem of non-line-of-sight (NLoS) propagation in vehicle-to-vehicle (V2V) communications. In this paper, we propose a UAV-assisted heterogeneous multi-server computation offloading (HMSCO) scheme. Specifically, our optimization objective to minimize the cost, measured by a weighted sum of delay and energy consumption, under the constraints of reliability requirements, tolerable delay, and computing resource limits, among others. Since the problem is non-convex, it is further decomposed into two sub-problems. First, a game-based binary offloading decision (BOD) is employed to determine whether to offload based on the parameters of computing tasks and networks. Then, a multi-agent enhanced dueling double deep Q-network (ED3QN) with centralized training and distributed execution is introduced to optimize server offloading decision and resource allocation. Simulation results demonstrate the good convergence and robustness of the proposed algorithm in a highly dynamic vehicular environment.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5323-5335"},"PeriodicalIF":6.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191796","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

Coarse-to-Fine Robust Heterogeneous Network Representation Learning Without Metapath 无需元路径的粗到细鲁棒性异构网络表征学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-21 DOI: 10.1109/TNSE.2024.3445724

Lei Chen;Haomiao Guo;Yong Lei;Yuan Li;Zhaohua Liu

{"title":"Coarse-to-Fine Robust Heterogeneous Network Representation Learning Without Metapath","authors":"Lei Chen;Haomiao Guo;Yong Lei;Yuan Li;Zhaohua Liu","doi":"10.1109/TNSE.2024.3445724","DOIUrl":"10.1109/TNSE.2024.3445724","url":null,"abstract":"Influenced by the heterogeneity, representation learning while preserving the structural and semantic information is more challenging for heterogeneous networks (HNs) than for homogeneous networks. Most of the existing heterogeneous representation models are depending on expensive and sensitive external metapaths to help learn structural and semantic information, and they are rarely considering network noise. In this case, a coarse-to-fine robust heterogeneous network representation learning model is proposed without metapath supervision, called CFRHNE. Inspired by the “divide and conquer” idea, the CFRHNE model divides the representation learning process into a coarse embedding stage of learning structural features and a fine embedding stage of learning semantic features. In the coarse embedding stage, a novel type-level homogeneous representation strategy is designed to learn the coarse representation vectors, by converting the heterogeneous structural feature learning of an HN into multiple homogeneous structural feature learning based on node types. In the fine embedding stage, a novel relation-level heterogeneous representation strategy is designed to further learn fine and robust representation vectors, by using the adversarial learning of multiple relations to add the semantic features to coarse representations. Extensive experiments on multiple datasets and tasks demonstrate the effectiveness of our CFRHNE model.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5773-5789"},"PeriodicalIF":6.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191794","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

EvoFuzzer: An Evolutionary Fuzzer for Detecting Reentrancy Vulnerability in Smart Contracts EvoFuzzer：用于检测智能合约中重入漏洞的进化模糊器

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-21 DOI: 10.1109/TNSE.2024.3447025

Bixin Li;Zhenyu Pan;Tianyuan Hu

{"title":"EvoFuzzer: An Evolutionary Fuzzer for Detecting Reentrancy Vulnerability in Smart Contracts","authors":"Bixin Li;Zhenyu Pan;Tianyuan Hu","doi":"10.1109/TNSE.2024.3447025","DOIUrl":"10.1109/TNSE.2024.3447025","url":null,"abstract":"Reentrancy vulnerability is one of the most serious security issues in smart contracts, resulting in millions of dollars in economic losses and posing a threat to the trust of the blockchain ecosystem. Therefore, researchers are paying more attention to this problem and have proposed various methods to detect and eliminate potential reentrancy vulnerabilities before contract deployment. Compared to symbolic execution and pattern-matching methods, fuzz testing method can achieve higher accuracy and are better suitable for detecting cross-contract vulnerabilities. However, existing fuzz testing tools often spend a long time exploring states with little pruning, and most of them adopt the reentrancy vulnerability oracle used by static analysis tools, which ignores whether the vulnerability can be exploited to compromise the access control, mutex, or time locks. To address these issues, we propose EvoFuzzer, an evolutionary fuzzer that focuses on the detection of reentrancy vulnerabilities. EvoFuzzer first leverages static analysis to exclude branches that have no impact on state transitions, then continuously optimizes test case generation using a genetic algorithm that considers both function sequence and parameter assignment, and Meanwhile, EvoFuzzer confirms whether reentrancy vulnerabilities can be exploited by simulating attacks. Our experiments have performed on 198 annotated contracts and 47 honeypot contracts, and experimental results show that EvoFuzzer can detect 91.7% of reentrancy vulnerabilities with no false positives, achieve the highest F1 score with 95.7%, which is 5.9% higher than the next best approach (Confuzzius), and we also find that it reduces more than 10% of branches when EvoFuzzer adopts a pruning strategy.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5790-5802"},"PeriodicalIF":6.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191795","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

Optimal Fleet Size for Cross-Route Dispatching in Electrified Bus Networks 电气化公交网络中跨线路调度的最优车队规模

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-20 DOI: 10.1109/TNSE.2024.3445915

Kianoosh Keshavarzian;Ali MoradiAmani;Mahdi Jalili

引用次数: 0