IEEE Transactions on Network and Service Management最新文献_第2页

Quantum-Enhanced Matching Mechanism for Secure and Efficient Power IoT Data Trading 安全高效电力物联网数据交易的量子增强匹配机制

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-24 DOI: 10.1109/TNSM.2026.3667701

Can Wang;Run-Hua Shi;Jiang-Yuan Lian;Pei-Xuan Wang;Ze-Hui Jiang

{"title":"Quantum-Enhanced Matching Mechanism for Secure and Efficient Power IoT Data Trading","authors":"Can Wang;Run-Hua Shi;Jiang-Yuan Lian;Pei-Xuan Wang;Ze-Hui Jiang","doi":"10.1109/TNSM.2026.3667701","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667701","url":null,"abstract":"The exponential growth of power IoT data has created immense potential for intelligent energy management, but it also presents critical challenges in achieving secure and efficient data trading. In particular, emerging data trading scenarios demand support for range nearest neighbor matching, which current schemes fail to address. This paper proposes a novel quantum trading matching scheme tailored for power data markets, which, for the first time, supports range nearest neighbor matching while balancing accuracy, efficiency, and privacy. To improve matching efficiency, we design a quantum private query (QPQ) mechanism based on a bidirectional sliding window (BSW), which replaces traditional linear search with dynamic range expansion. Furthermore, to ensure strong privacy and security in real-world scenarios, we develop a two-layer QPQ framework that performs data feature matching and identity retrieval separately, supported by a customized key distribution strategy. Our solution resists quantum attacks and significantly reduces computational overhead. At the same time, by utilizing non-ideal photon sources, it offers a practical and privacy-preserving solution for large-scale power data trading and matching.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2793-2806"},"PeriodicalIF":5.4,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362285","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

Anomaly Detection for IoT Global Connectivity 物联网全球连接异常检测

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-23 DOI: 10.1109/TNSM.2026.3666123

Jesus Omaña Iglesias;Carlos Segura Perales;Stefan Geißler;Diego Perino;Andra Lutu

{"title":"Anomaly Detection for IoT Global Connectivity","authors":"Jesus Omaña Iglesias;Carlos Segura Perales;Stefan Geißler;Diego Perino;Andra Lutu","doi":"10.1109/TNSM.2026.3666123","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3666123","url":null,"abstract":"Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it became increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a reactive approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR–an unsupervised anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic. We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2728-2740"},"PeriodicalIF":5.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362440","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

An Early Conflict Resolution Mechanism for Blockchain-Based Delay-Sensitive IoT Networks 基于区块链的延迟敏感物联网网络早期冲突解决机制

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-23 DOI: 10.1109/TNSM.2026.3667085

Aditya Pathak;Irfan Al-Anbagi;Howard J. Hamilton

{"title":"An Early Conflict Resolution Mechanism for Blockchain-Based Delay-Sensitive IoT Networks","authors":"Aditya Pathak;Irfan Al-Anbagi;Howard J. Hamilton","doi":"10.1109/TNSM.2026.3667085","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667085","url":null,"abstract":"Blockchain technology, particularly Hyperledger Fabric (HLF), has emerged as a promising solution to enhance security and privacy in various domains, including Internet of Things (IoT) networks. Conflicting transactions in a HLF-based IoT network occur when multiple transactions attempt to modify the same asset or data concurrently. Conflicting transactions can lead to data inconsistencies, because the network may be unable to determine the correct order or the most preferred valid transaction. Existing conflict resolution mechanisms in HLF-based IoT networks often introduce considerable transaction latency, detect and resolve conflicting transactions in the late stages of the transaction lifecycle (ordering and validation), or require significant changes to the underlying HLF blockchain platform. To overcome these limitations, we propose an Early Conflict Resolution (ECR) mechanism that detects and resolves conflicts during the endorsement stage. The ECR mechanism uses a local cache (Sync.Map) and a dependency graph to efficiently detect conflicts by analyzing the Read-Sets (RS) and Write-Sets (WS) of transactions. ECR resolves conflicts in the detected conflicting transactions through transaction reordering or sequential processing. It also executes non-conflicting transactions in parallel to speed their processing. Our results show that the ECR mechanism improves transaction latency and the success rate for varying conflict rates, block sizes, and IoT devices compared to existing mechanisms.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2741-2761"},"PeriodicalIF":5.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362368","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

Multimodal Learning-Based Relational Graph Neural Networks for Social Bot Detection 基于多模态学习的关系图神经网络用于社交机器人检测

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-23 DOI: 10.1109/TNSM.2026.3667016

Ke Wu;Yaguang Lin;Xiaoming Wang;Liang Wang

{"title":"Multimodal Learning-Based Relational Graph Neural Networks for Social Bot Detection","authors":"Ke Wu;Yaguang Lin;Xiaoming Wang;Liang Wang","doi":"10.1109/TNSM.2026.3667016","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667016","url":null,"abstract":"Social bots are virtual accounts controlled by automated programs that can disseminate harmful content on social media and even manipulate public opinion. Social bot detection aims to identify bot accounts, which is crucial for maintaining a healthy online ecosystem. However, advances in multimedia technology and smart device prevalence have diversified user-generated social media content, which now encompasses text, images, videos, and more. Effectively leverage multimodal content for robust social bot detection presents a significant research challenge. Furthermore, existing detection methods often overlook the latent social relationships, which we believe can significantly enhance bot detection. To address the issues, in this paper we propose a novel approach for social bot detection that comprehensively leverages users’ multimodal information. Specifically, we first develop an adaptive multimodal fusion mechanism capable of effectively integrating heterogeneous modal information under imbalanced data distributions to obtain more discriminative user representations. Second, we design a latent social relationship mining algorithm that reconstructs more complete social graphs to enhance the objectivity and completeness of user multimodal representations. Finally, on the basis of our proposed multimodal information fusion mechanism and latent social relationship mining algorithm, we design a new social bot detection model. We conduct extensive experiments on the TwiBot-20 dataset, demonstrating superior performance over baseline methods with significant improvements in both detection accuracy and F1-score. Comprehensive ablation studies and dimensionality-reduced visualizations of user representations further validate the critical role of multimodal information and the effectiveness of our proposed model.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2694-2711"},"PeriodicalIF":5.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299597","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

Privacy-Aware DRL for Differential Games-Assisted Malware Defense in Edge Intelligence-Enabled Social IoT 边缘智能社交物联网中差分游戏辅助恶意软件防御的隐私感知DRL

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-18 DOI: 10.1109/TNSM.2026.3666173

Shigen Shen;Jun Wu;Yizhou Shen;Xiaoping Wu;Jingnan Dong;Tian Wang;Ruidong Li

{"title":"Privacy-Aware DRL for Differential Games-Assisted Malware Defense in Edge Intelligence-Enabled Social IoT","authors":"Shigen Shen;Jun Wu;Yizhou Shen;Xiaoping Wu;Jingnan Dong;Tian Wang;Ruidong Li","doi":"10.1109/TNSM.2026.3666173","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3666173","url":null,"abstract":"The edge intelligence-enabled Social Internet of Things (SIoT) faces severe security threats from stealthy malware propagation, while existing defenses struggle to model complex behaviors or provide real-time and privacy-aware responses. Herein, we propose a comprehensive malware defense framework integrating a five-state propagation model, continuous-time differential games, and a privacy-aware reinforcement learning algorithm named PP-D3QN (Privacy-Preserving Dueling Double Deep Q Network). The malware propagation model includes susceptible, infectious, patched, quarantined, and removed states, accurately representing centralized and cooperative patching as well as quarantine detection mechanisms. Leveraging differential games, optimal defense strategies are theoretically derived by solving the Hamilton–Jacobi–Bellman equation, dynamically balancing infection risk, patching benefits, and quarantine costs. The PP-D3QN algorithm employs prioritized experience replay with strict control over private data sampling and Gaussian noise perturbation to ensure differential privacy, while learning effective defense strategies through practical interaction with dynamic edge intelligence-enabled SIoT systems. Extensive simulations demonstrate that the proposed method significantly improves malware suppression speed and SIoT nodes recovery rates, showcasing strong theoretical and practical value. This work offers a rigorous and applicable solution for dynamic malware defense under privacy-preserving constraints in edge intelligence-enabled SIoT systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2680-2693"},"PeriodicalIF":5.4,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299645","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

Dynamic Parallel Task Offloading and Sustainable On-Board Computing for Delay-Energy Optimization LEO Networks 时延能量优化LEO网络的动态并行任务卸载与可持续星载计算

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-17 DOI: 10.1109/TNSM.2026.3665512

Ahmad Y. Alhusenat;Lei Lei;Jinjin Tian;Lihong Zhu;Tong-Xing Zheng;Symeon Chatzinotas

{"title":"Dynamic Parallel Task Offloading and Sustainable On-Board Computing for Delay-Energy Optimization LEO Networks","authors":"Ahmad Y. Alhusenat;Lei Lei;Jinjin Tian;Lihong Zhu;Tong-Xing Zheng;Symeon Chatzinotas","doi":"10.1109/TNSM.2026.3665512","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3665512","url":null,"abstract":"Task offloading among low-earth orbit (LEO) satellites with on-board computing (OBC) is important for real-time applications. However, OBC is constrained by the battery capacity of LEO, which fluctuates with orbital dynamics and available solar power. This paper addresses the problem of energy sustainability and timeliness in LEO-OBC systems by proposing a sustainable OBC-LEO framework that combines parallel offloading strategies with dynamic energy management. This problem is formulated as a Markov decision process aiming to minimize the overall delay while satisfying the LEO satellite energy constraints and achieving a high task success rate. To balance immediate computational demands and long-term energy stability, a Lyapunov optimization-based dynamic parallel offloading (LODPO) algorithm is designed to make decisions dynamically within each time slot, integrated with subtask allocation based on a low-cost (SABLC) algorithm that dynamically adjusts task allocations. Finally, simulation results demonstrate that the LODPO framework achieves a significant reduction in execution delay, incurring only 34.0% of the delay cost of binary offloading. Most critically, it ensures exceptional reliability, with a task drop rate that is only 8.5% of that seen in binary offloading and 12.0% of that in the DQN-based approach. This ensures high responsiveness and dependability for mission-critical, delay-sensitive applications.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2636-2651"},"PeriodicalIF":5.4,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299543","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

Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning 基于深度强化学习的开放式RAN切片资源分配加速

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-17 DOI: 10.1109/TNSM.2026.3665553

Tuan-Vu Truong;Van-Dinh Nguyen;Quang-Trung Luu;Phi-Son Vo;Xuan-Phu Nguyen;Fatemeh Kavehmadavani;Symeon Chatzinotas

{"title":"Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning","authors":"Tuan-Vu Truong;Van-Dinh Nguyen;Quang-Trung Luu;Phi-Son Vo;Xuan-Phu Nguyen;Fatemeh Kavehmadavani;Symeon Chatzinotas","doi":"10.1109/TNSM.2026.3665553","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3665553","url":null,"abstract":"The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3055-3070"},"PeriodicalIF":5.4,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440541","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

Dynamic Migration in Digital Twin-Enabled Industrial Internet: A Stochastic Network Calculus Approach 数字双机工业互联网的动态迁移：随机网络演算方法

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-16 DOI: 10.1109/TNSM.2026.3665033

Rui Huang;Qingling Li;Liangru Xie;Fei Shang

{"title":"Dynamic Migration in Digital Twin-Enabled Industrial Internet: A Stochastic Network Calculus Approach","authors":"Rui Huang;Qingling Li;Liangru Xie;Fei Shang","doi":"10.1109/TNSM.2026.3665033","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3665033","url":null,"abstract":"Digital Twin (DT) technology serves as a critical enabler in Cyber-Physical-Social Systems (CPSS), especially within Industry 5.0’s human-centric manufacturing paradigm. However, the computational intensity of processing real-time data in DT systems often leads to resource saturation and performance degradation at computation nodes. Dynamic service migration of digital twins by offloading computation-intensive tasks to resource-rich nodes to offer a promising solution, yet introduces challenges in preserving service quality during migration. Key issues include high delay, data inconsistency, service interruptions, and limited bandwidth compromising system stability. To address these challenges, this paper proposes a dynamic migration scheduling strategy for digital twins based on a Lyapunov optimization framework. Our approach integrates Stochastic Network Calculus (SNC) for Quality of Service (QoS) quantification and a persistent queue mechanism for reliability assurance. Theoretical analysis and extensive simulations demonstrate that the proposed algorithm achieves near-optimal performance with provable bounds, effectively minimizing migration-induced delay while maintaining service reliability. The results confirm that our framework consistently outperforms existing solutions in managing service migration within industrial internet systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2778-2792"},"PeriodicalIF":5.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362397","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

HMap: Efficient Internet-Wide IPv6 Scanning With Dynamic Search HMap：高效的全互联网IPv6扫描与动态搜索

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-16 DOI: 10.1109/TNSM.2026.3664795

Bingnan Hou;Zhenzhong Yang;Xianzheng Meng;Xiaoyi Wang;Yifan Yang;Ling Hu;Xionglve Li;Zhiping Cai

{"title":"HMap: Efficient Internet-Wide IPv6 Scanning With Dynamic Search","authors":"Bingnan Hou;Zhenzhong Yang;Xianzheng Meng;Xiaoyi Wang;Yifan Yang;Ling Hu;Xionglve Li;Zhiping Cai","doi":"10.1109/TNSM.2026.3664795","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664795","url":null,"abstract":"Internet-wide scanning is integral to network measurement and security analysis, but the expansive address space of IPv6 limits existing approaches in achieving efficient global-scale scans. This study introduces HMap, an innovative IPv6 scanner that markedly improves scan efficiency and coverage through the implementation of a dynamic search (DS) technique, relying solely on IPv6 routeable BGP prefixes. DS employs a dynamic feedback-driven probing strategy that uses information from previous replies to prioritize more promising address regions in subsequent scans. In Internet-wide scans over IPv6, encompassing both ping-like and traceroute-like scans with DS, HMap has demonstrated its capability to discover 2.29 million non-alias active target addresses, 0.13 million peripheries/middleboxes, and 1.61 million router interfaces, using only million-scale probes. This represents a noteworthy improvement of 1.91 times, 1.63 times, and 12.38 times, respectively, compared to current state-of-the-art alternatives. Additionally, by utilizing an efficient target generation algorithm (TGA) that more effectively leverages seed addresses, HMap expands the non-alias active address count to 44.05 million. This coverage spans 18.97 thousand ASes with a one-hour scan at a limited probing speed of 100 Kpps. The volume of active IPv6 addresses is 4.88 times larger than the currently disclosed largest IPv6 hitlists, providing a more diverse set of IPv6 networks. Unlike prior IPv6 scan studies that preclude their use for Internet-scale security analysis, we also conduct the Internet-wide security scans of IPv6 networks, focusing on the exposed internal IPv6 devices and security-sensitive services in IPv6 routers.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2666-2679"},"PeriodicalIF":5.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299629","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

Minimizing the Cost of UAV-Assisted Marine Mobile Edge Computing System Based on Deep Reinforcement Learning 基于深度强化学习的无人机辅助海上移动边缘计算系统成本最小化

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-16 DOI: 10.1109/TNSM.2026.3664895

Siyang Xu;Ze Fan;Zijian Zhou;Qiuyu Lu;Biao Zhang;Yu Wang;Xin Song

{"title":"Minimizing the Cost of UAV-Assisted Marine Mobile Edge Computing System Based on Deep Reinforcement Learning","authors":"Siyang Xu;Ze Fan;Zijian Zhou;Qiuyu Lu;Biao Zhang;Yu Wang;Xin Song","doi":"10.1109/TNSM.2026.3664895","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664895","url":null,"abstract":"To enable compute-intensive and delay-sensitive maritime services, unmanned surface vessels (USVs) can offload tasks to mobile edge computing (MEC) servers mounted on unmanned aerial vehicles (UAVs). However, jointly minimizing energy consumption and latency is challenging due to the strong coupling between communication, computation, and mobility under stringent quality-of-service (QoS) requirements. To capture this trade-off, we formulate a weighted energy–delay minimization problem that jointly optimizes one-to-one UAV–USV scheduling, task partitioning, and UAV trajectory. The resulting problem is particularly difficult due to a hybrid discrete–continuous decision space and strong temporal coupling under stringent feasibility constraints. To address this mixed-integer nonconvex optimization problem, we reformulate it as a Markov decision process (MDP) and develop a constraint-aware OU–TD3 algorithm that integrates differentiable scheduling relaxation, feasibility-aware action mapping, and adaptive OU–Gaussian mixed exploration for stable learning in high-dimensional continuous control. We further extend the formulation and solution to a cooperative multi-UAV MEC setting with signal-to-interference-plus-noise ratio (SINR)-coupled interference and coordination constraints. Extensive simulations with statistical evaluation demonstrate stable convergence and up to 54.2% cost reduction over baseline schemes, while maintaining robustness under realistic maritime disturbances.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2608-2623"},"PeriodicalIF":5.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299601","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