IEEE Transactions on Services Computing最新文献_第10页

Leveraging Consortium Blockchain for Secure Cross-Domain Data Sharing in Supply Chain Networks 利用联盟区块链实现供应链网络中的安全跨域数据共享

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/TSC.2025.3544130

Runqun Xiong;Jing Cheng;Xirui Dong;Jiahang Pu;Feng Shan

{"title":"Leveraging Consortium Blockchain for Secure Cross-Domain Data Sharing in Supply Chain Networks","authors":"Runqun Xiong;Jing Cheng;Xirui Dong;Jiahang Pu;Feng Shan","doi":"10.1109/TSC.2025.3544130","DOIUrl":"10.1109/TSC.2025.3544130","url":null,"abstract":"Supply Chain Networks (SCNs) play a vital role in achieving strategic decision-making for production and distribution facilities, aiming to meet market demands and gain competitive advantages. With the application of new-generation information technology in the supply chain, enterprises within SCNs generate a substantial volume of relevant business data. Sharing this data among SCN enterprises can effectively reduce operating costs, optimize business processes, and enhance the overall efficiency of the supply chain. However, effective data sharing among SCN participants faces challenges, such as data leakage, data quality assurance, and fair data value allocation. To address these challenges, this paper proposes a secure cross-domain data sharing model in SCNs (named SCN-CDSM) based on consortium blockchain technology. The model introduces trust, enables cross-domain data exchange, and promotes cooperation among supply chain enterprises. To ensure privacy, group signatures and access control smart contracts are designed, along with an approach to reduce blockchain throughput limitations. Furthermore, a sharing incentive mechanism utilizing the Stackelberg game model based on data value is designed to foster fairness and collaboration. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed schemes, achieving both security and efficiency in data sharing within SCNs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"897-911"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data 一种准确表示动态QoS数据的自适应偏置扩展非负潜分解张量模型

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/TSC.2025.3544123

Xiuqin Xu;Mingwei Lin;Xin Luo;Zeshui Xu

{"title":"An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data","authors":"Xiuqin Xu;Mingwei Lin;Xin Luo;Zeshui Xu","doi":"10.1109/TSC.2025.3544123","DOIUrl":"10.1109/TSC.2025.3544123","url":null,"abstract":"Time-varying quality-of-service (QoS) data are usually utilized for Web service evaluation and selection. To accurately estimate the unknown information in time-varying QoS data, it is crucial to capture the temporal patterns hidden in the known data. The Non-negative Latent Factorization of Tensors (NLFT) model has performed well in describing the temporal patterns in time-varying QoS data. However, it assigns a single bias to each dimension of the target QoS tensor, making it suffer from estimation accuracy loss when describing the fluctuations of time-varying QoS data. To address this vital issue, this paper proposes an Adaptively Bias-extended NLFT (ABNT) model based on the fuzzy logic with two-fold ideas: a) extending the linear biases on each dimension of tensor for describing the complex fluctuations of QoS data precisely, b) building a fuzzy logic-incorporated particle swarm optimization algorithm to establish a self-adaptation mechanism for the count of extended linear biases and regularization coefficients. Detailed algorithms and analyses are provided for the proposed ABNT model. Empirical studies on two practical time-varying QoS datasets indicate that the estimation accuracy of the ABNT model outperforms that of state-of-the-art QoS data estimation models (with an average 23.94% improvement in MAE).","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"603-617"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462456","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

Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach 无人机- mec系统联合轨迹优化与资源分配：lyapunov辅助DRL方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/TSC.2025.3544124

Ying Chen;Yaozong Yang;Yuan Wu;Jiwei Huang;Lian Zhao

{"title":"Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach","authors":"Ying Chen;Yaozong Yang;Yuan Wu;Jiwei Huang;Lian Zhao","doi":"10.1109/TSC.2025.3544124","DOIUrl":"10.1109/TSC.2025.3544124","url":null,"abstract":"Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"854-867"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462246","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

Learning Schema Embeddings for Service Link Prediction: A Coupled Matrix-Tensor Factorization Approach 服务链路预测的学习模式嵌入：一种耦合矩阵张量分解方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-18 DOI: 10.1109/TSC.2025.3541552

Jing Yang;Xiaofen Wang;Laurence T. Yang;Yuan Gao;Shundong Yang;Xiaokang Wang

{"title":"Learning Schema Embeddings for Service Link Prediction: A Coupled Matrix-Tensor Factorization Approach","authors":"Jing Yang;Xiaofen Wang;Laurence T. Yang;Yuan Gao;Shundong Yang;Xiaokang Wang","doi":"10.1109/TSC.2025.3541552","DOIUrl":"10.1109/TSC.2025.3541552","url":null,"abstract":"Schema information is increasingly crucial to improve service discovery, recommendation, and composition, addressing link sparsity and lack of explainability inherent in methods relying solely on triples. However, existing approaches predominantly utilize schema information as a rigid filtering mechanism, equivalent to fixed conditions that lack the capability to adaptively adjust based on model learning. This paper introduces a novel learnable schema-aware knowledge embedding framework that enhances service link prediction by synergizing entity, relation, and type embeddings through a coupled matrix-tensor factorization model. To our knowledge, this is the first approach that couples entity and relation embeddings to enable adaptive learning of <bold>Schema <bold>Embeddings (<bold>SchemaE). Our framework is both expressive and easy to use, with the capability to generalize to existing bilinear models. Within this framework, we further propose the schema prompt method for embedding isolated nodes, which typically suffer from sparse relations or the absence of neighbors, leading to biased representation often overlooked in existing works. Despite embedding schema information, our model remains lightweight due to the introduction of a parameter-efficient strategy via type assists. We conduct extensive experiments on four public datasets, including comparisons with existing SOTA models, parameter analysis, performance validation on extended models, and visualization. The experimental results confirm the effectiveness and efficiency of the proposed model.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"883-896"},"PeriodicalIF":5.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443459","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

User Preference Oriented Service Caching and Task Offloading for UAV-Assisted MEC Networks 面向用户偏好的无人机辅助MEC网络服务缓存和任务卸载

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-13 DOI: 10.1109/TSC.2025.3536319

Ruiting Zhou;Yifeng Huang;Yufeng Wang;Lei Jiao;Haisheng Tan;Renli Zhang;Libing Wu

{"title":"User Preference Oriented Service Caching and Task Offloading for UAV-Assisted MEC Networks","authors":"Ruiting Zhou;Yifeng Huang;Yufeng Wang;Lei Jiao;Haisheng Tan;Renli Zhang;Libing Wu","doi":"10.1109/TSC.2025.3536319","DOIUrl":"10.1109/TSC.2025.3536319","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have emerged as a new and flexible paradigm to offer low-latency and diverse mobile edge computing (MEC) services for user equipment (UE). To minimize the service delay, caching is introduced in UAV-assisted MEC networks to bring service contents closer to UEs. However, UAV-assisted MEC is challenged by the heavy communication overhead introduced by service caching and UAV’s limited energy capacity. In this article, we propose an online algorithm, <italic>OOA, that jointly optimizes caching and offloading decisions for UAV-assisted MEC networks, to minimize the overall service delay. Specifically, to improve the caching effectiveness and reduce the caching overhead, <italic>OOA employs a greedy algorithm to dynamically make caching decisions based on UEs’ preferences on services and UAVs’ historical trajectories, with the goal of maximizing the probability of successful offloading. To realize the rational utilization of energy from a long-term perspective, <italic>OOA decomposes the online problem into a series of single-slot problems by scaling the UAV’s energy constraint into the objective, and iteratively optimizes UAV trajectory and task offloading at each time slot. Theoretical analysis proves that <italic>OOA converges to a suboptimal solution with polynomial time complexity. Extensive simulations based on real world data further show that <italic>OOA can reduce the service delay by up to 33% while satisfying the UAV’s energy constraint, compared to three state-of-the-art algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1097-1109"},"PeriodicalIF":5.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A QoS Prediction Framework via Utility Maximization and Region-Aware Matrix Factorization 基于效用最大化和区域感知矩阵分解的QoS预测框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-12 DOI: 10.1109/TSC.2025.3541554

Xia Chen;Yugen Du;Guoxing Tang;Fan Chen;Yingwei Luo;Hanting Wang

{"title":"A QoS Prediction Framework via Utility Maximization and Region-Aware Matrix Factorization","authors":"Xia Chen;Yugen Du;Guoxing Tang;Fan Chen;Yingwei Luo;Hanting Wang","doi":"10.1109/TSC.2025.3541554","DOIUrl":"10.1109/TSC.2025.3541554","url":null,"abstract":"With the surge of Web services, users are more concerned about Quality-of-Service (QoS) information when choosing Web services with similar functionalities. Today, effectively and accurately predicting QoS values is a tough challenge. Typically, traditional methods only use the QoS values provided by users to predict the missing QoS values, ignoring the arbitrariness of some users in providing observed QoS values and failing to consider the existence of anomalous QoS values with contingencies caused by some unstable Web services. Taking into account the above, this article proposes HyLoReF-us, a new framework for QoS prediction. HyLoReF-us uses the user reputation to measure the trustworthiness of users and the service reputation to measure the stability of web services. First, considering the utility generated by the invocation between users and Web services, HyLoReF-us employs a Logit model to calculate the user reputation and service reputation. Second, after combining the location information of users and services, as well as their reputations, HyLoReF-us obtains QoS predictions through an improved Matrix Factorization (MF) model. Finally, a series of experiments were conducted on the standard WS-DREAM dataset. Experimental results show that HyLoReF-us outperforms current state-of-the-art or baseline methods at Matrix Densities (MD) from 5% to 30%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"557-571"},"PeriodicalIF":5.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT 基于自适应异常感知的模拟物联网主动防御判定方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-07 DOI: 10.1109/TSC.2024.3436673

Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun

{"title":"An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT","authors":"Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun","doi":"10.1109/TSC.2024.3436673","DOIUrl":"https://doi.org/10.1109/TSC.2024.3436673","url":null,"abstract":"Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic IoT active defense architecture, improving system security and reliability despite prevailing security threats. In addition, an intelligent anomaly sensing algorithm is integrated into the adjudication module of the mimic IoT active defense architecture to support arbitration. An adaptive anomaly sensing model based on multi-feature selection is used to determine the anomaly score of the IoT device outputs, and this model fully considers the reliability of the adjudication data and improves the accuracy of the adjudication. Finally, we conduct a comparative analysis of the proposed adjudication algorithm against three others via a mimic power communication IoT system as an application scenario. The experimental results show that our algorithm can improve security and reduce the failure rate of the mimic IoT system.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"57-71"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361272","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

Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness 基于图卷积网络和多上下文感知的Next PoI推荐

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-07 DOI: 10.1109/TSC.2024.3463500

Wei Zhou;Cheng Fu;Chunyan Sang;Min Gao;Junhao Wen

{"title":"Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness","authors":"Wei Zhou;Cheng Fu;Chunyan Sang;Min Gao;Junhao Wen","doi":"10.1109/TSC.2024.3463500","DOIUrl":"https://doi.org/10.1109/TSC.2024.3463500","url":null,"abstract":"Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity problems in PoI recommendations can significantly impact the performance of the recommendation model. This paper introduces the Graph Convolutional Network and Multiple Context-Aware PoI Recommendation model (GMCA). First, we present a weighted graph convolutional network that aims to capture the optimal representations of users and PoIs within the user-PoI interaction graph. Second, we employ a fine-grained approach to analyze user check-in records and cluster them into multiple user activity centers. Furthermore, we incorporate time, location, and social context information into the matrix decomposition process. Third, User activity centers are constructed by clustering user check-in records, and the geographical influence of PoI location on user behavioral patterns is explored using probabilistic factor decomposition. The evaluation of the GMCA model on the Yelp and Gowalla datasets shows a significant improvement in Precision@10 indicators. Specifically, there is a 13.85% increase in Precision@10 on the Yelp dataset and a 9.01% increase on the Gowalla dataset. The effectiveness of the GMCA model has been confirmed through numerous experiments conducted on two public datasets.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"302-313"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361432","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

Behavior Tomographer: Identifying Hidden Cybercrimes by Behavior Interior Structure Modeling 行为层析：通过行为内部结构建模识别隐藏的网络犯罪

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-06 DOI: 10.1109/TSC.2025.3539194

Cheng Wang;Hangyu Zhu

{"title":"Behavior Tomographer: Identifying Hidden Cybercrimes by Behavior Interior Structure Modeling","authors":"Cheng Wang;Hangyu Zhu","doi":"10.1109/TSC.2025.3539194","DOIUrl":"10.1109/TSC.2025.3539194","url":null,"abstract":"Identifying hidden cybercrimes is a challenging task, as these behaviors are often carefully planned by criminals with counter-surveillance awareness. Existing solutions for cybercrime detection struggle to uncover enough clues to identify hidden criminal behaviors. Malicious behaviors are concealed beneath benign behaviors, and the boundaries between malicious and benign behaviors in the representation space are blurred to evade mainstream deep learning-based security authentication models. We introduce a <underline>behavior <underline>tomographer (BT) to reconstruct the behavior structure from three slices: agent, event, and attribute slices, enabling more granular detection of hidden cybercrimes. The core idea of BT is to reconstruct interior information about behavior structure from multiple slices, much like computed tomography in modern medicine enables the reconstruction of internal body. It enables the extraction of discriminative information from intricate interior associations between behavioral attributes rather than surface information meticulously crafted by criminals. Our experiments are conducted on two representative cybercrime datasets. Promising experimental results demonstrate that BT outperforms state-of-the-art models on key metrics, achieving around 0.99 AUC-ROC and approximately 0.9 AUC-PR. Moreover, BT notably excels at low false positive rates, showcasing its high effectiveness for real-world applications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"673-689"},"PeriodicalIF":5.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258432","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

DRMQ: Dynamic Resource Management for Enhanced QoS in Collaborative Edge-Edge Industrial Environments DRMQ：协同边缘工业环境中增强QoS的动态资源管理

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-06 DOI: 10.1109/TSC.2025.3539201

Fengyi Huang;Wenhua Wang;Qin Liu;Wentao Fan;Jianxiong Guo;Weijia Jia;Jiannong Cao;Tian Wang

{"title":"DRMQ: Dynamic Resource Management for Enhanced QoS in Collaborative Edge-Edge Industrial Environments","authors":"Fengyi Huang;Wenhua Wang;Qin Liu;Wentao Fan;Jianxiong Guo;Weijia Jia;Jiannong Cao;Tian Wang","doi":"10.1109/TSC.2025.3539201","DOIUrl":"10.1109/TSC.2025.3539201","url":null,"abstract":"In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industrial applications. Compared to general servers, edge computing devices based on Arm architecture exhibit lower latency and higher energy efficiency. This highlights the need for improved heterogeneous Collaborative Edge-Edge Industrial Environments (CEIE) and precise multi-user QoS metrics. Thus, we focus on dynamic resource management within the CEIE architecture to better satisfy diverse industrial applications, formulating a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem to minimize system costs. To reduce the computational complexity of solving the MINLP, we decompose the original problem into multi-user task offloading, Communication Resource Allocation (CmRA), and Computational Resource Allocation (CpRA) problems. These transformed problems are then tackled using DRMQ: an integrated learning optimization approach that combines model-free, priority experience replay-based Double Deep Q-Network (iDDQN) with model-based optimization, accelerating the Q-value function's convergence speed and reducing training time. Extensive simulations show that our proposed optimization scheme can reduce the average weighted system cost by at least 43.168% . Moreover, testbed experiments demonstrate that the proposed algorithm can reduce the average system cost by at least 42.650% in real-world applications, outperforming existing methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"743-757"},"PeriodicalIF":5.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258433","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