Ad Hoc Networks最新文献_第3页

Intelligent clustering and routing protocol for wireless sensor networks using quantum inspired Harris Hawk optimizer and deep reinforcement learning 使用量子启发的哈里斯鹰优化器和深度强化学习的无线传感器网络的智能聚类和路由协议

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-06-02 DOI: 10.1016/j.adhoc.2025.103914

Chuhang Wang , Huangshui Hu , Xinji Fan

{"title":"Intelligent clustering and routing protocol for wireless sensor networks using quantum inspired Harris Hawk optimizer and deep reinforcement learning","authors":"Chuhang Wang , Huangshui Hu , Xinji Fan","doi":"10.1016/j.adhoc.2025.103914","DOIUrl":"10.1016/j.adhoc.2025.103914","url":null,"abstract":"<div><div>Dynamic changes in wireless sensor networks (WSNs) present significant challenges to clustering and routing protocols, particularly impacting energy efficiency and network lifetime. Existing protocols often fail to address the trade-off between energy conservation and optimal cluster based routing, especially in highly dynamic environments. This paper proposes an Intelligent Clustering and Routing protocol for WSNs, called ICR-HHODRL, which innovatively integrates the Quantum-inspired Harris Hawk Optimizer (QHHO) for clustering and Deep Reinforcement Learning (DRL) for routing, aiming to improve energy efficiency, improve network throughput, and maximize network lifetime. The protocol minimizes message overhead by dynamically selecting optimal cluster head (CH) and forming clusters using QHHO with a new fitness function that considers node’s residual energy, average distance to neighbor nodes, and distance to the base station (BS), ensuring a balanced distribution of energy and CHs. Furthermore, ICR-HHODRL leverages a Dueling Double Deep Q-network (D3QN) with priority experience replay (D3QN-PER) for adaptive learning of optimal routing policies, addressing dynamic network conditions and enhancing load balancing. Experiment results show that the proposed ICR-HHODRL protocol outperforms several state-of-the-art clustering and routing protocols. Specifically, network lifetime is improved by 14.85%, 18.46%, 15.17%, 17.69%, and 14.77, network throughput is increased by 5.7%, 7.29%, 5.69%, 10.02%, and 7.21%, and network energy consumption is reduced by 16.96%, 20.03%, 15.51%, 8.61%, and 18.8%, compared to WOAD3QN-RP, MRP-ICHI, QoSCRSI, HHO-UCRA, and WOAC-HHOR, respectively. These findings highlight the protocol’s potential to significantly advance the state of the art in dynamic WSNs and offer promising solutions for low-power, resource-constrained networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103914"},"PeriodicalIF":4.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WISDOM: A framework for scaling on-device Wi-Fi sensing solutions WISDOM：用于扩展设备上Wi-Fi传感解决方案的框架

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-06-02 DOI: 10.1016/j.adhoc.2025.103915

Manoj Kumar Lenka, Ayon Chakraborty

{"title":"WISDOM: A framework for scaling on-device Wi-Fi sensing solutions","authors":"Manoj Kumar Lenka, Ayon Chakraborty","doi":"10.1016/j.adhoc.2025.103915","DOIUrl":"10.1016/j.adhoc.2025.103915","url":null,"abstract":"<div><div>Recent innovations in Wi-Fi sensing capitalizes on a host of powerful deep neural network architectures that make inferences based on minute spatio-temporal dynamics in the wireless channel. Many of such inference techniques being resource intensive, conventional wisdom recommends offloading them to the network Edge for further processing. In this paper, we argue that edge based sensing is often not a viable option for many applications (due to cost, bandwidth, latency etc.). Rather, we explore the paradigm of on-device Wi-Fi sensing where inference is carried out locally on resource constrained IoT platforms. We present extensive benchmark results characterizing the resource consumption (memory, energy) and the performance (accuracy, inference rate) of some typical sensing tasks. We propose <span>WISDOM</span>, a framework that, depending on capabilities of the hardware platform and application’s requirements, can compress the inference model. Such context aware compression aims to improve the overall utility of the system — maximal inference performance at minimal resource costs. We demonstrate that models obtained using the <span>WISDOM</span> framework achieve higher utility compared to baseline models that are just quantized for 83% of the cases. While for non-compressed models it has higher utility 99% of the time.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103915"},"PeriodicalIF":4.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT-AMLHP: Aligned multimodal learning of header-payload representations for resource-efficient malicious IoT traffic classification IoT- amlhp：针对资源高效恶意物联网流量分类的报头-有效负载表示的对齐多模态学习

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-06-01 DOI: 10.1016/j.adhoc.2025.103916

Fengyuan Nie , Guangjie Liu , Weiwei Liu , Jianan Huang , Bo Gao

{"title":"IoT-AMLHP: Aligned multimodal learning of header-payload representations for resource-efficient malicious IoT traffic classification","authors":"Fengyuan Nie , Guangjie Liu , Weiwei Liu , Jianan Huang , Bo Gao","doi":"10.1016/j.adhoc.2025.103916","DOIUrl":"10.1016/j.adhoc.2025.103916","url":null,"abstract":"<div><div>Traffic classification is crucial for securing Internet of Things (IoT) networks. Deep learning-based methods can autonomously extract latent patterns from massive network traffic, demonstrating significant potential for IoT traffic classification tasks. However, the limited computational and spatial resources of IoT devices pose challenges for deploying more complex deep learning models. Existing methods rely heavily on either flow-level features or raw packet byte features. Flow-level features often require inspecting entire or most of the traffic flow, leading to excessive resource consumption, while raw packet byte features fail to distinguish between headers and payloads, overlooking semantic differences and introducing noise from feature misalignment. Therefore, this paper proposes IoT-AMLHP, an aligned multimodal learning framework for resource-efficient malicious IoT traffic classification. Firstly, the framework constructs a packet-wise header-payload representation by parsing packet headers and payload bytes, resulting in an aligned and standardized multimodal traffic representation that enhances the characterization of heterogeneous IoT traffic. Subsequently, the traffic representation is fed into a resource-efficient neural network comprising a multimodal feature extraction module and a multimodal fusion module. The extraction module employs efficient depthwise separable convolutions to capture multi-scale features from different modalities while maintaining a lightweight architecture. The fusion module adaptively captures complementary features from different modalities and effectively fuses multimodal features. Extensive experiments on three public IoT traffic datasets demonstrate that the proposed IoT-AMLHP outperforms state-of-the-art methods in classification accuracy while significantly reducing computational and spatial resource overhead, making it highly suitable for deployment in resource-constrained IoT environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103916"},"PeriodicalIF":4.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory prediction training scheme in vehicular ad-hoc networks based on federated learning 基于联邦学习的车载自组织网络轨迹预测训练方案

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-31 DOI: 10.1016/j.adhoc.2025.103917

Jianhang Liu , Lele Yang , Neeraj Kumar , Abdullah Mohammed Almuhaideb , Konstantin Igorevich Kostromitin , Peiying Zhang

{"title":"Trajectory prediction training scheme in vehicular ad-hoc networks based on federated learning","authors":"Jianhang Liu , Lele Yang , Neeraj Kumar , Abdullah Mohammed Almuhaideb , Konstantin Igorevich Kostromitin , Peiying Zhang","doi":"10.1016/j.adhoc.2025.103917","DOIUrl":"10.1016/j.adhoc.2025.103917","url":null,"abstract":"<div><div>Vehicle trajectory prediction (TP) plays a crucial role in autonomous driving systems and is the core elements to improve traffic conditions and reduce the risk of accidents. However, establishing accurate TP models in real-time still presents numerous challenges. Firstly, relying on a central server for real-time model updates not only poses the risk of privacy leakage, but also increases the resource load with frequent data interactions. In addition, considering the changes in driving habits and traffic environment during vehicle traveling, the use of static TP models can lead to underfitting. Therefore, this paper proposes a TP Training Scheme in Vehicular Ad-hoc Networks Based on Federated Learning (FL-VANETs). In FL-VANETs, a Vehicle Relevance-Oriented Collaborative Vehicle Node Selection Algorithm (VR-CVNS) is designed to ensure that the reasonable construction of a decentralized Ad-hoc networks and enable serverless computing, thereby optimizing computational and communication efficiency. Additionally, through the FL framework, vehicle computing tasks are categorized, ensuring privacy security in the VANETs and enabling dynamic training of the TP model during vehicle movement, thereby improving the model’s predictive accuracy. The effectiveness and improvement of the method are verified through experiments and simulations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103917"},"PeriodicalIF":4.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SQBRP-SDFANET: A scalable Q-learning-based routing protocol for SD-FANETs SQBRP-SDFANET：一个可扩展的基于q学习的sd - fanet路由协议

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-31 DOI: 10.1016/j.adhoc.2025.103913

Nabila Bouziane , Zouina Doukha , Faudel Kimri , Moundher Djouama

{"title":"SQBRP-SDFANET: A scalable Q-learning-based routing protocol for SD-FANETs","authors":"Nabila Bouziane , Zouina Doukha , Faudel Kimri , Moundher Djouama","doi":"10.1016/j.adhoc.2025.103913","DOIUrl":"10.1016/j.adhoc.2025.103913","url":null,"abstract":"<div><div>In today’s fast-evolving world of wireless networking, creating efficient and reliable communication systems is essential. In this context, we introduce a new Q-learning-based routing protocol designed specifically for flying ad hoc networks (FANETs) that enhances path selection and network performance. Our protocol tackles the unique challenges of FANETs, such as dealing with rapidly changing topologies and managing UAV resources. We achieve this objective through various techniques, including partitioning the area of interest into hexagonal cells, reducing the exploration space to a specific angle, and enabling recovery in case of failure. The protocol scales effectively through geographic partitioning, as the size of the Q-learning table is determined by the number of hexagonal cells rather than by the number of UAVs. Path calculation is performed in two stages: initially, a path composed of cells, and then a mapping that represents each cell as a node with the best relay characteristics. Extensive simulation for dense network has been driven under different conditions, including varying cell sizes, UAV densities and speeds, network load, and learning period to demonstrate the robustness of our protocol in terms of packet delivery ratio and transmission delays.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103913"},"PeriodicalIF":4.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AUV-Assisted data collection using hybrid clustering and reinforcement learning in underwater acoustic sensor networks 基于混合聚类和强化学习的水下机器人辅助数据采集

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-27 DOI: 10.1016/j.adhoc.2025.103877

Yanxia Chen , Rongxin Zhu , Azzedine Boukerche , Qiuling Yang

{"title":"AUV-Assisted data collection using hybrid clustering and reinforcement learning in underwater acoustic sensor networks","authors":"Yanxia Chen , Rongxin Zhu , Azzedine Boukerche , Qiuling Yang","doi":"10.1016/j.adhoc.2025.103877","DOIUrl":"10.1016/j.adhoc.2025.103877","url":null,"abstract":"<div><div>Underwater Acoustic Sensor Networks (UASNs) have garnered increasing attention for applications such as environmental monitoring, disaster response, and marine resource exploration. Despite their advantages, including self-organization and flexible deployment, UASNs face significant challenges in the underwater environment, such as energy constraints, propagation delays, and limited bandwidth. Addressing these challenges requires efficient methods to optimize energy usage and data transmission. In this work, we propose ACRL, a clustering and reinforcement learning-based approach for underwater data collection. ACRL combines a hybrid Fuzzy C Means (FCM) and Firefly Algorithm (FA) to optimize clustering and cluster head selection, reducing energy consumption and workload while maintaining efficient data collection. Additionally, ACRL leverages Q-learning to refine Autonomous Underwater Vehicle (AUV) trajectory planning. Extensive simulations demonstrate that ACRL achieves reduced energy consumption and data collection delay, outperforming existing methods under various scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103877"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing load-balanced resource allocation in next-generation mobile networks: A parallelized multi-objective approach 下一代移动网络中优化负载均衡资源分配：一种并行多目标方法

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-27 DOI: 10.1016/j.adhoc.2025.103912

Jesús Calle-Cancho , Jesús Galeano-Brajones , David Cortés-Polo , Javier Carmona-Murillo , Francisco Luna-Valero

{"title":"Optimizing load-balanced resource allocation in next-generation mobile networks: A parallelized multi-objective approach","authors":"Jesús Calle-Cancho , Jesús Galeano-Brajones , David Cortés-Polo , Javier Carmona-Murillo , Francisco Luna-Valero","doi":"10.1016/j.adhoc.2025.103912","DOIUrl":"10.1016/j.adhoc.2025.103912","url":null,"abstract":"<div><div>The rapid evolution of mobile communications, driven by the proliferation of mobile devices and data-intensive applications, has driven an unprecedented increase in data traffic, pushing the current network infrastructure to its limits. In Beyond 5G and future 6G networks, minimizing network latency is crucial to support next-generation applications, such as immersive media, autonomous systems, and critical real-time services, all of which demand ultra-low latency and high reliability. In Multi-access Edge Computing environments, where future 6G networks will be deployed, efficient allocation of virtual base stations to the access network in dense environments will be essential to optimize performance and maintain quality of service. This efficient allocation will be key to effectively addressing the challenges present in these settings. This paper addresses this problem through a parallelized multi-objective evolutionary algorithm that simultaneously optimizes signaling delay, data plane overhead, and load balancing. By leveraging a Pareto-based approach, we provide a set of optimal trade-offs that enhance network adaptability and efficiency beyond traditional single-objective methods. Moreover, we introduce a novel metric inspired by the Sharpe ratio to evaluate the efficiency of load distribution across the network. Experimental results in various network topologies show that our approach significantly enhances network performance, achieving reductions in data plane overhead of up to 51.5% and 77.9% in signaling delay compared to a state-of-the-art solution based on a specialized heuristic. By providing a set of non-dominated solutions, our approach enables network operators to select configurations that best meet specific quality of service requirements and service priorities, thereby improving network adaptability and resilience under varying conditions.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103912"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Battery life optimization in LoRa networks using spreading factor reallocation 基于扩展因子再分配的LoRa网络电池寿命优化

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-27 DOI: 10.1016/j.adhoc.2025.103911

Ian H. de Andrade, Luís Henrique M.K. Costa, Rodrigo S. Couto

引用次数: 0

Location privacy-preserving ride matching with verifiable and collusion resistance for Ride-Hailing Services 网约车服务的位置隐私保护、可验证和抗合谋乘车匹配

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-26 DOI: 10.1016/j.adhoc.2025.103910

Hui Huang , Rui Zhang , Chenhuang Wu , Shihui Lin

{"title":"Location privacy-preserving ride matching with verifiable and collusion resistance for Ride-Hailing Services","authors":"Hui Huang , Rui Zhang , Chenhuang Wu , Shihui Lin","doi":"10.1016/j.adhoc.2025.103910","DOIUrl":"10.1016/j.adhoc.2025.103910","url":null,"abstract":"<div><div>Ride-Hailing Services (RHS) provide riders with convenient travel services and offer drivers economic incentives. However, during the process of requesting ride-matching from the Ride-Hailing Service Provider (RHSP), the transmission of data and query processing between riders and drivers may potentially expose sensitive user information, such as boarding and alighting locations and movement trajectories. Moreover, the RHSP might collude with drivers to manipulate the accuracy and integrity of ride-matching results. In this paper, we propose a novel privacy-preserving and collusion-resistant driver verification scheme (PPCRV) that supports privacy protection, collusion resistance, verifiability, and accountability. Our approach allows for personalized settings, enabling both riders and drivers to protect their location privacy within predefined cloaked areas. Additionally, we employ prefix encoding to link identities with location prefixes, generating unpredictable movement trajectories. We also utilize Indistinguishable Bloom Filter (IBF) for efficient querying. By IBF trees and proof information, we ensure verifiable matching results, allowing riders to independently replicate the matching process. Simultaneously, we apply traceable signature technology to maintain user anonymity while enabling effective identity verification and traceability, ensuring that malicious users can be held accountable when required. Finally, we conduct theoretical analyses and experiments to evaluate the scheme’s performance in terms of computational cost and communication overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103910"},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TReB: Task dependency aware-Resource allocation for Internet of Things using Binary offloading TReB：任务依赖感知-使用二进制卸载的物联网资源分配

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-25 DOI: 10.1016/j.adhoc.2025.103909

Priyanka Soni , Ajay Gajanan Hajare , Keerthan Kumar T.G. , Sourav Kanti Addya

{"title":"TReB: Task dependency aware-Resource allocation for Internet of Things using Binary offloading","authors":"Priyanka Soni , Ajay Gajanan Hajare , Keerthan Kumar T.G. , Sourav Kanti Addya","doi":"10.1016/j.adhoc.2025.103909","DOIUrl":"10.1016/j.adhoc.2025.103909","url":null,"abstract":"<div><div>The rapid growth of Internet of Things (IoT) applications in domains such as healthcare, smart homes, and autonomous vehicles has led to an exponential increase in data generated by compute intensive tasks. Efficiently offloading these tasks to nearby computational resources in fog environments remains a significant challenge due to the inherent heterogeneity and constrained resources of Fog Nodes (FNs). Most of the existing approaches fail to address the trade-offs between latency, energy, and resource utilization, particularly when managing dependent and independent task workloads. Moreover, establishing an offloading strategy within a densely interconnected IoT network is known to be <span><math><mrow><mi>N</mi><mi>P</mi></mrow></math></span>-hard. To overcome these limitations, in this work, we propose a <strong>T</strong>ask dependency-Aware <strong>Re</strong>source allocation for IoT using <strong>B</strong>inary offloading (<span>TReB</span>) framework by considering both independent and dependent tasks of IoT applications. The <span>TReB</span> utilizes the Analytic Hierarchy Process (AHP) technique to generate the preferences of FNs and tasks by considering diverse attributes. With preferences established, a binary offloading is handled through a one-to-many matching procedure, utilizing a Deferred Acceptance Algorithm (DAA). It allows <span>TReB</span> to jointly minimize system energy consumption, latency, and the number of outages in an IoT network. We evaluated the effectiveness of <span>TReB</span> through simulation experiments, and results show that the proposed approach achieves a 49.1%, 62.4%, and 41.7% minimization in overall system latency, energy, and outages compared to the existing baselines.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103909"},"PeriodicalIF":4.4,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0