Ad Hoc Networks最新文献

{"title":"A simulation-informed robust optimization framework for the design of energy efficient underwater sensor networks","authors":"Ozhan Eren ,&nbsp;Aysegul Altin-Kayhan","doi":"10.1016/j.adhoc.2025.103933","DOIUrl":"10.1016/j.adhoc.2025.103933","url":null,"abstract":"<div><div>Given that data generation rates of sensors might deviate from what is anticipated during the configuration phase due to several reasons such as event-driven data spikes, dynamic environmental conditions, propagation delay and data buffering, etc., designing robust transmission schemes is pivotal for Underwater Wireless Sensor Networks (UWSNs). Despite advances in underwater technologies, UWSN optimization under traffic uncertainty remains underexplored. This paper presents a novel simulation-informed robust optimization framework for designing energy-efficient UWSNs. We begin with a comprehensive review of the literature that addresses uncertainty in system parameters for wireless network design, followed by an analysis of research focused on modeling the motion of underwater objects. Then, we propose simulating an intrusion detection environment that includes moving targets, such as autonomous underwater vehicles and submarines navigating along 3D routes. To improve simulation accuracy, real bathymetric data is used to define the interactions between system elements including vehicles, sensors, and ocean topography. Then, the expected data generation rates of sensors and the corresponding admissible intervals are determined using the results from multiple simulation runs. The resulting data set is used to determine and conduct comprehensive analyses on optimal deterministic and robust configurations, where the maximum battery allocated to a sensor is minimized. The scenario-based comparison of network functional time between deterministic and robust configurations indicates that the robust design substantially outperforms the deterministic configuration across all data rate realizations, even at the lowest level of deviation from the expectations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103933"},"PeriodicalIF":4.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306225","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}

{"title":"Real-time explainable IoT security with machine learning and CTGAN-enhanced detection for resource-constrained devices","authors":"Tasnimul Hasan,&nbsp;Samia Tasnim","doi":"10.1016/j.adhoc.2025.103937","DOIUrl":"10.1016/j.adhoc.2025.103937","url":null,"abstract":"<div><div>The security threats and risks posed by Internet of Things (IoT) devices have been increasing significantly in recent times. Hence, an Intrusion Detection System (IDS) is required to handle and filter out cyber-attacks. Traditional IDSs face a major challenge in class imbalance within the data, which is the case for many real-world datasets related to intrusion, and a lack of model interpretability. In this paper, we introduce a novel IDS by fusing Generative Adversarial Network (GAN) and Explainable AI (XAI) techniques. Our proposed IDS uses Conditional Tabular GAN (CTGAN) as the synthetic data generator to address class imbalance issues. Additionally, in order to have global and local model interpretability of the proposed IDS, two XAI approaches are followed: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed IDS achieves accuracy between 97.20% and 100%, F1 score between 89.34% and 100%, test time from 0.0104 s to 0.5686 s, and model size ranging from 2.73 kB to 1510 kB across different datasets. To validate practical applicability, we deploy the best-performing models on a resource-constrained edge device (e.g., Jetson Nano), achieving efficient testing times and demonstrating suitability for real-time applications. We conduct a quantitative comparison with state-of-the-art methods, demonstrating improved performance, enhanced interpretability, and increased model transparency through XAI integration.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103937"},"PeriodicalIF":4.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366052","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}

{"title":"RAD-IoMT: Robust adversarial defence mechanisms for IoMT medical image analysis","authors":"Saifur Rahman,&nbsp;Shantanu Pal,&nbsp;Amirmohammad Fallah,&nbsp;Robin Doss,&nbsp;Chandan Karmakar","doi":"10.1016/j.adhoc.2025.103935","DOIUrl":"10.1016/j.adhoc.2025.103935","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT) represents a significant technological advancement with exceptional capabilities across various domains, particularly in healthcare. IoMT integrates medical devices, software applications, and healthcare systems, enabling seamless communication and data exchange over the Internet. As deep learning (DL) continues to evolve, applications within IoMT are increasingly dominant. However, these DL applications face new reliability challenges, particularly due to the security threat posed by adversarial attacks. These attacks introduce subtle and often imperceptible perturbations that can lead to significantly erroneous predictions by classifiers. To address these reliability concerns, we propose a novel security mechanism using an attack detector specifically designed to counter adversarial attacks within IoMT environments. This approach leverages a transformer model to enhance resistance against such attacks. We validate our method through experiments using datasets for skin cancer, retina damage, and chest X-rays, testing against both white-box attacks (e.g., Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD)) and black-box attacks (e.g., Additive Gaussian Noise (AGN) and Additive Uniform Noise (AUN)). Our proposed attack detector exhibited F1 and accuracy 0.91 and 0.94. Following the successful application of our attack detector, the disease classification model achieved an average F1 and accuracy of 0.97 and 0.98 compared to the attack model performance (F1 and accuracy of 0.64 and 0.60, respectively) across the three datasets.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103935"},"PeriodicalIF":4.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280484","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}

{"title":"Adaptive UAV deployment for enhanced connectivity in disaster-stricken emergency networks: A multi-objective approach","authors":"Bülent Bilgehan ,&nbsp;Özlem Sabuncu","doi":"10.1016/j.adhoc.2025.103953","DOIUrl":"10.1016/j.adhoc.2025.103953","url":null,"abstract":"<div><div>The work in this research aims to help in cases where a sudden natural calamity strikes. The compromised communication systems are unable to offer the essential services needed. This is a critical situation where vulnerable individuals urgently need access to emergency services through the unmanned aerial vehicle (UAV) network. The main hurdle here is quickly figuring out how to link the disaster area location to the base station. This study presents a dynamic UAV-assisted framework that utilizes multi-objective optimization for adaptive deployment, distinct from conventional static base station or single-layered UAV network methods. This research proposes a UAV solution that fulfills various objectives within ad hoc networks for emergency assistance. The study assumes the initial and the target locations are known. The study then introduces a communication relay and the necessary networking, effectively reducing the time it takes for the UAV to connect. The proposed approach dynamically optimizes UAV positioning and path planning, ensuring efficient connectivity under uncertain conditions. It then presents a multi-objective search algorithm for finding the exact point to assist in the disaster area and guides the UAVs to various paths for ultimate goals. Unlike existing strategies, this method enhances UAV adaptability, reduces energy consumption, and optimizes real-time deployment.</div><div>Additionally, the proposed method selects branching nodes, maximizing available paths and reducing network costs for communication in a research environment. This approach significantly improves network resilience and adaptability compared to traditional UAV deployment strategies. The simulations produce a 20 % reduction in network time, a 15 % increase in efficiency, and a 25 % reduction in UAV deployment compared with existing methods. The real-world experimental test produced a power consumption of 150 W, generally between 200–400 W. The experimental test verifies the numerical simulation results and demonstrates the proposed approach's effectiveness, showcasing its superiority in real-world disaster response scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103953"},"PeriodicalIF":4.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271110","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}

{"title":"DT-BFL: Digital Twins for Blockchain-enabled Federated Learning in Internet of Things networks","authors":"Wael Issa ,&nbsp;Nour Moustafa ,&nbsp;Benjamin Turnbull ,&nbsp;Kim-Kwang Raymond Choo","doi":"10.1016/j.adhoc.2025.103934","DOIUrl":"10.1016/j.adhoc.2025.103934","url":null,"abstract":"<div><div>Sixth-generation (6G) wireless networks enable faster, smarter, and more connected Internet of Things (IoT) systems, which in turn support edge intelligence and real-time decision-making. Federated learning (FL) supports this shift by allowing devices to collaboratively train models without sharing raw data, which helps to protect user privacy. There are, however, potential security challenges in FL deployments. For example, security challenges such as poisoning attacks and Byzantine clients can compromise the training process and degrade the accuracy and reliability of the global model. Although existing methods can detect malicious updates, many advanced attacks still bypass statistical defenses relying on metrics such as median and distance. In other words, developing an FL system that ensures both reliable decision-making and privacy and security guarantees in IoT networks remains a significant challenge. This study introduces a Digital Twin-driven Blockchain-enabled Federated Learning (DT-BFL) framework designed for IoT networks. The framework creates a digital representation of the IoT environment to support secure and decentralized edge intelligence using blockchain and federated learning technologies. DT-BFL is built to detect and filter out potentially poisoned model updates from malicious participants. This is achieved through a new smart contract-enabled decentralized aggregation method called Local Updates Purify (LUP). LUP uses a two-stage filtering process: First, it applies Median Absolute Deviation (MAD) to initially remove outliers, then uses statistical features and clustering to separate honest from malicious updates before aggregating the global model. It also assigns a Trust Score (TS) to each participant based on how much their updates differ from the global model and then uses a genuine criterion to select honest clients by evaluating trust scores, update similarity, and deviation from the global model. Experimental results show that DT-BFL effectively defends against various poisoning attacks on datasets like MNIST, ToN-IoT, and CIFAR-10 using models such as CNN, MLP, ResNet, and DenseNet, and maintains high accuracy even when 50% of the clients are malicious. Using a permissioned blockchain further secures the system by enabling aggregation of the decentralized model and authentication of clients through smart contracts. The source code is available on <span><span>https://github.com/UNSW-Canberra-2023/LUP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103934"},"PeriodicalIF":4.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271111","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}

{"title":"MRM-PSO: An enhanced particle swarm optimization technique for resource management in highly dynamic edge computing environments","authors":"Sajjad Molaei ,&nbsp;Masoud Sabaei ,&nbsp;Javid Taheri","doi":"10.1016/j.adhoc.2025.103952","DOIUrl":"10.1016/j.adhoc.2025.103952","url":null,"abstract":"<div><div>The resource constraints of Internet of Things (IoT) devices pose significant hurdles to delay-sensitive applications that operate in dynamic and wireless settings. Since offloading tasks to cloud servers can be hindered by security concerns and latency issues, edge and fog computing bring computation closer to data sources. Given their inherently distributed and resource-constrained nature, edge/fog-enabled platforms require more advanced resource-management solutions to address the numerous constraints encountered in dynamic and wireless environments. This study introduces an innovative resource management algorithm designed for dynamic edge/fog computing environments, tailored to real-world applications, with the objective of enhancing delay performance through optimal container placement. The resource management problem incorporates mobility patterns in wireless settings to reduce migration delay and the processing history of edge/fog nodes to provide a novel method for computing processing delay, resulting in a combined optimization problem expressed in an integer linear programming (ILP) format. To address the formulated NP-Hard problem, we developed a low-complexity Metaheuristic Resource Management algorithm based on Particle Swarm Optimization (MRM-PSO) with effective particle modelling. Our experimental findings demonstrate that greedy heuristics and genetic algorithm (GA) are inadequate for efficiently resolving a given problem, whereas our proposed MRM-PSO algorithm efficiently locates near-optimal solutions within reasonable execution times when compared to exact solvers. MRM-PSO reduces execution time by up to 663.82 % in the worst case and 2307.5 % in the best case. Furthermore, it attains a delay that is just 0.98 % higher in the best case and 5.54 % higher in the worst case compared to the optimal solution.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103952"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280486","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}

{"title":"Enhancing wircless channel authentication in industrial control: Attack-resistant CSI-based PUF","authors":"Yubo Song ,&nbsp;Wenchang Liu ,&nbsp;Hongyu Zhu ,&nbsp;Yi Gong ,&nbsp;Yang Li ,&nbsp;Jiyuan Huang ,&nbsp;Yazhi Deng","doi":"10.1016/j.adhoc.2025.103919","DOIUrl":"10.1016/j.adhoc.2025.103919","url":null,"abstract":"<div><div>The extensive deployment of wireless networks in industrial settings has led to a surge in wireless-connected devices, presenting formidable security challenges. Traditional encryption approaches, relying on cryptographic keys without binding to physical hardware details, are highly susceptible to cloning attacks when the keys are compromised. This paper presents a novel solution: a Channel State Information-based Physical Unclonable Function (CSI-PUF) for wireless devices. By leveraging CSI, it generates a unique device identity. A meticulously designed fuzzy extractor algorithm is incorporated. In the enrollment phase, random numbers are generated and encoded with BCH error correction codes. During the reconstruction phase, errors in the CSI-PUF output bit sequence are transferred to the BCH-encoded random numbers. This allows for error elimination through decoding, safeguarding the confidentiality of CSI. A prototype WiFi terminal authentication system based on this CSI-PUF framework is developed and implemented. Experimental evaluations in an interference-free industrial control environment reveal that the system attains an average authentication success rate exceeding 95%. This significantly bolsters the security of WiFi-enabled devices, indicating that the CSI-PUF can effectively enhance the authentication security of wireless devices in industrial scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103919"},"PeriodicalIF":4.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220970","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}

{"title":"Leading the Way: Reducing network traffic in vehicular Ad Hoc networks through cluster leader algorithms","authors":"J.V.G. Ferreira ,&nbsp;M.E.S. Freire ,&nbsp;E.M. Cruz ,&nbsp;C.V.S. Prazeres ,&nbsp;G.B. Figueiredo ,&nbsp;M.L.M. Peixoto","doi":"10.1016/j.adhoc.2025.103932","DOIUrl":"10.1016/j.adhoc.2025.103932","url":null,"abstract":"<div><div>The escalating data traffic from the growing number of connected vehicles equipped with sensors leads to significant challenges for communication resources and the shared service infrastructure of Vehicular Ad hoc Networks (VANETs). To tackle these challenges, traditional clustering algorithms such as K-means, Fuzzy C-means and DBSCAN have been used to group vehicles into manageable clusters. By organizing vehicles into clusters, these clustering algorithms select a representative subset of vehicles within each cluster to handle data communication, minimizing redundant transmissions and ensuring efficient data dissemination, thereby significantly reducing network congestion. However, relying solely on a subset for data transmission may be insufficient, as this approach can still generate substantial data. Furthermore, if the subset is geographically dispersed, it can lead to a loss of accuracy in data representation and communication. To address these limitations, the Leader Election Algorithm for Representation Identification in Cluster (LEADER) is introduced to designate a representative leader within each cluster, enhancing data transmission. LEADER aims to establish a message control mechanism within VANETs, optimizing data transmission and reducing communication overload. The experimental performance evaluation demonstrated that LEADER reduced network traffic data by up to 45% on average, while maintaining accuracy in representing groups.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103932"},"PeriodicalIF":4.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239526","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}

{"title":"Mitigation of frequent-handover in 5G and beyond using handover candidate cells list optimization","authors":"Abbas Ibrahim Mbulwa,&nbsp;Hoe Tung Yew,&nbsp;Ali Chekima,&nbsp;Jamal Ahmad Dargham","doi":"10.1016/j.adhoc.2025.103929","DOIUrl":"10.1016/j.adhoc.2025.103929","url":null,"abstract":"<div><div>5G and beyond networks require diverse spectrum allocations: lower band for coverage, mid-band for both coverage and capacity, and higher band for high to ultra-high data rates. To meet increasing capacity demands, small cells are deployed, significantly increasing base station (BS) proximity and network densification. This results in frequent handovers; In existing techniques, the handover candidate cells list (HCL) are reported as-is to the target selection (handover decision-making) stage, which results in “unnecessary” cells included as potential handover target, leading to high unnecessary handovers, ping-pong effects, and handover failures, causing signaling overhead, network congestion, and session disruptions for mobile users. This paper proposes an optimization approach for HCL to improve handover performance in 5G and beyond networks. The approach utilizes mobility load balancing (MLB) to identify overloaded cells/BSs in the neighbor cells list (NCL), which are then excluded from the HCL prior to the target selection stage. Additionally, it considers angular displacement (proximity) to remove cells that are significantly displaced from the UE’s direction of movement. This process reduces or eliminates potentially unnecessary candidates from the handover decision phase, thereby minimizing frequent handovers, unnecessary handovers, ping-pong effects, and handover failures. A comparative analysis is presented for the handover procedure with and without HCL optimization, in both manual and auto-tuning mobility robustness optimization (MRO) methods, in absolute and relative handover measurement strategies (events). The results indicate that HCL optimization significantly enhances handover performance across various MRO methods for absolute handover measurement events while maintaining high throughput. Handover performance improvements range from 17.98% to 96.80% for the handover rate (HR)/frequency, 58.56% to 99.75% for the ping-pong handover rate (PHR), 0.62% to 84.29% for the unnecessary handover rate (UHR), and 66% to 99.90% for the handover failure rate (HFR). This analysis suggests that HCL optimization should be considered a vital component in the design and implementation of handover management and control to maximize efficiency and reliability. The proposed approach optimizes handovers with minimal additional complexity, making it a viable solution for 5G and beyond networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103929"},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239654","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}

{"title":"Extreme learning machine-driven joint user mobility and content popularity-based proactive caching in multi-tier wireless networks","authors":"Ayaz Ahmad ,&nbsp;Fawad Ahmad ,&nbsp;Salman Atif ,&nbsp;Adel Aldalbahi","doi":"10.1016/j.adhoc.2025.103920","DOIUrl":"10.1016/j.adhoc.2025.103920","url":null,"abstract":"<div><div>Proactive caching in cache-enabled wireless networks for 5G and 6G is a promising technique for alleviating the immense data traffic on bandwidth-limited backhaul links while minimizing content latency and handovers, particularly in multi-tier cellular networks (MTCNs). In a MTCNs, caching nodes are densely deployed, reducing the distance between users and their associated base stations (BS), which significantly enhances the performance of the caching system. In this context, various efficient content caching techniques have been proposed for MTCNs. However, most of these methods do not consider user mobility, which can significantly impact the performance of content caching in MTCNs. This paper explores the impact of user mobility and the popularity of the content in cache-enabled MTCNs and proposes two proactive caching techniques tailored to these factors. The first method is the extreme learning machine-based mobility-aware Proactive (E-MAP) Caching Scheme, which focuses solely on the mobility of the user. The second method, the Extreme Learning Machine-based Mobility and Popularity Aware Proactive (E-MAPP) caching scheme, considers both user movement and content popularity. An optimization problem is formulated to minimize the average content latency for the caching system. Numerical simulations demonstrate that the proposed technique outperforms traditional methods in terms of average content latency and cache hit ratio (CHR).</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103920"},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271112","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}