Ad Hoc Networks最新文献

{"title":"Reliability-Aware Packet Replication in Multi-Path Data Transmission for Mission-Critical IoT Networks","authors":"Soumya Nandan Mishra,&nbsp;Manas Khatua","doi":"10.1016/j.adhoc.2025.103940","DOIUrl":"10.1016/j.adhoc.2025.103940","url":null,"abstract":"<div><div>Mission-critical IoT applications require a strict reliability guarantee of at least 99% to ensure seamless operation. However, due to the resource-constrained nature of IoT networks, data transmissions inherently suffer from losses. This contradiction presents significant challenges for the traditional IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), which was originally designed for general IoT networks. To address this issue, various multi-path RPL-based routing approaches have been proposed. One such solution is Reliable Multi-Path RPL (RMP-RPL), which attempts to enhance reliability by replicating packets and forwarding them through multiple parents. However, meeting the reliability requirement even with a multi-parent-based approach is difficult when the wireless links have high error rates. This is because one transmission attempt to each parent for a packet is not enough for such links. On the other hand, increasing the number of parents is also limited by many factors like resource consumption. To address these issues, we propose Reliability-Aware Packet Replication in Multi-Path Data Transmission (RAPID), which dynamically selects the number of parents and optimally determines the number of replicated packets per parent to meet the reliability constraint while minimizing redundant transmissions. The proposed scheme introduces a joint delivery ratio metric, and proposes greedy-based (RAPID-G) and approximation-based (RAPID-A) packet replication strategies to manage packet replication efficiently. Experimental results in Contiki COOJA simulator show that RAPID-A can achieve the reliability requirements of 90%, 95% and 99% under varying packet reception ratio and network density with an average energy consumption reduction of 34.23% as compared to RMP-RPL. The proposed protocol, RAPID-G, outperforms another multi-path algorithm, LFC, by 41.23%, 39.23% and 35.63% in terms of packet delivery ratio, end-to-end delay and energy consumption, respectively, and RAPID-A outperforms LFC by 38.63%, 44.12%, and 40.12% in terms of packet delivery ratio, end-to-end delay and energy consumption, respectively.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103940"},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297188","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":"QLA-MAODV: A Q-learning adaptive multicast routing protocol for mobile ad-hoc networks","authors":"Ola Ashour ,&nbsp;Thomas Kunz ,&nbsp;Marc St-Hilaire","doi":"10.1016/j.adhoc.2025.103942","DOIUrl":"10.1016/j.adhoc.2025.103942","url":null,"abstract":"<div><div>Mobile Ad-hoc Networks face challenges in achieving efficient multicasting due to dynamic topology changes and unreliable links. Existing multicast approaches either suffer from low packet delivery ratio or high overhead. These approaches rely on simple metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it remains available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor packet delivery ratio, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, a Q-Learning Adaptive Multicast Ad-hoc On-Demand Distance Vector routing protocol is proposed. It is an adaptive and bandwidth-efficient solution that utilizes link reliability as a routing metric instead of hop count, aiming to build a more stable multicast tree. By leveraging Q-learning principles, the proposed protocol continuously updates path costs to detect any deterioration. Additionally, the protocol dynamically explores the network using periodic group hello messages, enabling the identification of alternative paths and proactively switches to them if path costs deteriorate. Simulations conducted in Network Simulator 3 demonstrate the superiority of the proposed protocol over the traditional Multicast Ad-hoc On-Demand Distance Vector protocol. Furthermore, it outperforms a modified version, called Multicast Ad-hoc On-Demand Distance Vector-Route Reliability, that uses link reliability as a metric, demonstrating enhanced packet delivery ratio and reduced multicast-related overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103942"},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321792","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":"Indoor localization using router-to-router RSSI and transfer learning for dynamic environments","authors":"Liuyi Yang,&nbsp;Patrick Finnerty,&nbsp;Chikara Ohta","doi":"10.1016/j.adhoc.2025.103938","DOIUrl":"10.1016/j.adhoc.2025.103938","url":null,"abstract":"<div><div>With the increasing demand for indoor localization, received signal strength indicator (RSSI)-based fingerprint localization has gained widespread attention due to its low equipment costs. Traditional methods only use RSSI data collected from user devices to train localization models, but the coarse granularity of RSSI often limits accuracy. Additionally, changes in the environment, such as door opening and closing or furniture rearrangements, can render these models ineffective. While resource-intensive and time-consuming, data re-collection and model retraining are essential for capturing updated signal characteristics after environment changes, ensuring the model remains accurate and effective. To enhance localization accuracy, we expand on traditional approaches by incorporating RSSI data measured between wireless routers as additional fingerprint features, achieving nearly a 20% accuracy improvement. Furthermore, we address the challenges of dynamic environments by introducing a multi-task domain-adversarial transfer learning method, which extracts consistent features before and after environment changes. Transfer learning allows us to leverage knowledge from the environment before the change, thereby reducing the need for data re-collection after the change. Experiment results from simulated, real-world, and open dataset environments confirm the effectiveness of the proposed method in dynamic indoor localization. Our approach reduced the mean error distance (MED) by 35%, 44%, and 28%, respectively, with only 16%, 20%, and 17% of the data re-collected.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103938"},"PeriodicalIF":4.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297187","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":"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}