Ad Hoc Networks最新文献

{"title":"Multi-stage deep learning-based intrusion detection system for automotive Ethernet networks","authors":"Luigi F. Marques da Luz ,&nbsp;Paulo Freitas de Araujo-Filho ,&nbsp;Divanilson R. Campelo","doi":"10.1016/j.adhoc.2024.103548","DOIUrl":"10.1016/j.adhoc.2024.103548","url":null,"abstract":"<div><p>Modern automobiles are increasing the demand for automotive Ethernet as a high-bandwidth and flexible in-vehicle network technology. However, since Ethernet does not have native support for authentication or encryption, intrusion detection systems (IDSs) are becoming an attractive security mechanism to detect malicious activities that may affect Ethernet-based communication in cars. This paper proposes a novel multi-stage deep learning-based intrusion detection system to detect and classify cyberattacks in automotive Ethernet networks. The first stage uses a Random Forest classifier to detect cyberattacks quickly. The second stage, on the other hand, uses a Pruned Convolutional Neural Network that minimizes false positive rates while classifying different types of cyberattacks. We evaluate our proposed IDS using two publicly available automotive Ethernet intrusion datasets. The experimental results show that our proposed solution detects cyberattacks with a similar detection rate and a faster detection time compared to other state-of-the-art baseline automotive Ethernet IDSs.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025751","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":"Factor graph-based deep reinforcement learning for path selection scheme in full-duplex wireless multihop networks","authors":"Zhihan Cui,&nbsp;Yuto Lim,&nbsp;Yasuo Tan","doi":"10.1016/j.adhoc.2024.103542","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103542","url":null,"abstract":"<div><p>A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1570870524001537/pdfft?md5=9b9830eb5843b9e00111ce0e43e3db17&pid=1-s2.0-S1570870524001537-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A federated learning-based zero trust intrusion detection system for Internet of Things","authors":"Danish Javeed ,&nbsp;Muhammad Shahid Saeed ,&nbsp;Muhammad Adil ,&nbsp;Prabhat Kumar ,&nbsp;Alireza Jolfaei","doi":"10.1016/j.adhoc.2024.103540","DOIUrl":"10.1016/j.adhoc.2024.103540","url":null,"abstract":"<div><p>The rapid expansion of Internet of Things (IoT) devices presents unique challenges in ensuring the security and privacy of interconnected systems. As cyberattacks become more frequent, developing an effective and scalable Intrusion Detection System (IDS) based on Federated Learning (FL) for IoT becomes increasingly complex. Current methodologies struggle to balance spatial and temporal feature extraction, especially when dealing with dynamic and evolving cyber threats. The lack of diversity in datasets used for FL-based IDS evaluations further impedes progress. There is also a noticeable tradeoff between performance and scalability, particularly as the number of edge devices in communication increases. To address these challenges, this article introduces a horizontal FL model that combines Convolutional Neural Networks (CNN) and Bidirectional Long-Term Short Memory (BiLSTM) for effective intrusion detection. This hybrid approach aims to overcome the limitations of existing methods and enhance the effectiveness of intrusion detection in the context of FL for IoT. Specifically, CNN is used for spatial feature extraction, enabling the model to identify local patterns indicative of potential intrusions, while the BiLSTM component captures temporal dependencies and learns sequential patterns within the data. The proposed IDS follows a zero-trust model by keeping the data on local edge devices and sharing only the learned weights with the centralized FL server. The FL server then aggregates updates from various sources to optimize the accuracy of the global learning model. Experimental results using CICIDS2017 and Edge-IIoTset demonstrate the effectiveness of the proposed approach over centralized and federated deep learning-based IDS.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1570870524001513/pdfft?md5=ce7346cd6f957b06bddd591858574f96&pid=1-s2.0-S1570870524001513-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141041402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles","authors":"Jianbin Xue,&nbsp;Jia Yao,&nbsp;Jiahao Wang","doi":"10.1016/j.adhoc.2024.103545","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103545","url":null,"abstract":"<div><p>With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140918699","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":"Hierarchical multistep approach for intrusion detection and identification in IoT and Fog computing-based environments","authors":"Cristiano Antonio de Souza ,&nbsp;Carlos Becker Westphall ,&nbsp;Jean Douglas Gomes Valencio ,&nbsp;Renato Bobsin Machado ,&nbsp;Wesley dos R. Bezerra","doi":"10.1016/j.adhoc.2024.103541","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103541","url":null,"abstract":"<div><p>Special security techniques, such as intrusion detection mechanisms, are indispensable in modern computer systems. With the emergence of the Internet of Things they have become even more important. It is important to detect and identify the attack in a category so that countermeasures specific to the threat category can be resolved. However, most existing multiclass detection approaches have some weaknesses, mainly related to detecting specific categories of attacks and problems with false positives. This article addresses this research problem and advances state-of-the-art, bringing contributions to a two-stage detection architecture called DNNET-Ensemble, combining binary and multiclass detection. While the benign traffic can be quickly released on the first detection, the intrusive traffic can be subjected to a robust analysis approach without causing delay issues. Additionally, we propose the DNNET binary approach for the binary detection level, which can provide more accurate and faster binary detection. We present the proposal of a federated strategy to train the neural model of the DNNET method without sending data to the cloud, thus preserving the privacy of local data. The proposed Hybrid Attribute Selection strategy can find an optimal subset of attributes through a wrapper method with a lower training cost due to pre-selection using a filter method. Furthermore, the proposed Soft-SMOTE improvement allows operating with a balanced dataset with a minor training time increase, even in scenarios where there are a large number of classes with a large imbalance among them. Results obtained from experiments on renowned intrusion datasets and laboratory experiments demonstrate that the approach can achieve superior detection rates and false positive performance compared to other state-of-the-art approaches.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947347","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":"Asymmetric wake-up scheduling based on block designs for Internet of Things","authors":"Woosik Lee ,&nbsp;Jong-Hoon Youn ,&nbsp;Teuk-Seob Song","doi":"10.1016/j.adhoc.2024.103530","DOIUrl":"10.1016/j.adhoc.2024.103530","url":null,"abstract":"<div><p>In general, wireless sensors operate with a limited energy source, and energy efficiency is a critical design issue. In order to extend the operation time of wireless sensors, there have been many energy efficiency neighbor discovery protocols designed for wireless sensor networks (WSNs) such as Quorum-based, prime-number-based, and block-design-based protocols. Among them, the block-design-based approach was known to be more effective solutions for neighbor discovery in terms of the worst-case discovery latency for a given duty cycle. However, the original block- design-based approach is only applicable to a sensor network where all sensors have the same duty cycle. In order to expand a block-design-based neighbor discovery solution to asymmetric WSNs, we introduce a new neighbor discovery protocol (NDP) that combines two block-design-based schedules to produce a new set of discovery schedules for asymmetric WSNs. Furthermore, by using the Kronecker product method, we prove that any pair of neighboring sensors in the proposed protocol has at least one common active slot within a length of their discovery cycle. Furthermore, the results of the simulation study show that the proposed method is better than representative NDPs (such as Quorum, U-Connect, Disco, SearchLight, Hedis, and Todis) in terms of discovery latency and energy efficiency.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058206","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 privacy-preserving location data collection framework for intelligent systems in edge computing","authors":"Aiting Yao ,&nbsp;Shantanu Pal ,&nbsp;Xuejun Li ,&nbsp;Zheng Zhang ,&nbsp;Chengzu Dong ,&nbsp;Frank Jiang ,&nbsp;Xiao Liu","doi":"10.1016/j.adhoc.2024.103532","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103532","url":null,"abstract":"<div><p>With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1570870524001434/pdfft?md5=c6aa1e641a8186bdb8d3722893cdda2a&pid=1-s2.0-S1570870524001434-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight_PAEKS-based energy scheduling model considering priority in MicroGrid","authors":"Xialei Zhang,&nbsp;Yaoyang Wang,&nbsp;Tianjun Ma,&nbsp;Lifeng Guo,&nbsp;Zhiguo Hu","doi":"10.1016/j.adhoc.2024.103531","DOIUrl":"10.1016/j.adhoc.2024.103531","url":null,"abstract":"<div><p>Priority-based energy scheduling is proposed, whereby the data utility of measured user information in smart meters, including the priority and power demand of power consumers and the maximum power supply of power suppliers, is leveraged to ensure that in the case of limited power resources within islanded microgrids, energy is allocated to power consumers in the order of high to low priority. Nonetheless, in terms of data utility, most existing strategies only consider high-priority power consumers, neglecting the priority of power suppliers and low-priority power consumers, thus damaging the fairness of low-priority consumers and the interests of power suppliers, to the detriment of these groups. Moreover, smart meters are vulnerable to data integrity attacks, which may result in the manipulation of measured user information during the forwarding process, thereby disrupting normal priority-based energy scheduling. Therefore, focusing on data utility without considering data security is insufficient. Researchers have extensively investigated how to secure smart meters within advanced metering infrastructure (AMI), primarily employing cryptography-based methods to encrypt user information in measured meters and decrypt it at the microgrid central controller (MGCC), thereby protecting the data confidentiality of user information during the forwarding process and preventing data tampering. However, the prevailing cryptographic methods used to secure smart meters are extremely complex, and make smart meters and local controllers on the forwarding path unable to obtain any user information in which embedded consumers’ priority and cannot leverage the data utility of priority to forward information accordingly. In other words, existing models cannot simultaneously achieve data confidentiality and data utility during the forwarding process. To solve these issues, this study investigates a Lightweight_PAEKS-based energy scheduling model considering priority in microgrid (<em>LPESCP</em>). As a lightweight solution, the <em>LPESCP</em> comprehensively considers data confidentiality and data utility, as well as the priority and fairness of all users, to ensure the security of smart meters and optimize energy scheduling. In particular, the data utility problem is solved by using an optimization model that aims to maximize the global satisfaction degree of all users in terms of priority and fairness. The Lightweight_PAEKS and Paillier encryption scheme are used so that nodes on the forwarding path can successfully match priority-related keyword and forward information in order of the keyword corresponding emergency coefficient indicating the urgency levels of information, ensuring data utility, while do not know the specific keyword, emergency coefficient and other information, ensuring data confidentiality. To verify the effectiveness of the <em>LPESCP</em>, experiments are conducted for three cases generated based on random numbers. The results sh","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049933","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":"Delay-guaranteed Mobile Augmented Reality Task Offloading in Edge-assisted Environment","authors":"Jia Hao ,&nbsp;Yang Chen ,&nbsp;Jianhou Gan","doi":"10.1016/j.adhoc.2024.103539","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103539","url":null,"abstract":"<div><p>With the introduction of Augmented Reality (AR) into mobile devices, it becomes a trend to develop mobile AR applications in various fields. However, the execution of AR task demands the extensive resources of computation, memory and storage, which makes it difficult for mobile terminals with constrained hardware resources to carry out AR applications within the limited delay. In response to this challenge, we propose a mobile AR offloading method under the edge-assisted environment. Firstly, we divide an AR task into consecutive subtasks, and then collect the features of hardware, software, configuration, and runtime environments from the edge servers to be offloaded. With the features, we construct an AR subtask Execution delay Prediction Bayesian Network (EPBN) to predict the execution delay of different subtasks on each edge platform. Based on the prediction, we model the task offloading as the NP-hard Traveling Salesman Problem (TSP), and then propose a PSO-GA based solution by adopting the heuristic algorithm of Particle Swarm Optimization (PSO) to encode the offloading strategy and using Genetic Algorithm (GA) for particle update. The extensive experiments prove that the average performances of EPBN outperform the others with 17.23%, 23.97%, and 20.67% on micro-P, micro-R, and micro-F1 respectively, and the PSO-GA ensures that the offloading latency is reduced by nearly 5% compared to the suboptimal algorithm.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140879129","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":"Context-aware multi-modal route selection service for urban computing scenarios","authors":"Matheus Brito ,&nbsp;Camilo Santos ,&nbsp;Bruno S. Martins ,&nbsp;Iago Medeiros ,&nbsp;Marcos Seruffo ,&nbsp;Eduardo Cerqueira ,&nbsp;Denis Rosário","doi":"10.1016/j.adhoc.2024.103525","DOIUrl":"https://doi.org/10.1016/j.adhoc.2024.103525","url":null,"abstract":"<div><p>The rapid urban population growth introduces novel challenges to urban mobility scenarios, requiring innovative and connected solutions to relieve traffic congestion and enhance transportation efficiency, using unusual roads and public transportation modals. For instance, multi-modal routes allow more flexible modal combinations to offer smart multi-modal mobility. In this context, the route selection service must rely on different context information, such as criminality, accidents, air quality, and others, where Internet of Things technologies introduce active and ubiquitous sensing of many contexts along the urban scenario, providing data for statistical analysis to offer a safer and healthier urban reality. However, designing an efficient multi-modal route selection service that considers different context information to offer personalized routes is important. In this article, we describe a context-aware route selection service that considers adequate contextual information to provide routes according to the user’s preference. The multi-modal route selection service applies a multi-criteria method to give different degrees of importance to each criterion based on the user profile (<em>i.e.,</em> Worker, Green, Safe, and Tourist). We present extensive evaluation results from applying our multi-modal route selection approach to a London dataset. The approach successfully enables user-preferred transportation choices using four balanced selection profiles and ten route features. The proposed multi-modal route selection service demonstrates better performance in terms of economic, ecological, and time-saving metrics for each user profile compared to a greedy selection manner.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902093","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}