Ad Hoc Networks最新文献

{"title":"PLLM-CS: Pre-trained Large Language Model (LLM) for cyber threat detection in satellite networks","authors":"Mohammed Hassanin ,&nbsp;Marwa Keshk ,&nbsp;Sara Salim ,&nbsp;Majid Alsubaie ,&nbsp;Dharmendra Sharma","doi":"10.1016/j.adhoc.2024.103645","DOIUrl":"10.1016/j.adhoc.2024.103645","url":null,"abstract":"<div><p>Satellite networks are vital in facilitating communication services for various critical infrastructures. These networks can seamlessly integrate with a diverse array of systems. However, some of these systems are vulnerable due to the absence of effective intrusion detection systems, which can be attributed to limited research and the high costs associated with deploying, fine-tuning, monitoring, and responding to security breaches. To address these challenges, we propose a pre-trained Large Language Model for Cyber Security, for short PLLM-CS, which is a variant of pre-trained Transformers, which includes a specialized module for transforming network data into contextually suitable inputs. This transformation enables the proposed LLM to encode contextual information within the cyber data. To validate the efficacy of the proposed method, we conducted empirical experiments using two publicly available network datasets, UNSW_NB 15 and TON_IoT, both providing Internet of Things (IoT)-based traffic data. Our experiments demonstrate that proposed LLM method outperforms state-of-the-art techniques such as BiLSTM, GRU, and CNN. Notably, the PLLM-CS method achieves an outstanding accuracy level of 100% on the UNSW_NB 15 dataset, setting a new standard for benchmark performance in this domain.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167953","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 two-context-aware approach for navigation: A case study for vehicular route recommendation","authors":"Rafael S. Barbon ,&nbsp;Edmundo R.M. Madeira ,&nbsp;Ademar T. Akabane","doi":"10.1016/j.adhoc.2024.103655","DOIUrl":"10.1016/j.adhoc.2024.103655","url":null,"abstract":"<div><p>In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S157087052400266X/pdfft?md5=a85e68699e4c9d590fd509b0f69d7794&pid=1-s2.0-S157087052400266X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171603","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 joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks","authors":"Hong Min ,&nbsp;Amir Masoud Rahmani ,&nbsp;Payam Ghaderkourehpaz ,&nbsp;Komeil Moghaddasi ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.adhoc.2024.103656","DOIUrl":"10.1016/j.adhoc.2024.103656","url":null,"abstract":"<div><p>Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171604","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":"Cross-domain gesture recognition via WiFi signals with deep learning","authors":"Baogang Li ,&nbsp;Jiale Chen ,&nbsp;Xinlong Yu ,&nbsp;Zhi Yang ,&nbsp;Jingxi Zhang","doi":"10.1016/j.adhoc.2024.103654","DOIUrl":"10.1016/j.adhoc.2024.103654","url":null,"abstract":"<div><p>Compared with systems rely on wearable sensors, cameras or other devices, WiFi-based gesture recognition systems are convenient, non-contact and privacy-friendly, which have received widespread attention in recent years. In WiFi-based gesture recognition systems, the channel state information (CSI) carried by WiFi signals contains fine-grained information, which is commonly used to extract features of gesture activities. However, since the CSI patterns of the same gesture change across domains, these gesture recognition systems cannot effectively work without retraining in new domains, which will hinder the practical adoption of gesture recognition systems. Therefore, we propose a novel gesture recognition system that can address the issue of cross-domain recognition while achieving higher recognition accuracy for in-domain scenarios. Firstly, we employ CSI ratio and subcarrier selection to effectively eliminate noise from the CSI, and propose a method to reconstruct CSI sequence using low-frequency signals, which can effectively remove irrelevant noise in the high-frequency part and ensure the validity of the data. Next, we calculate the phase difference to explore the intrinsic features of gesture and convert the obtained data into RGB image. Finally, we use Dense Convolutional Network as backbone network, combined with dynamic convolution module, for RGB image recognition. Extensive experiments demonstrate that our proposed system can achieve 99.58% in-domain gesture recognition, and its performance across new person and orientations is 99.15% and 98.31%, respectively.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164834","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":"Vehicular edge cloud computing content caching optimization solution based on content prediction and deep reinforcement learning","authors":"Lin Zhu,&nbsp;Bingxian Li,&nbsp;Long Tan","doi":"10.1016/j.adhoc.2024.103643","DOIUrl":"10.1016/j.adhoc.2024.103643","url":null,"abstract":"<div><p>In conventional studies on vehicular edge computing, researchers frequently overlook the high-speed mobility of vehicles and the dynamic nature of the vehicular edge environment. Moreover, when employing deep reinforcement learning to address vehicular edge challenges, insufficient attention is given to the potential issue of the algorithm converging to a local optimal solution. This paper presents a content caching solution tailored for vehicular edge cloud computing, integrating content prediction and deep reinforcement learning techniques. Given the swift mobility of vehicles and the ever-changing nature of the vehicular edge environment, the study proposes a content prediction model based on Informer. Leveraging the Informer prediction model, the system anticipates the vehicular edge environment dynamics, thereby informing the caching of vehicle task content. Acknowledging the diverse time scales involved in policy decisions such as content updating, vehicle scheduling, and bandwidth allocation, the paper advocates a dual time-scale Markov modeling approach. Moreover, to address the local optimality issue inherent in the A3C algorithm, an enhanced A3C algorithm is introduced, incorporating an <span><math><mi>ɛ</mi></math></span>-greedy strategy to promote exploration. Recognizing the potential limitations posed by a fixed exploration rate <span><math><mi>ɛ</mi></math></span>, a dynamic baseline mechanism is proposed for updating <span><math><mi>ɛ</mi></math></span> dynamically. Experimental findings demonstrate that compared to alternative content caching approaches, the proposed vehicle edge computing content caching solution substantially mitigates content access costs. To support research in this area, we have publicly released the source code and pre-trained models at <span><span>https://github.com/JYAyyyyyy/Informer.git</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149092","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":"Blockchain and Quantum Machine Learning Driven Energy Trading for Electric Vehicles","authors":"Pankaj Kumar Kashyap ,&nbsp;Upasana Dohare ,&nbsp;Manoj Kumar ,&nbsp;Sushil Kumar","doi":"10.1016/j.adhoc.2024.103632","DOIUrl":"10.1016/j.adhoc.2024.103632","url":null,"abstract":"<div><p>With the steep growth of Electric Vehicles (EV's), the consequent demand of energy for charging puts significant load to powergrids. Renewable Energy Sources enabled microgrids can alleviate the problem of energy demand and trade the energy locally in Peer-to-Peer (P2P) manner, where seller (microgrid) and buyer (EV's) “meet” to trade electricity directly on agreed term without any intermediary. However, a foolproof system required for audit and verification of transaction record between seller and buyer to address privacy and security in untrusted and opaque local energy trading market (LETM). Centralized public blockchain enabled system (for audit the transaction records and storage) based on conventional learning models faces mainly two issues in the LETM. (a) if, centralize system runs out of energy and tear down then whole energy trading plunges treated as single point of failure (b) Conventional learning models fail to converge optimal point in case of large state and action space (large number of EV's and their energy demand). The primary objective of this paper to provide secure system for LETM, 1) Distributed nature of Consortium Blockchain used that solve the problem of single point of failure to audit and storage of transaction and profile info of microgrids and EV's. 2) Quantum based Reinforcement Learning (QRL) easily handles the large number of EV's energy supply and demand for smoothly run LETM. In this context, this paper presents Blockchain and Quantum Machine Learning driven energy trading model for EVs (B-MET). A utility maximization problem formulated as Markov Decision Process (MDP) and their solution provided by using QRL focusing on join optimization of selling price, loan amount and quantity of shared energy. MDP is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker, i.e., the future state depends only on the current state and action, not on the sequence of events that preceded it. QRL method combines quantum theory with traditional RL. It is inspire by the principles of state superposition and quantum parallelism. Convergence analysis and performance results attest that B-MET convergences faster, maximizes the utility with lower confirmation delay in P2P energy trading as compare to state of the art techniques.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129048","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":"THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios","authors":"Akhtar Saeed ,&nbsp;Mikail Erdem ,&nbsp;Ozgur Gurbuz ,&nbsp;Mustafa Alper Akkas","doi":"10.1016/j.adhoc.2024.103644","DOIUrl":"10.1016/j.adhoc.2024.103644","url":null,"abstract":"<div><p>For 6G non-terrestrial communications, drones will offer uninterrupted connectivity for surveillance, sensing, and localization. They will also serve as drone base stations to support terrestrial base stations, providing large bandwidth, high-rate, and ultra-reliable low latency services. In this paper, for the first time in the literature, we depict the true performance of Terahertz (THz) band communications among drones by applying various channel selection and power allocation schemes with practical THz antennas within (0.75–4.4) THz under realistic mobility and misalignment scenarios. Through numerical simulations, we unveil the capacity of drone links under different channel selection and power allocation schemes within 10s to 100s of Gbps at distances (1–100) m, when drones are in motion and subject to (mis)alignment due to mobility and even under beam misalignment fading. However, when exposed to real drone mobility traces, the performance of all channel selection schemes drops significantly, sometimes by up to six orders of magnitude, due to the occasional reverse orientations of antennas. In addition to the capacity analysis, we report available frequency bands (transmission windows) considering all schemes and mobility patterns. We also identify a band that is commonly available under all considered mobility and misalignment settings, and we evaluate its performance in terms of spectral and energy efficiencies, which can be useful in designing THz transceivers for drone communications. Our findings emphasize the essence of active beam control solutions to achieve the desired capacity potential of THz drone communications, while also highlighting the challenges of utilizing the THz band for drone communications.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164835","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":"Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network","authors":"Ansa Shermin S. ,&nbsp;Bhavya Mehta ,&nbsp;Sarang C. Dhongdi ,&nbsp;Mandar A. Chitre","doi":"10.1016/j.adhoc.2024.103641","DOIUrl":"10.1016/j.adhoc.2024.103641","url":null,"abstract":"<div><p>This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136795","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":"Towards sustainable industry 4.0: A survey on greening IoE in 6G networks","authors":"Saeed Hamood Alsamhi ,&nbsp;Ammar Hawbani ,&nbsp;Radhya Sahal ,&nbsp;Sumit Srivastava ,&nbsp;Santosh Kumar ,&nbsp;Liang Zhao ,&nbsp;Mohammed A.A. Al-qaness ,&nbsp;Jahan Hassan ,&nbsp;Mohsen Guizani ,&nbsp;Edward Curry","doi":"10.1016/j.adhoc.2024.103610","DOIUrl":"10.1016/j.adhoc.2024.103610","url":null,"abstract":"<div><p>The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyse the challenges and opportunities to make IoE greener using the discussed technologies.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S157087052400221X/pdfft?md5=4458c1f0b4d6c5d5d72267474fa6aea4&pid=1-s2.0-S157087052400221X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136107","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":"Joint differential evolution algorithm in RIS-assisted multi-UAV IoT data collection system","authors":"Yuchen Li ,&nbsp;Hongwei Ding ,&nbsp;Zhuguan Liang ,&nbsp;Bo Li ,&nbsp;Zhijun Yang","doi":"10.1016/j.adhoc.2024.103640","DOIUrl":"10.1016/j.adhoc.2024.103640","url":null,"abstract":"<div><p>This paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted multi-UAV data collection system, in which unmanned aerial vehicles (UAVs) collect data from Internet of Things (IoT) devices. The RIS, mounted on building surfaces, plays a vital role in preventing obstruction and improving the communication quality of the IoT-UAV transmission link. Our aim is to minimize the energy consumption of this system, including the transmission energy consumption of IoT devices and the hovering energy consumption of UAVs, by optimizing the deployment of UAVs and the phase shifts of RIS. To achieve this goal, a multi-UAV deployment and phase shift of RIS optimization algorithm (MUDPRA) is proposed that consists of two phases. In the first phase, a joint differential evolution (DE) algorithm with a two-layer structure featuring a variable population size, namely DEC-ADDE, is proposed to optimize the UAV deployment. Specifically, each UAV’s location is encoded as an individual, with the whole UAV deployment is considered as the population in DEC-ADDE. Thus, a differential evolution clustering (DEC) algorithm is employed initially to initialize the population, which allows for obtaining better initial UAV deployment without the need for a predefined number of UAVs. Subsequently, an adaptive and dynamic DE algorithm (ADDE) is employed to produce offspring population to further optimize UAV deployment. Finally, an adaptive updating strategy is adopted to adjust the population size to optimize the number of UAVs. In the second phase, a low-complexity method is proposed to optimize the phase shift of RIS with the aim of enhancing the IoT-UAV data transmission rate. Experimental results conducted on eight instances involving IoT devices ranging from 60 to 200 demonstrate the effectiveness of MUDPRA in minimizing energy consumption of this system compared to six alternative algorithms and three benchmark systems.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096629","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}