International Journal of Communication Systems最新文献

{"title":"Trust-Aware Routing Protocol Using Hierarchical Manta Ray Foraging Optimization Algorithm With Selfish Node Detection in MANET","authors":"Suresh Jeganathan,&nbsp;Gunasekaran Kulandaivelu,&nbsp;Dhurgadevi Muthusamy,&nbsp;Kishore Verma Samraj","doi":"10.1002/dac.70011","DOIUrl":"https://doi.org/10.1002/dac.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile ad hoc networks (MANETs) are fast, self-organizing, infrastructure-less wireless networks; it is highly suitable for use in emergencies, natural catastrophes, and between places without infrastructure. However, this approach fails to focus on the selfish nodes. To address this, a trust-aware routing protocol using the Hierarchical Manta Ray Foraging Optimization Algorithm (HMRFOA) with selfish node detection in MANET (SND-MSWGCN-TRP-MANET) is proposed in this manuscript. Initially, clustering is processed using Localized Sparse Incomplete Multiview Clustering (LSIMVC). The node is clustered by merging or combining two maximum comparable cluster nodes; then, the clustered node is assigned to the Mud Ring Optimization Algorithm (MROA), where the cluster head is selected using the parameters, such as energy (E), distance (Dist), delay (D), and quality of service (QoS). The selected cluster head is fed to the Multiscale Superpixel-Guided Weighted Graph Convolutional Network (MSWGCN) for effectively detecting the selfish node to ensure secure and efficient data routing in MANET. Finally, trust-aware routing is performed using the HMRFOA, where it selects the optimal path. Then, the proposed SND-MSWGCN-TRP-MANET is implemented, and the performance metrics like energy consumption, time consumption, end-to-end delay, throughput, average delay, packet delivery ratio (PDR), and packet loss ratio (PLR) are examined. The performance of the SND-MSWGCN-TRP-MANET approach attains 14.82%, 21.63%, and 31.57% lower energy consumption and 16.23%, 24.19%, and 31.82% higher throughput when analyzed to the existing techniques, like selfish node trust aware along optimized clustering for dependable routing protocol (SNTA-CRRP-NANET), trust-aware safe energy-efficient hybrid protocol in MANET (TASEP-MANET), and cluster with angular base energy efficient trusted routing protocol in MANET (EPTRP-MANET).</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Point-ConNet: Integrated Power Allocation and Target Assignment for Efficient Multi-Target Tracking in Distributed Radar Networks","authors":"V. Karthik,&nbsp;M. Priya,&nbsp;M. Ramkumar,&nbsp;Sathish Kumar Nagarajan","doi":"10.1002/dac.70030","DOIUrl":"https://doi.org/10.1002/dac.70030","url":null,"abstract":"<div>\u0000 \u0000 <p>The technique of using radar or other sensing devices to simultaneously monitor and track the positions of several moving objects is known as multiple target tracking or MTT. The challenge is to efficiently follow many targets in distributed radar networks by optimizing combined power allocation, resource allocation, and radar target assignment. This publication proposes an efficient deep-learning strategy for Joint Power Resource Allocation and Radar Target Assignment (JPRA-RTA) in distributed radar networks with ground-based transmitters and aerial receivers. The method has two phases: (i) Point-wise Activations Steerable Convolutional Neural Networks (Point-ConNet) with Narwhal Optimizer for joint power allocation and radar target assignment, and (ii) Hybrid Snow Geese Alpine Skiing Optimization (Hyb-SGASO), for resource allocation. First, the JPRA-RTA problem is converted into a regression problem solvable by Point-ConNet with the Narwhal Optimizer, enhancing computational efficiency, accuracy, fast convergence, and scalability. After optimizing power and radar target assignment, Hyb-SGASO is used to optimize remaining resources like communication bandwidth, ensuring balanced and efficient resource use. Thus, the proposed Point-ConNet- Hyb-SGASO method is implemented in Python and the method's performance is evaluated using metrics such as power consumption, spectral capacity, spectral efficiency, energy efficiency, tracking frame accuracy, cumulative distribution function (CDF), Mean Square Error (MSE), and Root Mean Square Error (RMSE), demonstrating significant improvements over traditional approaches. Thus, the proposed Point-Connet-Hyb-SGASO approach has achieved 18.76%, 23.04%, 28.06%, and 17.67% lower NMSE, 33.78%, 31.09%, 28.76%, and 24.89% higher energy efficiency compared with other conventional approaches like SDP-LHS-IPSOTS, ITPRS-LADMM, BCRLB-PSO, and SCA-RWO methods respectively.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Energy-Efficient Fuzzy Logic With Barnacle Mating Optimization-Based Clustering and Hybrid Optimized Cross-Layer Routing in Wireless Sensor Network","authors":"A. Renaldo Maximus,&nbsp;S. Balaji","doi":"10.1002/dac.6132","DOIUrl":"https://doi.org/10.1002/dac.6132","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent advancements in information technology have led to the widespread adoption of the Internet of Things (IoT) across various applications. Wireless sensor networks (WSNs), consisting of low-cost, compact sensors, are crucial for IoT systems, enabling data collection for tasks like surveillance and tracking. A major challenge in WSNs is achieving energy efficiency while extending network lifetime (NLT), necessitating effective clustering and routing strategies. Numerous existing methodologies for energy-efficient clustering and routing exhibit potential; however, they are hindered by constraints including inadequate adaptability to fluctuating network conditions, suboptimal selection of s cluster heads (CHs), and uneven energy consumption, resulting in diminished network longevity and efficacy. These issues require novel strategies to improve overall performance. To tackle this issues, this research presents a novel hybrid technique combining fuzzy logic with barnacles mating optimization (FL-BMO) to identify the most optimal CHs by evaluating critical criteria like average sink distance, average intracluster distance, residual energy, and CH balance factor. The FL-BMO methodology utilizes fuzzy logic to address uncertainties in sensor data, and the BMO algorithm, modeled after barnacle mating patterns, offers a resilient and adaptable optimization process, markedly enhancing energy efficiency and network longevity. In addition, an innovative natural-inspired hybrid cross-layer sunflower optimization routing (NiHCLR-SFO) technique has been introduced that entails optimal routing path selection. This approach balances exploration and exploitation during a route selection process, integrating multiple layers of the network functionality which eventually results in improved routing efficiency and network throughput. Such a hybrid approach has been implemented in MATLAB. The proposed method is compared with fuzzy reinforcement learning based data gathering (FRLDG), neuro-fuzzy-emperor penguin optimization (NF-EPO), bio-inspired cross-layer routing (BiHCLR), and fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR) protocols. From these comparisons, it was observed that the method propagates definite NLT gains reaching 39.74%, 32.92%, 15.95%, and 4.8076%, respectively. The proposed method outperforms the existing approaches (FRLDG, NF-EPO, FEEC-IIR, and BiHCLR) across several performance parameters: 99% packet delivery ratio (PDR), 2.8 ms of end-to-end delay time (E2ED), 1 Mbps of throughput, 30 mJ of energy consumption, 6000 rounds NLT, 2% bit error rate (BER), 1.25 buffer occupancy ratio, and 0.5% of packet loss ratio (PLR).</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"RSSI-Based Indoor Distance Estimation in Wi-Fi IoT Application Using AI Approaches","authors":"Sarika Mane,&nbsp;Makarand Kulkarni,&nbsp;Sudha Gupta","doi":"10.1002/dac.70008","DOIUrl":"https://doi.org/10.1002/dac.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>In many Internet of Things (IoT) applications, device tracking is required to improve localization-based services. Distance estimation is a key component of localization methods. Accurate distance estimation helps Wi-Fi access point's providers, to provide better quality of services (QoS) to connected devices. Distance estimation determines a device's precise location in an environment. In this work, three different scenarios are considered. The work proposes distance estimation using Euclidean distance and the received signal strength indicator (RSSI). This approach eliminates the need for three access points required in the triangulation method. Euclidean distance is measured between access points and devices spread throughout the indoor environment. Random forest (RF) algorithm, K-nearest neighbor (KNN), Gaussian process (GP), and ant colony optimization (ACO) are used to train the models to correlate Euclidian distances and the RSSI of connected devices. The performance is measured using mean absolute error (MAE). The optimum performance improvement obtained in Scenario 1, for RF, is 41.87%; in Scenario 2, for KNN, it is 37.98%; and in Scenario 3, for RF, it is 56.97%, with respect to earlier reported work. ACO achieves a 78.61% improvement in Scenario 1, 88.57% in Scenario 2, and 50.90% in Scenario 3 over the reported work.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Cooperative Resource Allocation Using Optimized Heterogeneous Context-Aware Graph Convolutional Networks in 5G Wireless Networks","authors":"Rakesh Kumar Godi,&nbsp;Soumyashree M. Panchal,&nbsp;Swathi Agarwal","doi":"10.1002/dac.70002","DOIUrl":"https://doi.org/10.1002/dac.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless personal communication is becoming more and more popular due to the rapid development of 5G communication networks. Modern wireless personal communication systems can be difficult to optimize due to the criteria for transmission speed and quality of service. In this manuscript, a cooperative resource allocation using optimized heterogeneous context-aware graph convolutional networks in 5G wireless networks (CRA-HCAGCN-5GWN) is proposed. Here, the cooperative resource allocation is used for channel information on a small scale rather than typical resource allocation when the channel environment is rapidly changing. HCAGCN fails to specify optimization techniques to identify optimal parameters for accurate cooperative resource allocation. Therefore, the Giant Trevally Optimizer (GTO) is employed to optimize the HCAGCN, which accurately optimizes resource allocation. The proposed CRA-HCAGCN-5GWN is implemented, and the performance metrics, like mean square error (MSE), minimum mean square error (MMSE), mean absolute error (MAE), root mean square error (RMSE), throughput, energy efficiency, and consumption time, are analyzed. The performance of the CRA-HCAGCN-5GWN approach attains 17.20%, 25.81%, and 32.18% lower mean square error; 16.40%, 28.81%, and 30.18% higher throughput; and 18.30%, 25.41%, and 31.08% lower energy efficiency when analyzed with existing methods.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Algorithmic Analysis of Cognitive Multichannel Retrial Network System With Finite-Source Primary Users and Admission Control for Secondary Users","authors":"B. Krishna Kumar,&nbsp;R. Sankar,&nbsp;R. Navaneetha Krishnan,&nbsp;R. Rukmani,&nbsp;S. Balaji","doi":"10.1002/dac.70006","DOIUrl":"https://doi.org/10.1002/dac.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>This research article explores a novel category of multichannel cognitive radio (CR) wireless retrial queueing network. The channels (radio spectrums) are shared between secondary users (SUs) and primary users (PUs), with PUs enjoying preemptive priority. The PUs are originated from a finite number, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>, of identical sources. In addition to PUs holding absolute priority over SUs, the impact of repeated attempts of PUs has been implemented. To optimize radio spectrum utilization and improve transmission quality, a waiting line buffer for SUs is deployed. For this intricate system, the ergodic condition and joint stationary probability distribution of SUs and PUs are derived using matrix-geometric methods. Various essential performance metrics of the system have been evaluated. Additionally, vital probabilistic descriptors, including successful, vain, and ideal retrials, are thoroughly examined in the context of the CR wireless network. By exploring the first-step principle, the first-passage time to attain a specific critical level in the waiting line buffer of SUs and associated average measures are discussed. Finally, comprehensive numerical results and graphical analyses are presented to provide insights into the proposed scheme.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Impact of Metaheuristic Optimization Algorithms on Wireless Network Coverage Enhancement With Reconfigurable Intelligent Surfaces","authors":"Nitin Panuganti,&nbsp;Pinku Ranjan,&nbsp;Anupam Shukla","doi":"10.1002/dac.70026","DOIUrl":"https://doi.org/10.1002/dac.70026","url":null,"abstract":"<div>\u0000 \u0000 <p>Reconfigurable intelligent surfaces are a transformative technological advancement in wireless networks toward enhancing coverage and signal strength by exploiting intelligent, programmable surfaces. This paper discusses the application of metaheuristic optimization to configure reconfigurable intelligent surfaces for performance enhancement in wireless communications. Reconfigurable intelligent surface (RIS) technology based on programmable surfaces dynamically adjusts a radio environment, thereby having an impact on signal quality enhancement and coverage. We test several optimization algorithms, including genetic algorithm (GA), cuckoo search (CS), harmony search (HS), ant colony optimization (ACO), differential evolution (DE), dual annealing, gradient descent, tabu search, and the bee algorithm. The results show that GA explores the solution space. CS and HS balance well between exploration and exploitation. ACO and DE find paths and parameters efficiently, and dual annealing excels in escaping local optima. Tabu search and the bee algorithm also provide strong performance in the avoidance of local minima. RIS elements can integrate highly into the network to enhance its performance, leading to better-received signal strength and improved coverage. The results have the potential to optimize wireless networks by combining the use of RIS technology with advanced algorithms such as those described here and call for further research in hybrid optimization approaches and complex network scenarios.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Joint Design of Fronthaul and Access Links in Massive MIMO Based CRANs","authors":"Umar Rashid,&nbsp;Faheem G. Awan","doi":"10.1002/dac.70012","DOIUrl":"https://doi.org/10.1002/dac.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we address the challenge of limited channel capacity of wireless fronthaul links in cloud radio access networks (CRANs). A novel architecture is proposed in which the base band unit (BBU) is equipped with a massive multiple-input multiple-output (MIMO) antenna array to communicate with a set of remote radio heads (RRHs) that serve multiple user equipments (UEs). Moreover, an optimal signal quantization strategy is also designed at the BBU to accommodate for the limited fronthaul capacity. To this end, a joint optimization of power allocation at the BBU, quantization noise covariance, and RRH beamforming vectors is formulated via maximizing the achievable sum-rate subject to power constraints at the BBU and RRHs. The resulting highly nonconvex problem is reformulated as a semidefinite relaxation (SDR) problem by using the difference of convex (DC) programming approach. It is also deduced analytically that the solution yielded by this SDR problem is always of rank one. Numerical results confirm the potential of the proposed system in improving the capacity of the CRAN systems with wireless fronthauling. In particular, the proposed design outperforms two proposed benchmark schemes.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Scarecrow-Shaped Antenna Optimization Using Machine Learning Algorithms","authors":"S. Bhavani,&nbsp;B. Raviteja,&nbsp;T. Shanmuganantham","doi":"10.1002/dac.70028","DOIUrl":"https://doi.org/10.1002/dac.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, scarecrow-shaped antenna with a Rogers RT6002 substrate with a permittivity of 2.94 and a thickness of 1 mm is presented. It is operating from 3.5 to 12 GHz frequency band. The next generation of wireless communication networks will make extensive use of machine learning (ML). It is anticipated that the growth of various communication-based applications will improve coverage and spectrum efficiency when compared with traditional systems. A wide range of domains, including antennas, can benefit from the application of ML to generate solutions. Scarecrow-shaped antenna is optimized using machine learning algorithms decision tree, random forest, XGBoost regression, K-nearest neighbor (KNN), and light gradient boosting regression (LGBR). The antenna's return loss, gain, and directivity were predicted in this work. The KNN achieved the highest accuracy in the prediction of return loss. Hence, proposed antenna is suitable for flexible wireless communication systems, IoT, 5G, and 6G.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Optimizing FECAF in MTSFM Waveforms Using Conjugate Gradient Descent With Efficient Line Search for Radar and Sonar Applications","authors":"G. Ravi Shankar Reddy,&nbsp;J. Pandu,&nbsp;C. H. Ashok Babu","doi":"10.1002/dac.70027","DOIUrl":"https://doi.org/10.1002/dac.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>Optimizing waveforms in radar and sonar systems is crucial for enhancing spectral efficiency and target detection capabilities, yet this process often faces challenges like computational complexity and convergence issues. This research introduces a novel method that leverages the frequency-domain exponential cross ambiguity function (FECAF) within multi-tone sinusoidal frequency modulated (MTSFM) waveforms, combined with the Conjugate Gradient Method with Efficient Line Search (CGM-ELS) for optimization. The optimization of MTSFM waveforms is achieved by combining the generalized integrated sidelobe level (GISL) and peak-to-mean power envelope ratio (PMEPR) metrics. The GISL metric controls the main lobe and sidelobe structure of the waveform's autocorrelation function (ACF), quantifying unwanted energy in sidelobes to find an optimal compromise. PMEPR ensures efficient operation of radar or sonar transmitters by minimizing energy variations in the waveform's envelope, which is crucial for peak power-limited systems. To optimize these metrics, the CGM-ELS algorithm is employed, ensuring efficient convergence through iterative adjustment of waveform parameters based on gradient information and penalty functions. The proposed method transforms the optimization problem into an unconstrained format, reducing computational complexity and improving convergence rates. Experimental results have shown significant enhancements in computational efficiency and convergence rate, demonstrating the effectiveness of the CGM-ELS algorithm in synthesizing waveforms with optimal ambiguity function characteristics for radar and sonar applications.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}