Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.
{"title":"Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches","authors":"Beenish Habib, F. Khursheed","doi":"10.1002/cpe.7996","DOIUrl":"https://doi.org/10.1002/cpe.7996","url":null,"abstract":"Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"42 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pengpeng Long, Yuhang Wu, Quan Chen, Lianglun Cheng
Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm.
{"title":"Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks","authors":"Pengpeng Long, Yuhang Wu, Quan Chen, Lianglun Cheng","doi":"10.1002/cpe.8044","DOIUrl":"https://doi.org/10.1002/cpe.8044","url":null,"abstract":"Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"367 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.
{"title":"Open‐domain event schema induction via weighted attentive hypergraph neural network","authors":"Wei Qin, Haozhe Jasper Wang, Xiangfeng Luo","doi":"10.1002/cpe.8029","DOIUrl":"https://doi.org/10.1002/cpe.8029","url":null,"abstract":"Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"81 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study shows how GPU performance of the ADER discontinuous Galerkin method in SeisSol (an earthquake simulation software) can be further improved while preserving its original design that ensures high CPU performance. We introduce a new code generator (“ChainForge”) that fuses subsequent batched matrix multiplications (“GEMMs”) into a single GPU kernel, holding intermediate results in shared memory as long as necessary. The generator operates as an external module linked against SeisSol's domain specific language YATeTo and, as a result, the original SeisSol source code remains mainly unchanged. In this paper, we discuss several challenges related to automatic fusion of GPU kernels and provide solutions to them. By and large, we gain 60% in performance of SeisSol's wave propagation solver using Fused‐GEMMs compared to the original GPU implementation. We demonstrated this on benchmarks as well as on a real production scenario simulating the Northridge 1994 earthquake.
{"title":"Fused GEMMs towards an efficient GPU implementation of the ADER‐DG method in SeisSol","authors":"Ravil Dorozhinskii, G. B. Gadeschi, Michael Bader","doi":"10.1002/cpe.8037","DOIUrl":"https://doi.org/10.1002/cpe.8037","url":null,"abstract":"This study shows how GPU performance of the ADER discontinuous Galerkin method in SeisSol (an earthquake simulation software) can be further improved while preserving its original design that ensures high CPU performance. We introduce a new code generator (“ChainForge”) that fuses subsequent batched matrix multiplications (“GEMMs”) into a single GPU kernel, holding intermediate results in shared memory as long as necessary. The generator operates as an external module linked against SeisSol's domain specific language YATeTo and, as a result, the original SeisSol source code remains mainly unchanged. In this paper, we discuss several challenges related to automatic fusion of GPU kernels and provide solutions to them. By and large, we gain 60% in performance of SeisSol's wave propagation solver using Fused‐GEMMs compared to the original GPU implementation. We demonstrated this on benchmarks as well as on a real production scenario simulating the Northridge 1994 earthquake.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"39 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Simulation method for infrared radiation transmission characteristics of typical ship targets based on optical remote sensing","authors":"Zheng Jiang, Ming Xu, Hao Shi, Liang Chen","doi":"10.1002/cpe.7515","DOIUrl":"https://doi.org/10.1002/cpe.7515","url":null,"abstract":"","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73538457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soumia Chokri, Sohaib Baroud, Safa Belhaous, M. Youssfi, M. Mestari
{"title":"Performance prediction of parallel applications using artificial neuronal network and graph representation","authors":"Soumia Chokri, Sohaib Baroud, Safa Belhaous, M. Youssfi, M. Mestari","doi":"10.1002/cpe.7514","DOIUrl":"https://doi.org/10.1002/cpe.7514","url":null,"abstract":"","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76041648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-07DOI: 10.1007/s10586-022-03734-9
A. Mirzaei
{"title":"A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization","authors":"A. Mirzaei","doi":"10.1007/s10586-022-03734-9","DOIUrl":"https://doi.org/10.1007/s10586-022-03734-9","url":null,"abstract":"","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87302497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A highly challenging aspect of the data compression technique is maintaining the quality of data that reconstructs in high compression rates. To overcome these limitations, a bilateral anisotropic Gabor wavelet transformation with deep stacked auto encoding (BAGWT‐DSAE) technique based lossesless image compression is proposed in this article to save the storage space and processing time during transferring the images. The proposed method contains three main processes namely preprocessing, compression and decompression. Initially input aerial image and digital image are taken and these images are given bilateral filter based preprocessing for eliminates the different types of noises and also multiple artifacts. Then the preprocessed images are given to anisotropic Gabor wavelet transformation based deep stacked auto encoding to compress and decompress the wavelet transform's sensitive sub‐bands effectually. In DSAE, the decoder of the auto encoder achieves a better quality decompressed image. The proposed method is implemented in MATLAB simulations run in PC through Intel Core, 8 GB of RAM, 2.50 GHz CPU and Windows 8. Then, the simulation performance of proposed BAGWT‐DSAE‐LIC method provides 20.23%, 24.85%, and 38.56% low compression ratio and 26.48%, 21.23%, and 12.53% lower computational time, 4.56%, 7.68%, and 8.34% high space saving than the existing methods.
{"title":"Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression","authors":"S. Kumar, R. Sarankumar, O. Vignesh, A. Prakash","doi":"10.1002/cpe.7383","DOIUrl":"https://doi.org/10.1002/cpe.7383","url":null,"abstract":"A highly challenging aspect of the data compression technique is maintaining the quality of data that reconstructs in high compression rates. To overcome these limitations, a bilateral anisotropic Gabor wavelet transformation with deep stacked auto encoding (BAGWT‐DSAE) technique based lossesless image compression is proposed in this article to save the storage space and processing time during transferring the images. The proposed method contains three main processes namely preprocessing, compression and decompression. Initially input aerial image and digital image are taken and these images are given bilateral filter based preprocessing for eliminates the different types of noises and also multiple artifacts. Then the preprocessed images are given to anisotropic Gabor wavelet transformation based deep stacked auto encoding to compress and decompress the wavelet transform's sensitive sub‐bands effectually. In DSAE, the decoder of the auto encoder achieves a better quality decompressed image. The proposed method is implemented in MATLAB simulations run in PC through Intel Core, 8 GB of RAM, 2.50 GHz CPU and Windows 8. Then, the simulation performance of proposed BAGWT‐DSAE‐LIC method provides 20.23%, 24.85%, and 38.56% low compression ratio and 26.48%, 21.23%, and 12.53% lower computational time, 4.56%, 7.68%, and 8.34% high space saving than the existing methods.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87793529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a new cybernetic control technology that can be used in complex software systems will be introduced. In this approach, the software systems are governed by cybernetic control objects and the class libraries defining the types of these cybernetic control objects are produced by special meta‐programming platforms. In this approach, the requirements of the software to be developed are received from the programmer by meta‐programming systems before coding. Actually, the cybernetic control objects have standard design and properties and the programmers only determine the quantities and the locations of these properties before library production process. Then, the meta‐programming platforms build project‐specific class libraries based on previously determined code templates. By this way, the cybernetic control objects are constructed with optimal memory and they can receive feedback about ongoing operations on the process. With the help of the feedback coming from the process, the control objects steer the process in the line of the programmer directives. By this way, the control of the programmer on the software increases significantly. In addition, in this paper, a typical application of this approach to the multithread programming will be introduced.
{"title":"The usage of cybernetic in complex software systems and its application to the deterministic multithreading","authors":"E. M. Bozkurt","doi":"10.1002/cpe.7375","DOIUrl":"https://doi.org/10.1002/cpe.7375","url":null,"abstract":"In this paper, a new cybernetic control technology that can be used in complex software systems will be introduced. In this approach, the software systems are governed by cybernetic control objects and the class libraries defining the types of these cybernetic control objects are produced by special meta‐programming platforms. In this approach, the requirements of the software to be developed are received from the programmer by meta‐programming systems before coding. Actually, the cybernetic control objects have standard design and properties and the programmers only determine the quantities and the locations of these properties before library production process. Then, the meta‐programming platforms build project‐specific class libraries based on previously determined code templates. By this way, the cybernetic control objects are constructed with optimal memory and they can receive feedback about ongoing operations on the process. With the help of the feedback coming from the process, the control objects steer the process in the line of the programmer directives. By this way, the control of the programmer on the software increases significantly. In addition, in this paper, a typical application of this approach to the multithread programming will be introduced.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80212365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ying-shun Wang, Yulan Ma, Yan Qiang, Juanjuan Zhao, Yi Li, Keqin Li
Safety is an important issue in the mining industry and the Internet of Things (IoT) plays an important role to enhance the safety of the underground working environment. The IoT is used to transfer data generated by underground sensors to cloud storage for further processing. However, third‐party platforms are often a target for cyber attacks. Serious mining accidents might occur if the data were tampered with. In the overground scenario, the security of trading data is also an important issue. The Mine Consortium Blockchain (MCB) is proposed to solve the above problems. The MCB avoids the risk of centralized storage and enables data security, provenance and transparency by taking advantage of blockchain technology. The MCB platform ensures that only designated participants can process mineral data. Any violation is immutably recorded in the MCB and is easily traced back by other participants. Classical consensus mechanisms as the core technology of the blockchain cannot be directly and appropriately applied to the mining industry. A Block Alliance Consensus (BAC) mechanism, which is suitable for all consortium blockchain scenarios, is proposed to improve the performance of the MCB. In addition, the block structure of the underground sensor data is optimized: the blocks only contain a hash of the sensor data and the data being stored in the cloud. The efficiency of the BAC is demonstrated by simulation experiments where the performance of the BAC consensus mechanism is compared with with the performance of classical consensus mechanisms. The MCB and the BAC consensus mechanism were also implemented on Hyperledger Fabric. Finally the Hyperledger Caliper evaluation tool was used to evaluate the performance of the system.
{"title":"BAC: A block alliance consensus mechanism for the mine consortium blockchain","authors":"Ying-shun Wang, Yulan Ma, Yan Qiang, Juanjuan Zhao, Yi Li, Keqin Li","doi":"10.1002/cpe.7344","DOIUrl":"https://doi.org/10.1002/cpe.7344","url":null,"abstract":"Safety is an important issue in the mining industry and the Internet of Things (IoT) plays an important role to enhance the safety of the underground working environment. The IoT is used to transfer data generated by underground sensors to cloud storage for further processing. However, third‐party platforms are often a target for cyber attacks. Serious mining accidents might occur if the data were tampered with. In the overground scenario, the security of trading data is also an important issue. The Mine Consortium Blockchain (MCB) is proposed to solve the above problems. The MCB avoids the risk of centralized storage and enables data security, provenance and transparency by taking advantage of blockchain technology. The MCB platform ensures that only designated participants can process mineral data. Any violation is immutably recorded in the MCB and is easily traced back by other participants. Classical consensus mechanisms as the core technology of the blockchain cannot be directly and appropriately applied to the mining industry. A Block Alliance Consensus (BAC) mechanism, which is suitable for all consortium blockchain scenarios, is proposed to improve the performance of the MCB. In addition, the block structure of the underground sensor data is optimized: the blocks only contain a hash of the sensor data and the data being stored in the cloud. The efficiency of the BAC is demonstrated by simulation experiments where the performance of the BAC consensus mechanism is compared with with the performance of classical consensus mechanisms. The MCB and the BAC consensus mechanism were also implemented on Hyperledger Fabric. Finally the Hyperledger Caliper evaluation tool was used to evaluate the performance of the system.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85859043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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