Y. Zeng, Xin Wang, Junfeng Yuan, Jilin Zhang, Jian Wan
Federated learning is a new framework of machine learning, it trains models locally on multiple clients and then uploads local models to the server for model aggregation iteratively until the model converges. In most cases, the local epochs of all clients are set to the same value in federated learning. In practice, the clients are usually heterogeneous, which leads to the inconsistent training speed of clients. The faster clients will remain idle for a long time to wait for the slower clients, which prolongs the model training time. As the time cost of clients’ local training can reflect the clients’ training speed, and it can be used to guide the dynamic setting of local epochs, we propose a method based on deep learning to predict the training time of models on heterogeneous clients. First, a neural network is designed to extract the influence of different model features on training time. Second, we propose a dimensionality reduction rule to extract the key features which have a great impact on training time based on the influence of model features. Finally, we use the key features extracted by the dimensionality reduction rule to train the time prediction model. Our experiments show that, compared with the current prediction method, our method reduces 30% of model features and 25% of training data for the convolutional layer, 20% of model features and 20% of training data for the dense layer, while maintaining the same level of prediction error.
{"title":"Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning","authors":"Y. Zeng, Xin Wang, Junfeng Yuan, Jilin Zhang, Jian Wan","doi":"10.1155/2022/6887040","DOIUrl":"https://doi.org/10.1155/2022/6887040","url":null,"abstract":"Federated learning is a new framework of machine learning, it trains models locally on multiple clients and then uploads local models to the server for model aggregation iteratively until the model converges. In most cases, the local epochs of all clients are set to the same value in federated learning. In practice, the clients are usually heterogeneous, which leads to the inconsistent training speed of clients. The faster clients will remain idle for a long time to wait for the slower clients, which prolongs the model training time. As the time cost of clients’ local training can reflect the clients’ training speed, and it can be used to guide the dynamic setting of local epochs, we propose a method based on deep learning to predict the training time of models on heterogeneous clients. First, a neural network is designed to extract the influence of different model features on training time. Second, we propose a dimensionality reduction rule to extract the key features which have a great impact on training time based on the influence of model features. Finally, we use the key features extracted by the dimensionality reduction rule to train the time prediction model. Our experiments show that, compared with the current prediction method, our method reduces 30% of model features and 25% of training data for the convolutional layer, 20% of model features and 20% of training data for the dense layer, while maintaining the same level of prediction error.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"9 1","pages":"6887040:1-6887040:15"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78261746","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 general, web 2.0 technology serves as an educational tool for teaching and learning aspects. The study is aimed at exploring the interactive system of football teaching in the information technology era. The coach and players will utilize the mobile learning resources to get effective learning about the fun. Using mobile learning technology, the coach has to implement different modes to make the players learn about the game. The study implemented the convolutional neural network (CNN) algorithm to evaluate the accuracy of using web 2.0 technology to cooperative learning environment system design of football teaching. The results show that the network teaching interactive learning system of football courses based on web 2.0 can achieve the intended function of the college educational administration management system.
{"title":"Application of Web 2.0 Technology to Cooperative Learning Environment System Design of Football Teaching","authors":"Hui Lin","doi":"10.1155/2022/5132618","DOIUrl":"https://doi.org/10.1155/2022/5132618","url":null,"abstract":"In general, web 2.0 technology serves as an educational tool for teaching and learning aspects. The study is aimed at exploring the interactive system of football teaching in the information technology era. The coach and players will utilize the mobile learning resources to get effective learning about the fun. Using mobile learning technology, the coach has to implement different modes to make the players learn about the game. The study implemented the convolutional neural network (CNN) algorithm to evaluate the accuracy of using web 2.0 technology to cooperative learning environment system design of football teaching. The results show that the network teaching interactive learning system of football courses based on web 2.0 can achieve the intended function of the college educational administration management system.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"133 1","pages":"5132618:1-5132618:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87038118","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}
The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and F value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.
{"title":"Question Text Classification Method of Tourism Based on Deep Learning Model","authors":"Wanli Luo, Lei Zhang","doi":"10.1155/2022/4330701","DOIUrl":"https://doi.org/10.1155/2022/4330701","url":null,"abstract":"The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and \u0000 \u0000 F\u0000 \u0000 value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"64 1","pages":"4330701:1-4330701:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77935968","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}
Tao He, Kunxin Zhu, Zhipeng Chen, Ruomei Wang, Fan Zhou
Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.
{"title":"Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing","authors":"Tao He, Kunxin Zhu, Zhipeng Chen, Ruomei Wang, Fan Zhou","doi":"10.1155/2022/5562995","DOIUrl":"https://doi.org/10.1155/2022/5562995","url":null,"abstract":"Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"54 1","pages":"5562995:1-5562995:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82311370","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}
Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction in visual communication are decomposed, and the 3D image visual communication model is constructed. Based on the calculation of the difference of 3D image texture feature points, the spatial position relationship of 3D image feature points after virtual reconstruction is calculated to complete the texture mapping of 3D image. The deep texture feature points of 3D image are extracted. According to the description coefficient of 3D image virtual reconstruction in visual communication, the virtual reconstruction results of 3D image are constrained. The virtual reconstruction algorithm of 3D image is designed to realize the virtual reconstruction of 3D image. The results show that when the number of samples is 200, the virtual reconstruction time of this paper method is 2.1 s, and the system running time is 5 s; the SNR of the virtual reconstruction is 35.5 db. The MSE of 3D image virtual reconstruction is 3%, and the SSIM of virtual reconstruction is 1.38%, which shows that this paper method can effectively improve the ability of 3D image virtual reconstruction.
{"title":"The Effect of 3D Image Virtual Reconstruction Based on Visual Communication","authors":"Li Xu, Ling Bai, Lei Li","doi":"10.1155/2022/6404493","DOIUrl":"https://doi.org/10.1155/2022/6404493","url":null,"abstract":"Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction in visual communication are decomposed, and the 3D image visual communication model is constructed. Based on the calculation of the difference of 3D image texture feature points, the spatial position relationship of 3D image feature points after virtual reconstruction is calculated to complete the texture mapping of 3D image. The deep texture feature points of 3D image are extracted. According to the description coefficient of 3D image virtual reconstruction in visual communication, the virtual reconstruction results of 3D image are constrained. The virtual reconstruction algorithm of 3D image is designed to realize the virtual reconstruction of 3D image. The results show that when the number of samples is 200, the virtual reconstruction time of this paper method is 2.1 s, and the system running time is 5 s; the SNR of the virtual reconstruction is 35.5 db. The MSE of 3D image virtual reconstruction is 3%, and the SSIM of virtual reconstruction is 1.38%, which shows that this paper method can effectively improve the ability of 3D image virtual reconstruction.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"49 1","pages":"6404493:1-6404493:8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87438105","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}
To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.
{"title":"Smart Financial Management System Based on Data Ming and Man-Machine Management","authors":"Maotao Lai","doi":"10.1155/2022/2717982","DOIUrl":"https://doi.org/10.1155/2022/2717982","url":null,"abstract":"To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"46 1","pages":"2717982:1-2717982:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89781168","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}
Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.
{"title":"A Multipath Payment Scheme Supporting Proof of Payment","authors":"Hangguan Qian, Lin You","doi":"10.1155/2022/9911915","DOIUrl":"https://doi.org/10.1155/2022/9911915","url":null,"abstract":"Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"38 1","pages":"9911915:1-9911915:7"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86558503","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}
S. Melo, Felipe Oliveira, C. A. Silva, Paulo Lopes, Gibeon S. Aquino
IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.
{"title":"OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing","authors":"S. Melo, Felipe Oliveira, C. A. Silva, Paulo Lopes, Gibeon S. Aquino","doi":"10.1155/2022/5331712","DOIUrl":"https://doi.org/10.1155/2022/5331712","url":null,"abstract":"IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"12 1","pages":"5331712:1-5331712:15"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87890318","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}
The emergence of wireless sensor networks connects the physical world with the information world and changes the way humans interact with nature. With the rapid development of modern information technology, accounting information systems (AIS) have emerged at the historic moment. Under the information environment, accounting data exists in paper or paperless form. The use of information technology not only brings convenient and efficient services to enterprises but also has a huge impact on the internal control of the enterprise. Because the network is open and unstable, the system is vulnerable to illegal intrusion and viruses. Based on the above background, the research content of this article is to use DES algorithm to encrypt accounting data. DES (Data Encryption Standard) encryption algorithm is a symmetric password encryption method. It has the advantages of fast encryption speed, simple and practical algorithm, and consideration of both security and efficiency requirements. This paper discusses the application of DES encryption technology to accounting data processing. To achieve data security management goals. Therefore, this paper proposes a DES algorithm based on the logistic chaotic system. Through experimental simulation, the results show that the chaotic discrete model has initial value sensitivity and iterative nonrepetition. The resulting key space is independent and random. In the application, you can perform random key input according to the performance of software and hardware, which is flexible; there is only one “1186828” in the initial DES algorithm encryption process, but each set of plain text in the improved DES algorithm corresponds to a corresponding set of keys and independence. The test results show that they are maintained between 5 and 6.6. It is proved that using the initial value sensitivity of the logistic system and using the initial value as the key can realize the secure management of accounting data on the premise of ensuring efficiency.
{"title":"Encryption Management of Accounting Data Based on DES Algorithm of Wireless Sensor Network","authors":"Zixin Lu","doi":"10.1155/2022/7203237","DOIUrl":"https://doi.org/10.1155/2022/7203237","url":null,"abstract":"The emergence of wireless sensor networks connects the physical world with the information world and changes the way humans interact with nature. With the rapid development of modern information technology, accounting information systems (AIS) have emerged at the historic moment. Under the information environment, accounting data exists in paper or paperless form. The use of information technology not only brings convenient and efficient services to enterprises but also has a huge impact on the internal control of the enterprise. Because the network is open and unstable, the system is vulnerable to illegal intrusion and viruses. Based on the above background, the research content of this article is to use DES algorithm to encrypt accounting data. DES (Data Encryption Standard) encryption algorithm is a symmetric password encryption method. It has the advantages of fast encryption speed, simple and practical algorithm, and consideration of both security and efficiency requirements. This paper discusses the application of DES encryption technology to accounting data processing. To achieve data security management goals. Therefore, this paper proposes a DES algorithm based on the logistic chaotic system. Through experimental simulation, the results show that the chaotic discrete model has initial value sensitivity and iterative nonrepetition. The resulting key space is independent and random. In the application, you can perform random key input according to the performance of software and hardware, which is flexible; there is only one “1186828” in the initial DES algorithm encryption process, but each set of plain text in the improved DES algorithm corresponds to a corresponding set of keys and independence. The test results show that they are maintained between 5 and 6.6. It is proved that using the initial value sensitivity of the logistic system and using the initial value as the key can realize the secure management of accounting data on the premise of ensuring efficiency.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"108 2 1","pages":"7203237:1-7203237:14"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78015228","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}
Jia Liu, Wei Chen, Ziyang Chen, Lin Liu, Yuhong Wu, Kai Liu, Amar Jain, Yasser H. Elawady
Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.
{"title":"Optimized Query Algorithms for Top- K Group Skyline","authors":"Jia Liu, Wei Chen, Ziyang Chen, Lin Liu, Yuhong Wu, Kai Liu, Amar Jain, Yasser H. Elawady","doi":"10.1155/2022/3404906","DOIUrl":"https://doi.org/10.1155/2022/3404906","url":null,"abstract":"Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top-\u0000 \u0000 k\u0000 \u0000 group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"33 1","pages":"3404906:1-3404906:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80849569","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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