{"title":"AI-enabled learning techniques for Internet of Things communications","authors":"A. Souri, Mu Chen","doi":"10.3233/JHS-210660","DOIUrl":"https://doi.org/10.3233/JHS-210660","url":null,"abstract":"","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"60 1","pages":"203-204"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75262640","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}
Multi-party computation (MPC) sorting and searching protocols are frequently used in different databases with varied applications, as in cooperative intrusion detection systems, private computation of set intersection and oblivious RAM. Ivan Damgard et al. have proposed two techniques i.e., bit-decomposition protocol and bit-wise less than protocol for MPC. These two protocols are used as building blocks and have proposed two oblivious MPC protocols. The proposed protocols are based on data-dependent algorithms such as insertion sort and binary search. The proposed multi-party sorting protocol takes the shares of the elements as input and outputs the shares of the elements in sorted order. The proposed protocol exhibits O ( 1 ) constant round complexity and O ( n log n ) communication complexity. The proposed multi-party binary search protocol takes two inputs. One is the shares of the elements in sorted order and the other one is the shares of the element to be searched. If the position of the search element exists, the protocol returns the corresponding shares, otherwise it returns shares of zero. The proposed multi-party binary search protocol exhibits O ( 1 ) round complexity and O ( n log n ) communication complexity. The proposed multi-party sorting protocol works better than the existing quicksort protocol when the input is in almost sorted order. The proposed multi-party searching protocol gives almost the same results, when compared to the general binary search algorithm.
{"title":"Oblivious stable sorting protocol and oblivious binary search protocol for secure multi-party computation","authors":"C. H. K. Rao, K. Singh, Anoop Kumar","doi":"10.3233/JHS-210652","DOIUrl":"https://doi.org/10.3233/JHS-210652","url":null,"abstract":"Multi-party computation (MPC) sorting and searching protocols are frequently used in different databases with varied applications, as in cooperative intrusion detection systems, private computation of set intersection and oblivious RAM. Ivan Damgard et al. have proposed two techniques i.e., bit-decomposition protocol and bit-wise less than protocol for MPC. These two protocols are used as building blocks and have proposed two oblivious MPC protocols. The proposed protocols are based on data-dependent algorithms such as insertion sort and binary search. The proposed multi-party sorting protocol takes the shares of the elements as input and outputs the shares of the elements in sorted order. The proposed protocol exhibits O ( 1 ) constant round complexity and O ( n log n ) communication complexity. The proposed multi-party binary search protocol takes two inputs. One is the shares of the elements in sorted order and the other one is the shares of the element to be searched. If the position of the search element exists, the protocol returns the corresponding shares, otherwise it returns shares of zero. The proposed multi-party binary search protocol exhibits O ( 1 ) round complexity and O ( n log n ) communication complexity. The proposed multi-party sorting protocol works better than the existing quicksort protocol when the input is in almost sorted order. The proposed multi-party searching protocol gives almost the same results, when compared to the general binary search algorithm.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"519 ","pages":"67-82"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JHS-210652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72435728","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}
From the recent study, it is observed that even though cloud computing grants the greatest performance in the case of storage, computing, and networking services, the Internet of Things (IoT) still suffers from high processing latency, awareness of location, and least mobility support. To address these issues, this paper integrates fog computing and Software-Defined Networking (SDN). Importantly, fog computing does the extension of computing and storing to the network edge that could minimize the latency along with mobility support. Further, this paper aims to incorporate a new optimization strategy to address the “Load balancing” problem in terms of latency minimization. A new Thresholded-Whale Optimization Algorithm (T-WOA) is introduced for the optimal selection of load distribution coefficient (time allocation for doing a task). Finally, the performance of the proposed model is compared with other conventional models concerning latency. The simulation results prove that the SDN based T-WOA algorithm could efficiently minimize the latency and improve the Quality of Service (QoS) in Software Defined Cloud/Fog architecture.
{"title":"Load balancing strategy in software defined network by improved whale optimization algorithm","authors":"S. Darade, M. Akkalakshmi","doi":"10.3233/JHS-210657","DOIUrl":"https://doi.org/10.3233/JHS-210657","url":null,"abstract":"From the recent study, it is observed that even though cloud computing grants the greatest performance in the case of storage, computing, and networking services, the Internet of Things (IoT) still suffers from high processing latency, awareness of location, and least mobility support. To address these issues, this paper integrates fog computing and Software-Defined Networking (SDN). Importantly, fog computing does the extension of computing and storing to the network edge that could minimize the latency along with mobility support. Further, this paper aims to incorporate a new optimization strategy to address the “Load balancing” problem in terms of latency minimization. A new Thresholded-Whale Optimization Algorithm (T-WOA) is introduced for the optimal selection of load distribution coefficient (time allocation for doing a task). Finally, the performance of the proposed model is compared with other conventional models concerning latency. The simulation results prove that the SDN based T-WOA algorithm could efficiently minimize the latency and improve the Quality of Service (QoS) in Software Defined Cloud/Fog architecture.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"20 1","pages":"151-167"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80821817","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}
Fuel prices, which are of broad concern to the general public, are always seen as a challenging research topic. This paper proposes a variational Bayesian structural time-series model (STM) to effectively process complex fuel sales data online and provide real-time forecasting of fuel sales. While a traditional STM normally uses a probability model and the Markov chain Monte Carlo (MCMC) method to process change points, using the MCMC method to train the online model can be difficult given a relatively heavy computing load and time consumption. We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.
{"title":"A fuel sales forecast method based on variational Bayesian structural time series","authors":"Huiqiang Lian, Bing Liu, Pengyuan Li","doi":"10.3233/JHS-210651","DOIUrl":"https://doi.org/10.3233/JHS-210651","url":null,"abstract":"Fuel prices, which are of broad concern to the general public, are always seen as a challenging research topic. This paper proposes a variational Bayesian structural time-series model (STM) to effectively process complex fuel sales data online and provide real-time forecasting of fuel sales. While a traditional STM normally uses a probability model and the Markov chain Monte Carlo (MCMC) method to process change points, using the MCMC method to train the online model can be difficult given a relatively heavy computing load and time consumption. We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"3 1","pages":"45-66"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88705158","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}
Ermioni Qafzezi, Kevin Bylykbashi, Phudit Ampririt, Makoto Ikeda, Keita Matsuo, L. Barolli
Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.
{"title":"A fuzzy-based approach for resource management in SDN-VANETs: Effect of trustworthiness on assessment of available edge computing resources","authors":"Ermioni Qafzezi, Kevin Bylykbashi, Phudit Ampririt, Makoto Ikeda, Keita Matsuo, L. Barolli","doi":"10.3233/JHS-210650","DOIUrl":"https://doi.org/10.3233/JHS-210650","url":null,"abstract":"Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"14 1","pages":"33-44"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73961403","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}
Trust-aware routing is the significant direction for designing the secure routing protocol in Wireless Sensor Network (WSN). However, the trust-aware routing mechanism is implemented to evaluate the trustworthiness of the neighboring nodes based on the set of trust factors. Various trust-aware routing protocols are developed to route the data with minimum delay, but detecting the route with good quality poses a challenging issue in the research community. Therefore, an effective method named Sunflower Sine Cosine (SFSC)-based stacked autoencoder is designed to perform Electroencephalogram (EEG) signal classification using trust-aware routing in WSN. Moreover, the proposed SFSC algorithm incorporates Sunflower Optimization (SFO) and Sine Cosine Algorithm (SCA) that reveals an optimal solution, which is the optimal route used to transmit the EEG signal. Initially, the trust factors are computed from the nodes simulated in the network environment, and thereby, the trust-based routing is performed to achieve EEG signal classification. The proposed SFSC-based stacked autoencoder attained better performance by selecting the optimal path based on the fitness parameters, like energy, trust, and distance. The performance of the proposed approach is analyzed using the metrics, such as sensitivity, accuracy, and specificity. The proposed approach acquires 94.708%, 94.431%, and 95.780% sensitivity, accuracy, and specificity, respectively, with 150 nodes.
{"title":"Trust aware routing using sunflower sine cosine-based stacked autoencoder approach for EEG signal classification in WSN","authors":"Shanthi Kumaraguru, M. Jebarani","doi":"10.3233/JHS-210654","DOIUrl":"https://doi.org/10.3233/JHS-210654","url":null,"abstract":"Trust-aware routing is the significant direction for designing the secure routing protocol in Wireless Sensor Network (WSN). However, the trust-aware routing mechanism is implemented to evaluate the trustworthiness of the neighboring nodes based on the set of trust factors. Various trust-aware routing protocols are developed to route the data with minimum delay, but detecting the route with good quality poses a challenging issue in the research community. Therefore, an effective method named Sunflower Sine Cosine (SFSC)-based stacked autoencoder is designed to perform Electroencephalogram (EEG) signal classification using trust-aware routing in WSN. Moreover, the proposed SFSC algorithm incorporates Sunflower Optimization (SFO) and Sine Cosine Algorithm (SCA) that reveals an optimal solution, which is the optimal route used to transmit the EEG signal. Initially, the trust factors are computed from the nodes simulated in the network environment, and thereby, the trust-based routing is performed to achieve EEG signal classification. The proposed SFSC-based stacked autoencoder attained better performance by selecting the optimal path based on the fitness parameters, like energy, trust, and distance. The performance of the proposed approach is analyzed using the metrics, such as sensitivity, accuracy, and specificity. The proposed approach acquires 94.708%, 94.431%, and 95.780% sensitivity, accuracy, and specificity, respectively, with 150 nodes.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"5 1","pages":"101-119"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73623793","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":"Application of Internet of Things intelligent image-positioning studio classroom in English teaching","authors":"Jie Chen, Yukun Chen, Jiaxin Lin","doi":"10.3233/JHS-210667","DOIUrl":"https://doi.org/10.3233/JHS-210667","url":null,"abstract":"","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"10 1","pages":"279-289"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84729606","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}
Khuram Khalid, I. Woungang, S. K. Dhurandher, Jagdeep Singh, L. Barolli
Unlike communication networks which are traditionally assumed to be connected, Opportunistic networks (OppNets) are a type of wireless ad hoc networks with no guarantee of end-to-end path for data routing, which is due to node mobility, volatile links, and frequent disconnections. As such, data transmission among the nodes relies on their cooperation and this is realized in a store-and-carry fashion. To this end, several opportunistic routing techniques have been proposed in the literature, some of which using geocasting, a technique that consists of scheduling the message to a specific region toward its destination. This paper proposes a Fuzzy-based Check-and-Spray Geocast (FCSG) routing protocol for OppNets, in which a Check-and-Spray mechanism is used to control the message flooding within the destination cast and a fuzzy controller is used for selecting the suitable relay nodes to carry the message toward the destination, with the aim to improve the delivery ratio. Using simulations, the proposed FCSG protocol is shown to outperform the F-GSAF, GeoEpidemic and EECSG routing protocols in terms of overhead ratio, average latency, and delivery ratio, under varying number of nodes, buffer size, and Time-to-Live.
{"title":"A fuzzy-based check-and-spray geocast routing protocol for opportunistic networks","authors":"Khuram Khalid, I. Woungang, S. K. Dhurandher, Jagdeep Singh, L. Barolli","doi":"10.3233/JHS-210648","DOIUrl":"https://doi.org/10.3233/JHS-210648","url":null,"abstract":"Unlike communication networks which are traditionally assumed to be connected, Opportunistic networks (OppNets) are a type of wireless ad hoc networks with no guarantee of end-to-end path for data routing, which is due to node mobility, volatile links, and frequent disconnections. As such, data transmission among the nodes relies on their cooperation and this is realized in a store-and-carry fashion. To this end, several opportunistic routing techniques have been proposed in the literature, some of which using geocasting, a technique that consists of scheduling the message to a specific region toward its destination. This paper proposes a Fuzzy-based Check-and-Spray Geocast (FCSG) routing protocol for OppNets, in which a Check-and-Spray mechanism is used to control the message flooding within the destination cast and a fuzzy controller is used for selecting the suitable relay nodes to carry the message toward the destination, with the aim to improve the delivery ratio. Using simulations, the proposed FCSG protocol is shown to outperform the F-GSAF, GeoEpidemic and EECSG routing protocols in terms of overhead ratio, average latency, and delivery ratio, under varying number of nodes, buffer size, and Time-to-Live.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"17 1","pages":"1-12"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88069015","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}
One of the effective text categorization methods for learning the large-scale data and the accumulated data is incremental learning. The major challenge in the incremental learning is improving the accuracy as the text document consists of numerous terms. In this research, a incremental text categorization method is developed using the proposed Spider Grasshopper Crow Optimization Algorithm based Deep Belief Neural network (SGrC-based DBN) for providing optimal text categorization results. The proposed text categorization method has four processes, such as are pre-processing, feature extraction, feature selection, text categorization, and incremental learning. Initially, the database is pre-processed and fed into vector space model for the extraction of features. Once the features are extracted, the feature selection is carried out based on mutual information. Then, the text categorization is performed using the proposed SGrC-based DBN method, which is developed by the integration of the spider monkey optimization (SMO) with the Grasshopper Crow Optimization Algorithm (GCOA) algorithm. Finally, the incremental text categorization is performed based on the hybrid weight bounding model that includes the SGrC and Range degree and particularly, the optimal weights of the Range degree model is selected based on SGrC. The experimental result of the proposed text categorization method is performed by considering the data from the Reuter database and 20 Newsgroups database. The comparative analysis of the text categorization method is based on the performance metrics, such as precision, recall and accuracy. The proposed SGrC algorithm obtained a maximum accuracy of 0.9626, maximum precision of 0.9681 and maximum recall of 0.9600, respectively when compared with the existing incremental text categorization methods.
{"title":"Incremental text categorization based on hybrid optimization-based deep belief neural network","authors":"V. Srilakshmi, K. Anuradha, C. Bindu","doi":"10.3233/JHS-210659","DOIUrl":"https://doi.org/10.3233/JHS-210659","url":null,"abstract":"One of the effective text categorization methods for learning the large-scale data and the accumulated data is incremental learning. The major challenge in the incremental learning is improving the accuracy as the text document consists of numerous terms. In this research, a incremental text categorization method is developed using the proposed Spider Grasshopper Crow Optimization Algorithm based Deep Belief Neural network (SGrC-based DBN) for providing optimal text categorization results. The proposed text categorization method has four processes, such as are pre-processing, feature extraction, feature selection, text categorization, and incremental learning. Initially, the database is pre-processed and fed into vector space model for the extraction of features. Once the features are extracted, the feature selection is carried out based on mutual information. Then, the text categorization is performed using the proposed SGrC-based DBN method, which is developed by the integration of the spider monkey optimization (SMO) with the Grasshopper Crow Optimization Algorithm (GCOA) algorithm. Finally, the incremental text categorization is performed based on the hybrid weight bounding model that includes the SGrC and Range degree and particularly, the optimal weights of the Range degree model is selected based on SGrC. The experimental result of the proposed text categorization method is performed by considering the data from the Reuter database and 20 Newsgroups database. The comparative analysis of the text categorization method is based on the performance metrics, such as precision, recall and accuracy. The proposed SGrC algorithm obtained a maximum accuracy of 0.9626, maximum precision of 0.9681 and maximum recall of 0.9600, respectively when compared with the existing incremental text categorization methods.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"31 1","pages":"183-202"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82479950","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}
Existing blockchains, especially public blockchains, face the challenges of scalability which means the processing capacity will not get better with the addition of nodes, making it somewhat infeasible for mobile computing applications. Some improved technologies are known to speed up processing capacity by shrinking the consensus group, increasing the block capacity and/or shortening the block interval. Even these solutions are met with major problems such as storage limitations and weak security. To face the realistic application scenarios for blockchain technology in the mobile realm, we propose a new public blockchain designed based on sharding, aggregate signature and cryptographic sortition which we call SAC. In SAC, the transaction rate increases with the number of shards while the length of the consensus signature is a constant. Meanwhile, in SAC, the assignment of consensus representatives is controlled by a verifiable random function, which can effectively solve the problem of centralized consensus. In addition, this paper analyzes the performance of SAC to give adequate comparison with other sharding technologies while also giving a rational security analysis. Our experimental results clearly show the potential applicability of this novel blockchain protocol to in mobile computation.
{"title":"New public blockchain protocol based on sharding and aggregate signatures","authors":"Jinhua Fu, Wenhui Zhou, Mi-xue Xu, Xueming Si, Chao Yuan, Yongzhong Huang","doi":"10.3233/JHS-210653","DOIUrl":"https://doi.org/10.3233/JHS-210653","url":null,"abstract":"Existing blockchains, especially public blockchains, face the challenges of scalability which means the processing capacity will not get better with the addition of nodes, making it somewhat infeasible for mobile computing applications. Some improved technologies are known to speed up processing capacity by shrinking the consensus group, increasing the block capacity and/or shortening the block interval. Even these solutions are met with major problems such as storage limitations and weak security. To face the realistic application scenarios for blockchain technology in the mobile realm, we propose a new public blockchain designed based on sharding, aggregate signature and cryptographic sortition which we call SAC. In SAC, the transaction rate increases with the number of shards while the length of the consensus signature is a constant. Meanwhile, in SAC, the assignment of consensus representatives is controlled by a verifiable random function, which can effectively solve the problem of centralized consensus. In addition, this paper analyzes the performance of SAC to give adequate comparison with other sharding technologies while also giving a rational security analysis. Our experimental results clearly show the potential applicability of this novel blockchain protocol to in mobile computation.","PeriodicalId":54809,"journal":{"name":"Journal of High Speed Networks","volume":"73 1","pages":"83-99"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88508185","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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