Pub Date : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924842
Fan Zhenlin, Baofeng Zheng, Wu Bin, Cui Fangzi
A Field Work Safety Monitoring System for Western China was build up to fill the gaps in real-time and dynamic monitoring for geological survey field work in hard and dangerous regions. The system was based on Beidou satellite network, private network of China Geological Survey and mobile communication network. It combined the Geographic Information System technology, safety production management and dynamic monitoring for vehicle and staff. Applications of the system indicated that it could improve the security of geological survey and guarantee the safety of lives and property in hard and dangerous regions. The occupational hazard and accident risk was reduced significantly to ensure successful implementation of the geological survey work.
{"title":"A Field Work Safety Monitoring System based on Beidou satellite for hard and dangerous regions in Western China","authors":"Fan Zhenlin, Baofeng Zheng, Wu Bin, Cui Fangzi","doi":"10.1109/COMPCOMM.2016.7924842","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924842","url":null,"abstract":"A Field Work Safety Monitoring System for Western China was build up to fill the gaps in real-time and dynamic monitoring for geological survey field work in hard and dangerous regions. The system was based on Beidou satellite network, private network of China Geological Survey and mobile communication network. It combined the Geographic Information System technology, safety production management and dynamic monitoring for vehicle and staff. Applications of the system indicated that it could improve the security of geological survey and guarantee the safety of lives and property in hard and dangerous regions. The occupational hazard and accident risk was reduced significantly to ensure successful implementation of the geological survey work.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"29 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131588414","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7925237
Jian Yang, Hang-sheng Zhao, Xi Chen
Spectrum prediction forecasts future channel status based on history data, which partly solves the problem of robustness and reliability in spectrum sensing. A genetic algorithm optimized back propagation (GA-BP) training has been proposed to solve the problem that the neural network based spectrum prediction model always trapped in local optimal solution. Selection, crossover and mutation are performed to increase the randomness, which ensures the population converge to the set that contains the global optimal solution. Then the model continuously performs local searching with back propagation (BP) training. Simulation results show that the performance of GA-BP training outperforms BP training, and SU should choose training method according to his own requirements. The improvement of prediction accuracy will promote the application of spectrum prediction in cognitive radio networks, and maybe helpful to solve the problem in robustness and reliability of spectrum sensing.
{"title":"Genetic algorithm optimized training for neural network spectrum prediction","authors":"Jian Yang, Hang-sheng Zhao, Xi Chen","doi":"10.1109/COMPCOMM.2016.7925237","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7925237","url":null,"abstract":"Spectrum prediction forecasts future channel status based on history data, which partly solves the problem of robustness and reliability in spectrum sensing. A genetic algorithm optimized back propagation (GA-BP) training has been proposed to solve the problem that the neural network based spectrum prediction model always trapped in local optimal solution. Selection, crossover and mutation are performed to increase the randomness, which ensures the population converge to the set that contains the global optimal solution. Then the model continuously performs local searching with back propagation (BP) training. Simulation results show that the performance of GA-BP training outperforms BP training, and SU should choose training method according to his own requirements. The improvement of prediction accuracy will promote the application of spectrum prediction in cognitive radio networks, and maybe helpful to solve the problem in robustness and reliability of spectrum sensing.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389143","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924980
Dang Jingjing, Cheng Li, Yuan Meng
Channel estimation is very important in a communication system. An important indicator for the evaluation of a wireless communication system is the symbol error rate of channel estimation. With the analysis of least square (LS) channel estimation algorithm, this paper proposed an improved LS channel estimation algorithm based on phase compensation. Experimental results show that this algorithm can effectively reduce the symbol error rate of the system and improve the accuracy of the LS algorithm. Furthermore, the proposed algorithm reduces the impacts of system's frequency offset on channel estimation.
{"title":"Research on least square channel estimation algorithm based on phase compensation","authors":"Dang Jingjing, Cheng Li, Yuan Meng","doi":"10.1109/COMPCOMM.2016.7924980","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924980","url":null,"abstract":"Channel estimation is very important in a communication system. An important indicator for the evaluation of a wireless communication system is the symbol error rate of channel estimation. With the analysis of least square (LS) channel estimation algorithm, this paper proposed an improved LS channel estimation algorithm based on phase compensation. Experimental results show that this algorithm can effectively reduce the symbol error rate of the system and improve the accuracy of the LS algorithm. Furthermore, the proposed algorithm reduces the impacts of system's frequency offset on channel estimation.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126251277","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7925056
He Jiaxin, Xu Chunxiu, Wu Yuewei
Delay Tolerant Networks (DTNs) are characterized by the continuously varied network environment and the limitation of resource. To conquer this problem, DTN routing protocols focus on sending multiple copies of data packets to increase the probability of reaching the destination. In addition, nodes in DTN have strong social attributes which affect the performance of networks. However, resource efficiency and social attributes are not the main concern in most of existing DTN routing protocols. In this paper, we present the resource-efficient routing protocol based on historical encounter time interval (RRPHETI). RRPHETI aims at considering the energy and buffer capability when selecting relay nodes and limiting replicas. RRPHETI creates a model to capture the resource consumption behaviors in DTN, and utilizes the maximum likelihood method to estimate the parameters of delivery probability. In particular, RRPHETI exploits historical encounter time interval to measure social relations between nodes, as the more intimate the nodes are, the smaller the encounter time interval becomes. At last, RRPHETI formulates the utility value of a node as a criterion for packet forwarding so as to keep the consistency of direction of packet towards the destination node. The simulation results show that RRPHETI achieves higher delivery ratio and better overhead ratio and average delay with minimal energy consumption compared to other protocols within resource constrained network situations.
{"title":"Resource-efficient routing protocol based on historical encounter time interval in DTN","authors":"He Jiaxin, Xu Chunxiu, Wu Yuewei","doi":"10.1109/COMPCOMM.2016.7925056","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7925056","url":null,"abstract":"Delay Tolerant Networks (DTNs) are characterized by the continuously varied network environment and the limitation of resource. To conquer this problem, DTN routing protocols focus on sending multiple copies of data packets to increase the probability of reaching the destination. In addition, nodes in DTN have strong social attributes which affect the performance of networks. However, resource efficiency and social attributes are not the main concern in most of existing DTN routing protocols. In this paper, we present the resource-efficient routing protocol based on historical encounter time interval (RRPHETI). RRPHETI aims at considering the energy and buffer capability when selecting relay nodes and limiting replicas. RRPHETI creates a model to capture the resource consumption behaviors in DTN, and utilizes the maximum likelihood method to estimate the parameters of delivery probability. In particular, RRPHETI exploits historical encounter time interval to measure social relations between nodes, as the more intimate the nodes are, the smaller the encounter time interval becomes. At last, RRPHETI formulates the utility value of a node as a criterion for packet forwarding so as to keep the consistency of direction of packet towards the destination node. The simulation results show that RRPHETI achieves higher delivery ratio and better overhead ratio and average delay with minimal energy consumption compared to other protocols within resource constrained network situations.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432826","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924813
Yu Ming, Zhang Guang, Wu Taihu, Gu Biao, Li Liangzhe, Wang Chunchen, Wang Dan, Chen Feng
The widening application of automated external defibrillators (AEDs) present very strong requirements for reliable shockable rhythm detection. In this study, we developed a BP neural network to differentiate well between shockable and nonshockable rhythm. A total of 18 metrics were extracted from the ECG signals. Each one of these metrics respectively characteristics each aspect of the signals, such as morphology, gaussianity, spectra, variability, complexity, and so on. These metrics were regarded as the input vector of the BP neural network. After the training, a classifier used for shockable and nonshockable rhythm classification was obtained. The constructed BP neural network was tested with the database of VFDB and CUDB, the sensitivity and specificity reached up to 93.04% and 97.43 %, respectively.
{"title":"Detection of shockable rhythm using multi-parameter fusion identification and BP neural network","authors":"Yu Ming, Zhang Guang, Wu Taihu, Gu Biao, Li Liangzhe, Wang Chunchen, Wang Dan, Chen Feng","doi":"10.1109/COMPCOMM.2016.7924813","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924813","url":null,"abstract":"The widening application of automated external defibrillators (AEDs) present very strong requirements for reliable shockable rhythm detection. In this study, we developed a BP neural network to differentiate well between shockable and nonshockable rhythm. A total of 18 metrics were extracted from the ECG signals. Each one of these metrics respectively characteristics each aspect of the signals, such as morphology, gaussianity, spectra, variability, complexity, and so on. These metrics were regarded as the input vector of the BP neural network. After the training, a classifier used for shockable and nonshockable rhythm classification was obtained. The constructed BP neural network was tested with the database of VFDB and CUDB, the sensitivity and specificity reached up to 93.04% and 97.43 %, respectively.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121424837","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7925036
Yuqing Wang, Zhenming Peng, Yanmin He
The S transform is a time-frequency representation with multi-scale focus. It adopts a scalable Gaussian window function to provide a frequency dependent resolution. However, it still suffers from low resolution, which does not satisfy the high precision seismic imaging. Therefore, we propose the sparse S transform to obtain a sparse and aggregated time-frequency spectrum, and apply it into seismic data analysis. The S transform is considered as inverse problem with L1 minimization constraint known as basis pursuit denoising (BPDN) form. The good performance of the proposed method is assessed on simulated and real seismic data. The results indicate that our method can enhance the sparsity of ST, and provide a high resolution and focused time-frequency spectrum for seismic data, which is conducive to seismic imaging and reservoir interpretation.
{"title":"Time-frequency representation for seismic data using sparse S transform","authors":"Yuqing Wang, Zhenming Peng, Yanmin He","doi":"10.1109/COMPCOMM.2016.7925036","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7925036","url":null,"abstract":"The S transform is a time-frequency representation with multi-scale focus. It adopts a scalable Gaussian window function to provide a frequency dependent resolution. However, it still suffers from low resolution, which does not satisfy the high precision seismic imaging. Therefore, we propose the sparse S transform to obtain a sparse and aggregated time-frequency spectrum, and apply it into seismic data analysis. The S transform is considered as inverse problem with L1 minimization constraint known as basis pursuit denoising (BPDN) form. The good performance of the proposed method is assessed on simulated and real seismic data. The results indicate that our method can enhance the sparsity of ST, and provide a high resolution and focused time-frequency spectrum for seismic data, which is conducive to seismic imaging and reservoir interpretation.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123092977","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7925050
Z. Limin, Li Zhe-qing, Wang Hui, L. Peiyu, Chen Xi
A new backup topology design method is proposed in this paper to solve the problem of network congestion during IP fast recovery rerouting. IP fast reroute establishes a series of backup topologies before single network failure, and uses a certain backup topology to reroute. The main previous work develops a method which determines the Key Nodes in process of creating the backup topology to separate traffic on high load links to the other links, utilizing the network topology and traffic matrix. In this paper, a new method to determine Key Nodes is designed, whose main idea is to select the Key Nodes according to the Betweenness Centrality and Closeness of nodes in backup topology, and then maximizes available links of Key Nodes. The experimental results show that maximum link load reduction is approximately 73% compared with the state of conventional algorithm, and maximum reduced hops is about 45% when single link failure. In addition, considering the location of the Key Nodes for a large network is a better strategy when using the Closeness algorithm to select the Key Nodes.
{"title":"A new backup topology design method for IP fast recovery","authors":"Z. Limin, Li Zhe-qing, Wang Hui, L. Peiyu, Chen Xi","doi":"10.1109/COMPCOMM.2016.7925050","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7925050","url":null,"abstract":"A new backup topology design method is proposed in this paper to solve the problem of network congestion during IP fast recovery rerouting. IP fast reroute establishes a series of backup topologies before single network failure, and uses a certain backup topology to reroute. The main previous work develops a method which determines the Key Nodes in process of creating the backup topology to separate traffic on high load links to the other links, utilizing the network topology and traffic matrix. In this paper, a new method to determine Key Nodes is designed, whose main idea is to select the Key Nodes according to the Betweenness Centrality and Closeness of nodes in backup topology, and then maximizes available links of Key Nodes. The experimental results show that maximum link load reduction is approximately 73% compared with the state of conventional algorithm, and maximum reduced hops is about 45% when single link failure. In addition, considering the location of the Key Nodes for a large network is a better strategy when using the Closeness algorithm to select the Key Nodes.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322303","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924870
Yongcheng Li, Changdong Han, Manxi Wang, Hui-fang Chen, Lei Xie
In this paper, the primary user emulation attack (PUEA) detection problem in the cognitive radio network (CRN) with mobile secondary user (SU) is investigated. We propose a hybrid PUEA detection method, in which two kinds of wireless channel characteristics between the transmitter and the receiver, the Doppler spread and the variance of the received signal power, are utilized to infer the source of the received signal. In the CRN with mobile SU, the Doppler spread between the SU and the primary user (PU) may differ from that between the SU and the primary user emulator (PUE) because of different relative velocities. However, the performance of the PUEA detection method based on Doppler spread may be affected the relative position between the SU and the PUE. In order to make the detection performance be unaffected the relative position between the SU and PUE, the variance of the received signal power is also used as the signature of the transmitter. The proposed PUEA detection method is validated by Monte Carlo simulations. Simulation results show that the performance of the proposed hybrid PUEA detection method is good with well-chosen parameters.
{"title":"A primary user emulation attack detection scheme in cognitive radio network with mobile secondary user","authors":"Yongcheng Li, Changdong Han, Manxi Wang, Hui-fang Chen, Lei Xie","doi":"10.1109/COMPCOMM.2016.7924870","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924870","url":null,"abstract":"In this paper, the primary user emulation attack (PUEA) detection problem in the cognitive radio network (CRN) with mobile secondary user (SU) is investigated. We propose a hybrid PUEA detection method, in which two kinds of wireless channel characteristics between the transmitter and the receiver, the Doppler spread and the variance of the received signal power, are utilized to infer the source of the received signal. In the CRN with mobile SU, the Doppler spread between the SU and the primary user (PU) may differ from that between the SU and the primary user emulator (PUE) because of different relative velocities. However, the performance of the PUEA detection method based on Doppler spread may be affected the relative position between the SU and the PUE. In order to make the detection performance be unaffected the relative position between the SU and PUE, the variance of the received signal power is also used as the signature of the transmitter. The proposed PUEA detection method is validated by Monte Carlo simulations. Simulation results show that the performance of the proposed hybrid PUEA detection method is good with well-chosen parameters.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126410281","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924915
Cheng Zihang, Huang Zhen
DNA computation is a new computing model with high performance in storage of DNA molecules and parallelism of biochemical reactions, but it needs complex conditions of biochemical operation, likely astable and uncontrollable. The main work of this paper is optimizing GN algorithm to solve a graph clustering question on social networks, which simulated on computer using the DNA computation model to improve the computational efficiency. Simulation results of the Karate Club interpersonal relationship network indicate that the proposed algorithm has a better performance than traditional GN algorithm.
{"title":"Optimization of GN algorithm based on DNA computation","authors":"Cheng Zihang, Huang Zhen","doi":"10.1109/COMPCOMM.2016.7924915","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924915","url":null,"abstract":"DNA computation is a new computing model with high performance in storage of DNA molecules and parallelism of biochemical reactions, but it needs complex conditions of biochemical operation, likely astable and uncontrollable. The main work of this paper is optimizing GN algorithm to solve a graph clustering question on social networks, which simulated on computer using the DNA computation model to improve the computational efficiency. Simulation results of the Karate Club interpersonal relationship network indicate that the proposed algorithm has a better performance than traditional GN algorithm.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126492890","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 : 2016-10-01DOI: 10.1109/COMPCOMM.2016.7924669
W. Kou, Xuejing Yang, Changxian Liang, Changbo Xie, Shu Gan
The continuously growing volume of massive remote sensing data raised huge challenges on storage space and querying efficiency. In this paper, a new management model of remote sensing data has been proposed to address these issues of system architecture, data storage strategies, and functionalities based on Hadoop Distributed File System (HDFS). The model is capable of relieving the overloading problems of the single NameNode server in HDFS by taking a dual storage mechanism and a simulating operations method. On one hand, Relational Database Management System (RDBMS) and HDFS are separately taken to store and manage image files and related metadata of remote sensing data; on the other hand, operations of file systems are simulated by RDBMS. The study results show the model could improve management and storage efficiency of remote sensing data.
{"title":"HDFS enabled storage and management of remote sensing data","authors":"W. Kou, Xuejing Yang, Changxian Liang, Changbo Xie, Shu Gan","doi":"10.1109/COMPCOMM.2016.7924669","DOIUrl":"https://doi.org/10.1109/COMPCOMM.2016.7924669","url":null,"abstract":"The continuously growing volume of massive remote sensing data raised huge challenges on storage space and querying efficiency. In this paper, a new management model of remote sensing data has been proposed to address these issues of system architecture, data storage strategies, and functionalities based on Hadoop Distributed File System (HDFS). The model is capable of relieving the overloading problems of the single NameNode server in HDFS by taking a dual storage mechanism and a simulating operations method. On one hand, Relational Database Management System (RDBMS) and HDFS are separately taken to store and manage image files and related metadata of remote sensing data; on the other hand, operations of file systems are simulated by RDBMS. The study results show the model could improve management and storage efficiency of remote sensing data.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114167840","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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