Pub Date : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996144
R. GeethaRamani, K. Sivaselvi
Human Brain can be studied and analysed using neuroimages viz. MRI(Magnetic Resonance Imaging) / fMRI(Functional Magnetic Resonance Imaging) / PET(Positron Emission Tomography) / EEG(Electroencephalography) / MEG(Magnetoencephalography), which brings out the hidden information from it. Connectomes are graph like structure which represents complex brain network connectivity which are of two type's mainly structural connectivity and functional connectivity. Connectivity can be anatomical and functional properties of the brain. Nodes are voxels or Regions of Interest whereas edges are fibre bundles, temporal correlation between regions, in structural connectome and functional connectome respectively. This work focuses on functional connectivity analysis of brain network obtained through RS- fMRI images for identification of important regions in the human brain using image processing and graph theoretical approaches. The RS-fMRI images are obtained from 1000 Functional connectomes project and preprocessed using image processing techniques. Then the image is parcellated using AAL(Automated Anatomical Labeling) atlas and binary matrix is obtained. The graph is constructed from the derived matrix that exhibits functional connectivity between ROIs(Region of Interest). The various centrality measures (degree centrality, eigenvector centrality, betweenness centrality and closeness centrality) are used to identify the ROIs that act as provincial and/or connector hubs. The prominent provincial hubs are Rolandic Operculum, Thalamus, Insula, Hippocampus, Olfactory and connector hubs are Insula, Putamen, Occipital superior gyrus, Parietal Superior gyrus and Supramarginal gyrus. This work highlights the key regions in human brain which is involved in massive communication and information flow within the network.
{"title":"Human brain hubs(provincial and connector) identification using centrality measures","authors":"R. GeethaRamani, K. Sivaselvi","doi":"10.1109/ICRTIT.2014.6996144","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996144","url":null,"abstract":"Human Brain can be studied and analysed using neuroimages viz. MRI(Magnetic Resonance Imaging) / fMRI(Functional Magnetic Resonance Imaging) / PET(Positron Emission Tomography) / EEG(Electroencephalography) / MEG(Magnetoencephalography), which brings out the hidden information from it. Connectomes are graph like structure which represents complex brain network connectivity which are of two type's mainly structural connectivity and functional connectivity. Connectivity can be anatomical and functional properties of the brain. Nodes are voxels or Regions of Interest whereas edges are fibre bundles, temporal correlation between regions, in structural connectome and functional connectome respectively. This work focuses on functional connectivity analysis of brain network obtained through RS- fMRI images for identification of important regions in the human brain using image processing and graph theoretical approaches. The RS-fMRI images are obtained from 1000 Functional connectomes project and preprocessed using image processing techniques. Then the image is parcellated using AAL(Automated Anatomical Labeling) atlas and binary matrix is obtained. The graph is constructed from the derived matrix that exhibits functional connectivity between ROIs(Region of Interest). The various centrality measures (degree centrality, eigenvector centrality, betweenness centrality and closeness centrality) are used to identify the ROIs that act as provincial and/or connector hubs. The prominent provincial hubs are Rolandic Operculum, Thalamus, Insula, Hippocampus, Olfactory and connector hubs are Insula, Putamen, Occipital superior gyrus, Parietal Superior gyrus and Supramarginal gyrus. This work highlights the key regions in human brain which is involved in massive communication and information flow within the network.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121642202","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996168
R. Madhu, R. Senthilkumar
Recommendation system provides information about the arrival and importance of a newly released movie to their registered user. The pursuit of the users is analyzed from their past history. In this paper, a recommendation system is proposed to recommend rating of the movie to the users. The learning phase of the system takes in the user particulars about the user till-date and his rating towards those movies. Having the Genre of the movie and its rating, the system is trained by data mining classifiers like Bayesian, Multiclass Classifier, Decision Stump Tree, Best First Decision Tree(BFTree) and Radial Basis Function(RBF) and the classification parameters i.e. True Positive rates(TP), False Positive rates(FP), Precision, Recall and Mean Absolute Error are computed. It has been concluded that the RBF classifier performs better than the other classifiers. This paper also focuses to address the problem of cold start movie. The genre of the new release is obtained and it's recommended to the corresponding user, those who are closely correlated. Implementations are carried out using movie lens datasets.
{"title":"Recommendation system to accomplish user pursuit","authors":"R. Madhu, R. Senthilkumar","doi":"10.1109/ICRTIT.2014.6996168","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996168","url":null,"abstract":"Recommendation system provides information about the arrival and importance of a newly released movie to their registered user. The pursuit of the users is analyzed from their past history. In this paper, a recommendation system is proposed to recommend rating of the movie to the users. The learning phase of the system takes in the user particulars about the user till-date and his rating towards those movies. Having the Genre of the movie and its rating, the system is trained by data mining classifiers like Bayesian, Multiclass Classifier, Decision Stump Tree, Best First Decision Tree(BFTree) and Radial Basis Function(RBF) and the classification parameters i.e. True Positive rates(TP), False Positive rates(FP), Precision, Recall and Mean Absolute Error are computed. It has been concluded that the RBF classifier performs better than the other classifiers. This paper also focuses to address the problem of cold start movie. The genre of the new release is obtained and it's recommended to the corresponding user, those who are closely correlated. Implementations are carried out using movie lens datasets.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122830568","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996114
P. A. Elizabeth, Manju Mohan, P. Samuel, S. Pandian, B. Tyagi
Suppression of mosquito breeding is a mandatory first step to reduce the source of major mosquito-borne diseases like Malaria and Dengue fever. This paper deals with an innovative method for the identification of mosquito breeding sites (stagnant pools), using wireless networking technologies and removal of stagnant water through electromechanical pumping systems. The stagnant water areas are first identified and reported by public users using a web-based portal or using Short Message Service (SMS) through mobile phones. Based on the complaints stored in the database, a route via the stagnant sites is drawn on a map using a Geographic Information System (GIS). A vehicle carrying a Global Positioning system (GPS), on-board camera, and a pumping system with a tank for removing the stagnant water traverses the via points of stagnant pools. Finally, stagnant water is removed using a pumping system and stored in the onboard tank for emptying later. Results of the implementation of a proof-of-concept prototype system are reported, to illustrate the effectiveness of the proposed approach.
{"title":"Identification and eradication of mosquito breeding sites using wireless networking and electromechanical technologies","authors":"P. A. Elizabeth, Manju Mohan, P. Samuel, S. Pandian, B. Tyagi","doi":"10.1109/ICRTIT.2014.6996114","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996114","url":null,"abstract":"Suppression of mosquito breeding is a mandatory first step to reduce the source of major mosquito-borne diseases like Malaria and Dengue fever. This paper deals with an innovative method for the identification of mosquito breeding sites (stagnant pools), using wireless networking technologies and removal of stagnant water through electromechanical pumping systems. The stagnant water areas are first identified and reported by public users using a web-based portal or using Short Message Service (SMS) through mobile phones. Based on the complaints stored in the database, a route via the stagnant sites is drawn on a map using a Geographic Information System (GIS). A vehicle carrying a Global Positioning system (GPS), on-board camera, and a pumping system with a tank for removing the stagnant water traverses the via points of stagnant pools. Finally, stagnant water is removed using a pumping system and stored in the onboard tank for emptying later. Results of the implementation of a proof-of-concept prototype system are reported, to illustrate the effectiveness of the proposed approach.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130390267","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996146
G. Logeswari, D. Sangeetha, V. Vaidehi
As there is an increasing need to share the medical information for public health research, enormous amount of Personal Health Records (PHR's) are periodically collected and shared between two or many sources for research purpose. Sharing medical information about an individual without revealing sensitive information is the biggest challenge. Privacy and security are the two biggest obstacles for this process. Since medical information is related to human subjects, it is essential to preserve the privacy of the patients and ensure security to the medical information stored in cloud. In this paper, privacy of the shared PHR's is preserved through data anonymization and encryption algorithm. PHR's can be anonymized using various techniques such as generalization, suppression, truncation, etc., This paper focuses to provide efficient analysis of the shared PHR's by the proposed Efficient K-Means Clustering (EKMC) algorithm and to reduce the cost of data storage by the proposed Data Aggregation and Deduplication (DAD) algorithm. The EKMC algorithm is efficient and consumes less time when compared to the traditional k-means clustering algorithm. A set of performance analysis showing the effectiveness of our approach using synthetic data sets is presented.
{"title":"A cost effective clustering based anonymization approach for storing PHR's in cloud","authors":"G. Logeswari, D. Sangeetha, V. Vaidehi","doi":"10.1109/ICRTIT.2014.6996146","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996146","url":null,"abstract":"As there is an increasing need to share the medical information for public health research, enormous amount of Personal Health Records (PHR's) are periodically collected and shared between two or many sources for research purpose. Sharing medical information about an individual without revealing sensitive information is the biggest challenge. Privacy and security are the two biggest obstacles for this process. Since medical information is related to human subjects, it is essential to preserve the privacy of the patients and ensure security to the medical information stored in cloud. In this paper, privacy of the shared PHR's is preserved through data anonymization and encryption algorithm. PHR's can be anonymized using various techniques such as generalization, suppression, truncation, etc., This paper focuses to provide efficient analysis of the shared PHR's by the proposed Efficient K-Means Clustering (EKMC) algorithm and to reduce the cost of data storage by the proposed Data Aggregation and Deduplication (DAD) algorithm. The EKMC algorithm is efficient and consumes less time when compared to the traditional k-means clustering algorithm. A set of performance analysis showing the effectiveness of our approach using synthetic data sets is presented.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132691783","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996181
G. Thendral, C. Valliyammai
Cloud is an innovative service platform. In this computing standard it delivers all the resources such as both hardware and software as a service over the Internet. Since the information are outsourced on the server of cloud and maintained at an anonymous place, there is the possibility of alteration or modification on the data because of any of the failures or because of the fraudulence of the mischievous server. To achieve the data integrity, there is a need of employing some of the data verification and auditing techniques. The proposed work is to perform the dynamic auditing for integrity verification and data dynamics in cloud storage with lower computation and communication cost, using techniques such as tagging, hash tag table and arbitrary sampling. It also supports timely anomaly detection and updates to outsourced data.
{"title":"Dynamic auditing and updating services in cloud storage","authors":"G. Thendral, C. Valliyammai","doi":"10.1109/ICRTIT.2014.6996181","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996181","url":null,"abstract":"Cloud is an innovative service platform. In this computing standard it delivers all the resources such as both hardware and software as a service over the Internet. Since the information are outsourced on the server of cloud and maintained at an anonymous place, there is the possibility of alteration or modification on the data because of any of the failures or because of the fraudulence of the mischievous server. To achieve the data integrity, there is a need of employing some of the data verification and auditing techniques. The proposed work is to perform the dynamic auditing for integrity verification and data dynamics in cloud storage with lower computation and communication cost, using techniques such as tagging, hash tag table and arbitrary sampling. It also supports timely anomaly detection and updates to outsourced data.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128142924","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996183
M. Geethanjali, J. Angela, Jennifa Sujana, T. Revathi
Cloud computing provides dynamic provisioning for real time applications over the Internet. These services are accessed by number of clients as pay per use over the internet. In this scenario, scheduling the current jobs to be executed with given constraints for the real time tasks is an essential requirement. Hence task scheduling is a major challenge in cloud computing. In general, the main aim of Cloud Service Providers (CSPs) is to earn more amount of revenue. So, the providers may provide false information about their resources to gain more profit. To enforce the genuineness of information, game theory model is used. In older approaches, a scheduling algorithm is used to schedule the task with maximum estimated gain and executes the tasks in the queue. Therefore it increases the execution time of the task. This paper presents a scheduling mechanism for real time tasks to achieve timing constraint and minimum cost for the job execution. The game theory mechanism ensures that the truthful information is provided by CSPs. we found that the induced results of the proposed algorithm are effective and our simulation results outperform the traditional scheduling algorithms with multi-objective optimization.
{"title":"Ensuring truthfulness for scheduling multi-objective real time tasks in multi cloud environments","authors":"M. Geethanjali, J. Angela, Jennifa Sujana, T. Revathi","doi":"10.1109/ICRTIT.2014.6996183","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996183","url":null,"abstract":"Cloud computing provides dynamic provisioning for real time applications over the Internet. These services are accessed by number of clients as pay per use over the internet. In this scenario, scheduling the current jobs to be executed with given constraints for the real time tasks is an essential requirement. Hence task scheduling is a major challenge in cloud computing. In general, the main aim of Cloud Service Providers (CSPs) is to earn more amount of revenue. So, the providers may provide false information about their resources to gain more profit. To enforce the genuineness of information, game theory model is used. In older approaches, a scheduling algorithm is used to schedule the task with maximum estimated gain and executes the tasks in the queue. Therefore it increases the execution time of the task. This paper presents a scheduling mechanism for real time tasks to achieve timing constraint and minimum cost for the job execution. The game theory mechanism ensures that the truthful information is provided by CSPs. we found that the induced results of the proposed algorithm are effective and our simulation results outperform the traditional scheduling algorithms with multi-objective optimization.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"23 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128439236","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996150
R. Sudharsana, R. Tharini, K. Muthumeenakshi, S. Radha
Spectrum sensing in a noise uncertain environment is treacherous and it requires very careful detection algorithm. Noise uncertainty will make the detector unreliable due to the SNR walls. So, a double threshold method is used to improve the detection performance where each secondary user will use two thresholds detector for detection. The thresholds are calculated according to the noise uncertainty at each secondary user. Here, we have worked on and analyzed the results of traditional energy detector in a noise uncertain environment using double threshold with the aid of co-operative sensing. The unsuitability of the traditional energy detector is observed from the analysis and we propose a dual threshold based optimum power energy detection algorithm suitable under noise uncertainty.
{"title":"Performance improvement using optimum power energy detector under noise uncertain environment in cognitive radio","authors":"R. Sudharsana, R. Tharini, K. Muthumeenakshi, S. Radha","doi":"10.1109/ICRTIT.2014.6996150","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996150","url":null,"abstract":"Spectrum sensing in a noise uncertain environment is treacherous and it requires very careful detection algorithm. Noise uncertainty will make the detector unreliable due to the SNR walls. So, a double threshold method is used to improve the detection performance where each secondary user will use two thresholds detector for detection. The thresholds are calculated according to the noise uncertainty at each secondary user. Here, we have worked on and analyzed the results of traditional energy detector in a noise uncertain environment using double threshold with the aid of co-operative sensing. The unsuitability of the traditional energy detector is observed from the analysis and we propose a dual threshold based optimum power energy detection algorithm suitable under noise uncertainty.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324183","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996157
K. Manikandan
Computer Aided Diagnosis (CAD) acts as a primary tool for the radiologists to have a second opinion for identifying whether the lung is affected by any abnormalities or not. Lung image segmentation and classification plays a vital role in CAD system. Despite many ongoing researches, lung image segmentation has still scope for improvement in terms of accuracy and automation. The proposed blob based segmentation aims to improve the segmentation of the lung image from chest CT in terms of sensitivity and accuracy. Blob based segmentation consists of three important processing stages, in preprocessing stage an automatic thresholding method is used to separate the lung image from background image; in second stage, segmentation of left and right lungs are carried out based on intensity value. Finally, Region of Interest (ROI) is identified from lung image and results are classified using a Neuro Fuzzy Classifier. On comparing with existing methods, the proposed method achieves good result in terms of accuracy and sensitivity.
{"title":"Blob based segmentation for lung CT image to improving CAD performance","authors":"K. Manikandan","doi":"10.1109/ICRTIT.2014.6996157","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996157","url":null,"abstract":"Computer Aided Diagnosis (CAD) acts as a primary tool for the radiologists to have a second opinion for identifying whether the lung is affected by any abnormalities or not. Lung image segmentation and classification plays a vital role in CAD system. Despite many ongoing researches, lung image segmentation has still scope for improvement in terms of accuracy and automation. The proposed blob based segmentation aims to improve the segmentation of the lung image from chest CT in terms of sensitivity and accuracy. Blob based segmentation consists of three important processing stages, in preprocessing stage an automatic thresholding method is used to separate the lung image from background image; in second stage, segmentation of left and right lungs are carried out based on intensity value. Finally, Region of Interest (ROI) is identified from lung image and results are classified using a Neuro Fuzzy Classifier. On comparing with existing methods, the proposed method achieves good result in terms of accuracy and sensitivity.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064496","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996176
Anbazhagi, L. Tamilselvan, Shakkeera
Cloud Computing is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources between the service provider and consumers. In cloud computing, many tasks are to be executed by the available services to achieve better performance, minimum total time for completion, shortest response time, utilization of resources etc. Because of these different intentions, we need to propose a scheduling algorithm to perform appropriate allocation map of tasks on resources. In existing system task scheduling algorithm have been designed based on priority and total completion time in cloud computing. The task scheduling algorithm first computes the priority of the tasks based on the inputs of the users and then sorts the tasks by priority. Second, this algorithm calculates the minimum completion time of all the tasks on different resources and schedules onto a resources accordingly. The drawbacks in existing system are, it does not effectively use the idle resources. In this paper we proposed a dynamic scheduling algorithm that efficiently uses the idle time of resources from monitoring the task timing information on resources. The multi-dimensional cost matrix table is developed based on execution time, CPU usage of each tasks and current CPU usage of resources and also we have extended the deadline time value using min-max policies to complete the tasks within a earlier time period. In this paper, we have considered deadline, idle time and reliability as QoS parameters for scheduling.
{"title":"QoS based dynamic task scheduling in IaaS cloud","authors":"Anbazhagi, L. Tamilselvan, Shakkeera","doi":"10.1109/ICRTIT.2014.6996176","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996176","url":null,"abstract":"Cloud Computing is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources between the service provider and consumers. In cloud computing, many tasks are to be executed by the available services to achieve better performance, minimum total time for completion, shortest response time, utilization of resources etc. Because of these different intentions, we need to propose a scheduling algorithm to perform appropriate allocation map of tasks on resources. In existing system task scheduling algorithm have been designed based on priority and total completion time in cloud computing. The task scheduling algorithm first computes the priority of the tasks based on the inputs of the users and then sorts the tasks by priority. Second, this algorithm calculates the minimum completion time of all the tasks on different resources and schedules onto a resources accordingly. The drawbacks in existing system are, it does not effectively use the idle resources. In this paper we proposed a dynamic scheduling algorithm that efficiently uses the idle time of resources from monitoring the task timing information on resources. The multi-dimensional cost matrix table is developed based on execution time, CPU usage of each tasks and current CPU usage of resources and also we have extended the deadline time value using min-max policies to complete the tasks within a earlier time period. In this paper, we have considered deadline, idle time and reliability as QoS parameters for scheduling.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004537","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 : 2014-04-10DOI: 10.1109/ICRTIT.2014.6996149
P. Dhanalakshmi, S. Divya, S. Kamila, K. Muthumeenakshi, S. Radha
In Cognitive Radio network, the Secondary Users (SU) makes use of the spectrum of licensed primary user when they are unoccupied. Here we have studied and expressed the normalized throughput for both single secondary user and multiple secondary users coexisting under cooperative sensing technique. We have also incorporated the consequences due to imperfect sensing and analyzed the throughput under multiple primary users. The general frame structure of SU consists of slots for both sensing and data transmission. The accuracy of cooperative sensing depends on the sensing time and the number of participating SUs in the network. We have also analyzed various cooperative sensing schemes and have proposed optimal K-of-N rule as the efficient one and thus enhanced the throughput.
{"title":"Modeling and analysis of Cognitive Radio with multiple primary/Secondary users and imperfect sensing","authors":"P. Dhanalakshmi, S. Divya, S. Kamila, K. Muthumeenakshi, S. Radha","doi":"10.1109/ICRTIT.2014.6996149","DOIUrl":"https://doi.org/10.1109/ICRTIT.2014.6996149","url":null,"abstract":"In Cognitive Radio network, the Secondary Users (SU) makes use of the spectrum of licensed primary user when they are unoccupied. Here we have studied and expressed the normalized throughput for both single secondary user and multiple secondary users coexisting under cooperative sensing technique. We have also incorporated the consequences due to imperfect sensing and analyzed the throughput under multiple primary users. The general frame structure of SU consists of slots for both sensing and data transmission. The accuracy of cooperative sensing depends on the sensing time and the number of participating SUs in the network. We have also analyzed various cooperative sensing schemes and have proposed optimal K-of-N rule as the efficient one and thus enhanced the throughput.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114875880","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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