Pub Date : 2017-02-01DOI: 10.1109/ICCCT2.2017.7972315
K. Joshitha, A. Gangasri
The objective of the proposed system is to develop an adaptive iterative linear regression (ILR) based clustering for wireless sensor network. ILR classifies the initial cluster simultaneously in horizontal and vertical patterns to form two sub clusters. Among these two, the best is selected based on similarity index (SI). This selected cluster is taken as reference and the iteration continues until the convergence criteria ‘Delta’ is met. The cluster quality is evaluated using internal and external indices and then compared with existing k-means and hierarchical clustering. The performance indices confirm the supremacy of the ILR clustering.
{"title":"Regression based cluster formation for enhancement of lifetime of WSN","authors":"K. Joshitha, A. Gangasri","doi":"10.1109/ICCCT2.2017.7972315","DOIUrl":"https://doi.org/10.1109/ICCCT2.2017.7972315","url":null,"abstract":"The objective of the proposed system is to develop an adaptive iterative linear regression (ILR) based clustering for wireless sensor network. ILR classifies the initial cluster simultaneously in horizontal and vertical patterns to form two sub clusters. Among these two, the best is selected based on similarity index (SI). This selected cluster is taken as reference and the iteration continues until the convergence criteria ‘Delta’ is met. The cluster quality is evaluated using internal and external indices and then compared with existing k-means and hierarchical clustering. The performance indices confirm the supremacy of the ILR clustering.","PeriodicalId":445567,"journal":{"name":"2017 2nd International Conference on Computing and Communications Technologies (ICCCT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654946","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 : 2017-02-01DOI: 10.1109/ICCCT2.2017.7972271
S. Prathiba, S. Sowvarnica
Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.
{"title":"Survey of failures and fault tolerance in cloud","authors":"S. Prathiba, S. Sowvarnica","doi":"10.1109/ICCCT2.2017.7972271","DOIUrl":"https://doi.org/10.1109/ICCCT2.2017.7972271","url":null,"abstract":"Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.","PeriodicalId":445567,"journal":{"name":"2017 2nd International Conference on Computing and Communications Technologies (ICCCT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131366721","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 : 2017-02-01DOI: 10.1109/ICCCT2.2017.7972286
V. Akila, T. Sheela
Reliable and trustful data aggregation is needed for most application in Wireless Sensor Networks. In this paper, we propose a new approach to attain data and key privacy protection in data aggregation called Preserving Data and Key Privacy in Data Aggregation for Wireless Sensor Networks (PDKP).Existing privacy preserving protocol provide more computational and communicational overhead in the sensor nodes. It increases the consumption of energy among the nodes. In our scheme, the encrypted content of the data is shared without revealing the data and key to other nodes by using simple technique. It preserves the key and data from an adversary with less computational and communication overhead. The base station can identify the distrustful groups related to the set of group aggregates and the retransmission of data is performed only for the abnormal data sensing intermediate nodes. It preserves the various security issues such as data freshness, data integrity, data confidentiality in data aggregation. Our simulation result showed that implementation of PDKP reduces the communication overhead and increases energy efficiency.
{"title":"Preserving data and key privacy in Data Aggregation for Wireless Sensor Networks","authors":"V. Akila, T. Sheela","doi":"10.1109/ICCCT2.2017.7972286","DOIUrl":"https://doi.org/10.1109/ICCCT2.2017.7972286","url":null,"abstract":"Reliable and trustful data aggregation is needed for most application in Wireless Sensor Networks. In this paper, we propose a new approach to attain data and key privacy protection in data aggregation called Preserving Data and Key Privacy in Data Aggregation for Wireless Sensor Networks (PDKP).Existing privacy preserving protocol provide more computational and communicational overhead in the sensor nodes. It increases the consumption of energy among the nodes. In our scheme, the encrypted content of the data is shared without revealing the data and key to other nodes by using simple technique. It preserves the key and data from an adversary with less computational and communication overhead. The base station can identify the distrustful groups related to the set of group aggregates and the retransmission of data is performed only for the abnormal data sensing intermediate nodes. It preserves the various security issues such as data freshness, data integrity, data confidentiality in data aggregation. Our simulation result showed that implementation of PDKP reduces the communication overhead and increases energy efficiency.","PeriodicalId":445567,"journal":{"name":"2017 2nd International Conference on Computing and Communications Technologies (ICCCT)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114715098","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 : 2017-02-01DOI: 10.1109/ICCCT2.2017.7972258
T. Mangayarkarasi, D. N. Jamal
In this paper, a computer assistive tool is proposed to Process and analyse ultrasound Kidney Images for the classification of Renal Pathologies. The Ultrasound Kidney Images are classified into four classes: Normal, Cyst, Calculi and Tumor. Scanned Kidney Ultra-Sound (US) Images are obtained and Knowledge pertaining to common Pathologies from an Urologist Perspective is utilized as inputs to carry out the classification. The Images are preprocessed for the removal of Speckle noises by applying Median and Gaussian filter. Optimal thresholding segmentation algorithm is used to obtain the region of Interest. A set of first order statistical features are extracted. These features are given as inputs for training and testing the probabilistic neural network classifier. Hold out method is adopted where in 50% images are used for training and remaining 50% images are used for testing. The efficiency of the classifier is finally evaluated. A classification rate of 93.5% is obtained. The results achieved, are based on performance metrics calculations and are highly satisfactory.
{"title":"PNN-based analysis system to classify renal pathologies in Kidney Ultrasound Images","authors":"T. Mangayarkarasi, D. N. Jamal","doi":"10.1109/ICCCT2.2017.7972258","DOIUrl":"https://doi.org/10.1109/ICCCT2.2017.7972258","url":null,"abstract":"In this paper, a computer assistive tool is proposed to Process and analyse ultrasound Kidney Images for the classification of Renal Pathologies. The Ultrasound Kidney Images are classified into four classes: Normal, Cyst, Calculi and Tumor. Scanned Kidney Ultra-Sound (US) Images are obtained and Knowledge pertaining to common Pathologies from an Urologist Perspective is utilized as inputs to carry out the classification. The Images are preprocessed for the removal of Speckle noises by applying Median and Gaussian filter. Optimal thresholding segmentation algorithm is used to obtain the region of Interest. A set of first order statistical features are extracted. These features are given as inputs for training and testing the probabilistic neural network classifier. Hold out method is adopted where in 50% images are used for training and remaining 50% images are used for testing. The efficiency of the classifier is finally evaluated. A classification rate of 93.5% is obtained. The results achieved, are based on performance metrics calculations and are highly satisfactory.","PeriodicalId":445567,"journal":{"name":"2017 2nd International Conference on Computing and Communications Technologies (ICCCT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123779934","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 : 2017-02-01DOI: 10.1109/ICCCT2.2017.7972301
Swati V, T. P. Rani
The heterogeneous Internet of things bridges together multiple devices with different capabilities, functionalities, configurations, platforms, varieties and multiple users with different roles advocating its ubiquity and thereby, exposing the security risks faced by devices and users. Security is paramount for the safe, reliable operations of IoT connected devices and it is the foundational enabler for IoT. The cryptographic mechanisms are one of the means to provide security. The cryptographic schemes must be strong enough to meet the security requirements but, at the same time, they must meet the limitations in the resource constrained networks. This project focuses on efficient message deliveries among resource-constrained devices in IoT using the ElGamal Signature Scheme to enable the protection of user data. ElGamal signature scheme make use of (i) complexity in computing discrete logarithms, (ii) secure end-to-end communication based on session resumption and (iii) proves its power by use of random number generation. The proposed model employs data encryption rather than key exchanges in order to avoid overhead in sending large number of packets between energy-constrained sensors.
{"title":"Clustered security for Smart Networks","authors":"Swati V, T. P. Rani","doi":"10.1109/ICCCT2.2017.7972301","DOIUrl":"https://doi.org/10.1109/ICCCT2.2017.7972301","url":null,"abstract":"The heterogeneous Internet of things bridges together multiple devices with different capabilities, functionalities, configurations, platforms, varieties and multiple users with different roles advocating its ubiquity and thereby, exposing the security risks faced by devices and users. Security is paramount for the safe, reliable operations of IoT connected devices and it is the foundational enabler for IoT. The cryptographic mechanisms are one of the means to provide security. The cryptographic schemes must be strong enough to meet the security requirements but, at the same time, they must meet the limitations in the resource constrained networks. This project focuses on efficient message deliveries among resource-constrained devices in IoT using the ElGamal Signature Scheme to enable the protection of user data. ElGamal signature scheme make use of (i) complexity in computing discrete logarithms, (ii) secure end-to-end communication based on session resumption and (iii) proves its power by use of random number generation. The proposed model employs data encryption rather than key exchanges in order to avoid overhead in sending large number of packets between energy-constrained sensors.","PeriodicalId":445567,"journal":{"name":"2017 2nd International Conference on Computing and Communications Technologies (ICCCT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869487","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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