Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208346
S. Janakiraman, N. Suriya, V. Nithiya, B. Radhakrishnan, J. Ramanathan, Rengarajan Amirtharajan
The advent of rapid growth of the Internet has ascertained the hidden communication with its focus on security that has gained increasing importance. Of the various methods for establishing hidden communication, one important method is Steganography where the very existence of the data is concealed. Here, the embedding of secret data is varied by employing block based segmentation and thus, Steganography is performed. Categorization of the cover image is done with the help of a reference point and thereby, based on the variation in the MSB bit plane, the secret data is hidden. The proposed method will increase the complexity and the embedding capacity of the image and thus proving to be more efficient by the usage of utmost two or three bits for embedding the secret information in a cover pixel.
{"title":"Reflective code for gray block embedding","authors":"S. Janakiraman, N. Suriya, V. Nithiya, B. Radhakrishnan, J. Ramanathan, Rengarajan Amirtharajan","doi":"10.1109/ICPRIME.2012.6208346","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208346","url":null,"abstract":"The advent of rapid growth of the Internet has ascertained the hidden communication with its focus on security that has gained increasing importance. Of the various methods for establishing hidden communication, one important method is Steganography where the very existence of the data is concealed. Here, the embedding of secret data is varied by employing block based segmentation and thus, Steganography is performed. Categorization of the cover image is done with the help of a reference point and thereby, based on the variation in the MSB bit plane, the secret data is hidden. The proposed method will increase the complexity and the embedding capacity of the image and thus proving to be more efficient by the usage of utmost two or three bits for embedding the secret information in a cover pixel.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651961","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208354
M. S. Kumar, Y. S. Kumaraswamy
Digital medical images take up most of the storage space in the medical database. Digital images are in the form of X-Rays, MRI, CT. These medical images are extensively used in diagnosis and planning treatment schedule. Retrieving required medical images from the database in an efficient manner for diagnosis, research and educational purposes is essential. Image retrieval systems are used to retrieve similar images from database by inputting a query image. Image retrieval systems extract features in the image to a feature vector and use similarity measures for retrieval of images from the database. So the efficiency of the image retrieval system depends upon the feature selection and its classification. In this paper, it is proposed to implement a novel feature selection mechanism using Discrete Sine Transforms (DST) with Information Gain for feature reduction. Classification results obtained from existing Support Vector Machine (SVM) is compared with the proposed Support Vector Machine model. Results obtained show that the proposed SVM classifier outperforms conventional SVM classifier and multi layer perceptron neural network.
{"title":"An improved support vector machine kernel for medical image retrieval system","authors":"M. S. Kumar, Y. S. Kumaraswamy","doi":"10.1109/ICPRIME.2012.6208354","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208354","url":null,"abstract":"Digital medical images take up most of the storage space in the medical database. Digital images are in the form of X-Rays, MRI, CT. These medical images are extensively used in diagnosis and planning treatment schedule. Retrieving required medical images from the database in an efficient manner for diagnosis, research and educational purposes is essential. Image retrieval systems are used to retrieve similar images from database by inputting a query image. Image retrieval systems extract features in the image to a feature vector and use similarity measures for retrieval of images from the database. So the efficiency of the image retrieval system depends upon the feature selection and its classification. In this paper, it is proposed to implement a novel feature selection mechanism using Discrete Sine Transforms (DST) with Information Gain for feature reduction. Classification results obtained from existing Support Vector Machine (SVM) is compared with the proposed Support Vector Machine model. Results obtained show that the proposed SVM classifier outperforms conventional SVM classifier and multi layer perceptron neural network.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129161360","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208368
B. M. Vaganan, D. Pandiaraja, S. Sundar, E. E. Priya
Chronic obstructive pulmonary disease (COPD) is associated with the respiratory system. COPD is often treated with inhalers whose two major ingredients are the bronchodilators and the steroids. In this paper we mathematically model the deposition of the inhaled drug on the infected airway into Cauchy-Euler differential equation and use Visual Basic to simulate the evolution of the recovery of the inflamed airway.
{"title":"Cauchy-Euler model, cellular automata simulation of the rate of recovery of the infected airway from COPD","authors":"B. M. Vaganan, D. Pandiaraja, S. Sundar, E. E. Priya","doi":"10.1109/ICPRIME.2012.6208368","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208368","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD) is associated with the respiratory system. COPD is often treated with inhalers whose two major ingredients are the bronchodilators and the steroids. In this paper we mathematically model the deposition of the inhaled drug on the infected airway into Cauchy-Euler differential equation and use Visual Basic to simulate the evolution of the recovery of the inflamed airway.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130554564","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208288
T. Kathirvalavakumar, S. J. Subavathi
In this paper, a new adaptive learning rate algorithm to train a single hidden layer neural network is proposed. The adaptive learning rate is derived by differentiating linear and nonlinear errors and functional constraints weight decay term at hidden layer and penalty term at output layer. Since the adaptive learning rate calculation involves first order derivative of linear and nonlinear errors and second order derivatives of functional constraints, the proposed algorithm converges quickly. Simulation results show the advantages of proposed algorithm.
{"title":"Modified backpropagation algorithm with adaptive learning rate based on differential errors and differential functional constraints","authors":"T. Kathirvalavakumar, S. J. Subavathi","doi":"10.1109/ICPRIME.2012.6208288","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208288","url":null,"abstract":"In this paper, a new adaptive learning rate algorithm to train a single hidden layer neural network is proposed. The adaptive learning rate is derived by differentiating linear and nonlinear errors and functional constraints weight decay term at hidden layer and penalty term at output layer. Since the adaptive learning rate calculation involves first order derivative of linear and nonlinear errors and second order derivatives of functional constraints, the proposed algorithm converges quickly. Simulation results show the advantages of proposed algorithm.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131018048","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208365
R. Roselin, K. Thangavel
The mammography is the most effective procedure for to diagnosis the breast cancer at an early stage. A granule is a mass of objects, in the universe of discourse, put together by indistinguishability, similarity, proximity, or functionality. In mammograms, it is quite difficult to identify the suspicious region which is a mass of calcification on the breast tissue. This paper proposes rough entropy based granular computing to segment mammogram images. The proposed method is evaluated by classification algorithms which are available in WEKA.
{"title":"Mammogram image segmentation using granular computing based on rough entropy","authors":"R. Roselin, K. Thangavel","doi":"10.1109/ICPRIME.2012.6208365","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208365","url":null,"abstract":"The mammography is the most effective procedure for to diagnosis the breast cancer at an early stage. A granule is a mass of objects, in the universe of discourse, put together by indistinguishability, similarity, proximity, or functionality. In mammograms, it is quite difficult to identify the suspicious region which is a mass of calcification on the breast tissue. This paper proposes rough entropy based granular computing to segment mammogram images. The proposed method is evaluated by classification algorithms which are available in WEKA.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662694","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208355
R. Harini, C. Chandrasekar
Image Segmentation is one of the significant elements in the part of image processing. It becomes most essential demanding factor while typically dealing with medical image segmentation. In this paper, proposal of our work comprises of formation of kernel for the medical images by performing the deviation of mapped image data within the scope of each region from the piecewise constant model and based on the regularization term based on the function of indices value of the region. The functional objective minimization is carried out by two steps minimization in image segmentation using graph cut methods, and minimization with respect to region parameters using constant point computation. Nearest neighbor classifiers are introduced to the benchmarked image data segmented portions. Among the different methods in supervised statistical pattern recognition, the nearest neighbor rule results in achieving high performance without requirement of the prior assumptions about the distributions from which the training sets are taken.
{"title":"Image segmentation using nearest neighbor classifiers based on kernel formation for medical images","authors":"R. Harini, C. Chandrasekar","doi":"10.1109/ICPRIME.2012.6208355","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208355","url":null,"abstract":"Image Segmentation is one of the significant elements in the part of image processing. It becomes most essential demanding factor while typically dealing with medical image segmentation. In this paper, proposal of our work comprises of formation of kernel for the medical images by performing the deviation of mapped image data within the scope of each region from the piecewise constant model and based on the regularization term based on the function of indices value of the region. The functional objective minimization is carried out by two steps minimization in image segmentation using graph cut methods, and minimization with respect to region parameters using constant point computation. Nearest neighbor classifiers are introduced to the benchmarked image data segmented portions. Among the different methods in supervised statistical pattern recognition, the nearest neighbor rule results in achieving high performance without requirement of the prior assumptions about the distributions from which the training sets are taken.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127004654","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208341
S. Nithya, Rekha C Chandrasekar, R. Kaniezhil
Mobile Ad-hoc Network (MANET) is the self organizing collection of mobile nodes. Ad hoc wireless networks have massive commercial and military potential because of their mobility support. Quality of Service (QoS) routing in mobile Ad-Hoc networks is challenging due to rapid change in network topology. In this paper, we focused to reduce flooding performance of the Fisheye State Routing (FSR) protocol in Grid using ns-2 network simulator under different performance metrics scenario in respect to number of Nodes and Pause-Time. The connection establishment is costly in terms of time and resource where the network is mostly affected by connection request flooding. The proposed approach presents a way to reduce flooding in MANETs. Flooding is dictated by the propagation of connection-request packets from the source to its neighborhood nodes. The proposed architecture promotes on the concept of sharing neighborhood information. The proposed approach focuses on exposing its neighborhood peer to another node that is referred to as its friend-node, which had requested/forwarded connection request. If there is a high probability for the friend node to communicate through the exposed routes, this could improve the efficacy of bandwidth utilization by reducing flooding, as the routes have been acquired, without any broadcasts. Friendship between nodes is quantized based on empirical computations and heuristic algorithms. The nodes store the neighborhood information in their cache that is periodically verified for consistency. Simulation results show the performance of this proposed method.
{"title":"A new approach to reduce flooding in Grid Fisheye state routing (GFSR) protocol by propagation neighborhood","authors":"S. Nithya, Rekha C Chandrasekar, R. Kaniezhil","doi":"10.1109/ICPRIME.2012.6208341","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208341","url":null,"abstract":"Mobile Ad-hoc Network (MANET) is the self organizing collection of mobile nodes. Ad hoc wireless networks have massive commercial and military potential because of their mobility support. Quality of Service (QoS) routing in mobile Ad-Hoc networks is challenging due to rapid change in network topology. In this paper, we focused to reduce flooding performance of the Fisheye State Routing (FSR) protocol in Grid using ns-2 network simulator under different performance metrics scenario in respect to number of Nodes and Pause-Time. The connection establishment is costly in terms of time and resource where the network is mostly affected by connection request flooding. The proposed approach presents a way to reduce flooding in MANETs. Flooding is dictated by the propagation of connection-request packets from the source to its neighborhood nodes. The proposed architecture promotes on the concept of sharing neighborhood information. The proposed approach focuses on exposing its neighborhood peer to another node that is referred to as its friend-node, which had requested/forwarded connection request. If there is a high probability for the friend node to communicate through the exposed routes, this could improve the efficacy of bandwidth utilization by reducing flooding, as the routes have been acquired, without any broadcasts. Friendship between nodes is quantized based on empirical computations and heuristic algorithms. The nodes store the neighborhood information in their cache that is periodically verified for consistency. Simulation results show the performance of this proposed method.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116960098","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208282
A. Banumathi, A. Pethalakshmi
Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it's difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.
聚类是一种广泛应用于数据挖掘的技术，用于在大数据集中发现模式。本文对模糊c均值算法进行了分析，发现聚类结果的质量取决于初始种子，初始种子的选择可以是顺序的，也可以是随机的。模糊C-Means采用K-Means聚类方法进行聚类的初始操作，然后计算隶属度。模糊C-Means与K-Means算法非常相似，因此本文概述了K-Means算法，并证明了如何通过UCAM (Unique Clustering with Affinity Measure)聚类算法纠正K-Means算法的缺点，然后对UCAM进行改进，给出了一种新的观点，即Fuzzy-UCAM。模糊C-均值算法的初始化需要有聚类C的个数和初始种子的个数。对于实时的大型数据库，很难准确地预测聚类和初始种子的数量。为了克服这一缺点，本文重点研究了不给出模糊c均值初始种子和簇数的Fuzzy- ucam聚类算法。利用亲和度量获得了唯一的聚类。
{"title":"Increasing cluster uniqueness in Fuzzy C-Means through affinity measure","authors":"A. Banumathi, A. Pethalakshmi","doi":"10.1109/ICPRIME.2012.6208282","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208282","url":null,"abstract":"Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it's difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"341 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955953","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208291
E. K. Nesamalar, C. P. Chandran
In this paper Flexible Protein Ligand Docking is carried out using Genetic Clustering with Bee Colony Optimization. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. A Genetic clustering algorithm combine a Genetic Algorithm (GA) with the K-medians clustering algorithm. GA is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a new Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, we propose a new algorithm called Genetic clustering Bee Colony Optimization (GCBCO). The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.
{"title":"Genetic clustering with Bee Colony Optimization for flexible protein-ligand docking","authors":"E. K. Nesamalar, C. P. Chandran","doi":"10.1109/ICPRIME.2012.6208291","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208291","url":null,"abstract":"In this paper Flexible Protein Ligand Docking is carried out using Genetic Clustering with Bee Colony Optimization. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. A Genetic clustering algorithm combine a Genetic Algorithm (GA) with the K-medians clustering algorithm. GA is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a new Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, we propose a new algorithm called Genetic clustering Bee Colony Optimization (GCBCO). The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893305","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208296
K. R. Nanthagobal, C. Chandrasekar
The service discovery mechanism in next generation wireless network should be flexible to both location and environment change of the user which can be achieved by appropriately predicting the user mobility. As a result, effective user mobility prediction technique need to be designed for offering the services without affecting the user location. In this paper, we propose a location aware service discovery protocol in next generation wireless networks. This technique consists of three phases: Handoff triggering based on received signal strength of the base station (BS), Client mobility prediction as per its velocity and direction, BS selection with maximum available bandwidth and residual power. By simulation results, we show that our proposed approach minimizes the query latency.
{"title":"Location-aware service discovery in next generation wireless networks","authors":"K. R. Nanthagobal, C. Chandrasekar","doi":"10.1109/ICPRIME.2012.6208296","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208296","url":null,"abstract":"The service discovery mechanism in next generation wireless network should be flexible to both location and environment change of the user which can be achieved by appropriately predicting the user mobility. As a result, effective user mobility prediction technique need to be designed for offering the services without affecting the user location. In this paper, we propose a location aware service discovery protocol in next generation wireless networks. This technique consists of three phases: Handoff triggering based on received signal strength of the base station (BS), Client mobility prediction as per its velocity and direction, BS selection with maximum available bandwidth and residual power. By simulation results, we show that our proposed approach minimizes the query latency.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130107520","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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