Informatics Publishing Limited

Pawan Kumar Tanwar ¹, Ajay Khunteta ², Vishal Goar ³

Affiliations
1 Department of Computer Science, Poornima University, Jaipur, IN
2 Department Computer Science, Poornima University, Jaipur, IN
3 Department of Computer Application, Govt. Engineering College, Bikaner, IN

Abstract

Nowadays, Everything can be searched through the cloud space. Therefore, this article is also dealing with the cloud computing, where searching of information and preservation of privacy is key area of concern. Henceforth taking multi-keyword ranked search with dynamic updation as a dimension of information searching, have been selected it as a research area. Moreover the searching is restricted to single keyword only. Therefore, we have taken the concept of multi keyword ranked searching. One more thing is that efforts have not been made regarding dynamic updation (insertion and deletion etc. of documents) previously. To cover up the dynamic updation part the schemes BDMRS-CM (Basic Dynamic Multi-Keyword Ranked Search scheme in the Known Ciphertext Model) by using the secure kNN algorithm and the EDMRS-BM (Enhanced Dynamic Multi-Keyword Ranked Search scheme in the Known Background Model) were designed and their performance have been evaluated and analyzed.

Keywords

Cloud Computing, Dynamic Updation, Multi-Keyword.

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Pawan Kumar Tanwar ¹, Ajay Khunteta ¹, Vishal Goar ²

Affiliations
1 Department of Computer Science, Poornima University, Jaipur, IN
2 Department of Computer Application, Govt. Engineering College, Bikaner, IN

Abstract

In these days, required information can be searched through the cloud. Henceforth, this paper deals with the cloud computing, where information seeking and privacy preservation are the main area of emphasis. Therefore, keeping multi-keyword ranked search with dynamic updation as an area of data searching (information seeking), have been chosen here as a research dimension. One more thing is that the in most of the cases searching is bounded to unit keyword only. Hence, we have chosen the other dimension of searching which is multi keyword ranking search. Moreover, efforts have not been made in the area of dynamic updation earlier. Here dynamic updation caters the addition and deletion of documents dynamically. For covering the dynamic updation part the scheme SSEDU (searchable symmetric encryption with dynamic updation) has been designed and deployed. In this paper we have presented the efforts made for the performance evaluation of the said SSEDU scheme.

Keywords

Cloud Computing, Dynamic Updation, Multi-Keyword Ranked Search, SSEDU.

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Ajay Khunteta ¹, Anurag Bajpai ¹

Affiliations
1 Department of Electronics Engineering, Rajasthan Technical University, Kota, IN

Abstract

Wireless Sensor Network (WSN) is the deployed randomly and on the far places to sense information. The security, quality of service and energy consumption is the major issues of WSN. To minimize the consumption of higher amount of energy in these networks, clustering is applied. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for the cluster head selection in the network. The cluster head is selected based on the energy consumption and distance to base station. A node is selected as Cluster Head (CH) if it has the highest amount of energy and the least distance from base station. In this research work, genetic algorithm is applied with the LEACH protocol for the cluster head selection. The proposed work is implemented in Matlab. The results of existing LEACH protocol is compared with proposed LEACH protocol in terms of certain parameters. The comparative analysis and achieved outcomes show that the proposed approach performs well in terms of energy consumption as it consumes lesser amount of energy.

Keywords

LEACH, Clustering, Genetic Algorithm, Cluster Head.

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Jyoti Lakhani , Ajay Khunteta , Anupama Chowdhary , Dharmesh Harwani

Abstract

Clustering is an unsupervised technique that is used when the information about data is inadequate. Clustering algorithms identify clusters in the dataset and acquire information on the number of clusters to be identified as a prior parameter. In the present work, an adaptive evolutionary clustering algorithm (AECRAM), which evolves automatically, was tested to identify the number of clusters in a multivariate dataset. It is important to test whether the identified clusters are actual clusters or not, and if difference exists in them, then to find out these differences, relative to their actual clusters using cluster validity indexes. Using adaptive evolutionary algorithm cluster validation was done in this work and compared with K-means and EKM algorithms for their relative efficiency. The observations made in the study clearly indicated that the proposed adaptive clustering algorithm could detect correct and accurate number of clusters in the multivariate datasets. The quality of the clusters identified by the AECRAM has been found to be superior to the other two algorithms used. The present study is also useful in other research where relative cluster validations among adaptive evolutionary clustering algorithms are required.

Keywords

Cluster, Validity Index, Dunn Index, Malignant, Benign.

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