Informatics Publishing Limited

Gaurav Kumar Nigam ¹, Chetna Dabas ¹

Affiliations
1 Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida - 201301, Uttar Pradesh, IN

Abstract

Wireless sensor Networks consists of a huge number of little powered nodes typically in the range of hundreds to thousands in number that are multifunctional and randomly deployed in a hostile environment. If a node detects an abnormal event, it will automatically send a hop by hop warning message to the sink node. There are various challenges and design issues in WSN like node deployment, routing, energy consumption, clustering, fault tolerance, coverage, connectivity and QoS i.e. Quality of Service. The author proposed a clustering approach that adopts a hybrid Compressive Sensing (CS) for sensor networks. The method compares the number of transmissions in the data aggregation techniques commonly used: Shortest path tree with hybrid compressive sensing, clustering without compressive sensing, optimal tree with hybrid compressive sensing, shortest path tree without compressive sensing, clustering with hybrid compressive sensing. Compressive sensing uses the largely similar data of large scale Wireless sensor networks and aims to minimize data transmissions without compromising the precision of the result obtained from data. The author finds that in comparison to these methods the proposed approach can reduce the data transmissions.

Keywords

Cluster Head, Compressive Sensing, Shortest Path Tree, Wireless Sensor Networks.

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Anubhuti Singh ¹, Chetna Dabas ¹, J. P. Gupta ¹

Affiliations
1 Jaypee Institute of Information and Technology, Noida - 201301, Uttar Pradesh, IN

Abstract

Objectives: To address a pair wise text comparison of large dataset while making use of cosine similarity metric and adjacent method and to develop a model for parallel processing of giant data while using distributed algorithms on parallel clusters. Methods/Statistical Analysis: This works makes use of K-means algorithm based on map-reduce on document files with effective number of clusters in a Java environment. This work reflects an approach to classify text documents using feature selection method makes use of cosine similarity method. Within fixed number of iterations, efficient numbers of clusters have been implemented. The implementation has been carried out in Java environment. Findings: The proposed work reflects an approach to classify text documents using feature selection method. Application/Improvements: While using cosine similarity methods, the results retrieved are quite improved and acceptable.

Keywords

Cosine Similarity, Document Files, Text Clustering

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