Performance analysis of resource-aware framework classification, clustering and frequent items in wireless sensor networks

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Jumadi M. Parenreng, Muhammad Ilyas Syarif, Supeno Djanali, Ary Masharuddin Shiddiqi

2011 Proceeding of the International Conference on e-Education Entertainment and e-Management, ICEEE 2011 Conference paper Cited by 5

Abstract

Reliability device Wireless Sensor Network (WSN) can be measured through the effective utilization of energy in the form of battery, memory and CPU. The source energy became a major part of the WSN so that the required energy efficiency techniques to maximize the performance. In the process, implemented energy efficiency carried out by maximizing the process of selection of data to be processed and stored as raw data by applying the concept data mining of existing data. The implementation done by applying an algorithm that is resource-aware framework with Light Weight Classification (LWClass), Light Weight Frequent Item (LWF) and Light Weight Clustering (LWCluster). From the three forms of efficiency of the algorithm is obtained with a value efesiensi pada LWClass, LWF, and algorithms LWCluster each have an efficiency of 14.32%, 15.88% and 17.71%. Then usability of Resource Aware (RA) is proven to improve the efficiency and lifetime of a network of WSNs, reaching 14-17% and 10-11 hours. © 2011 IEEE.

Affiliations

Information Engineering and Computer Education, State University of Makassar, Makassar, Indonesia; Electrical Engineering, State Polytechnic of Ujung Pandang, Makassar, Indonesia; Information Engineering, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia