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

Closed

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 Quartile

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

Research at a Glance

Premium content — register to unlock

Research at a Glance

Register to unlock

Topics & SDG Alignment

Premium content — register to unlock

Topics & SDG Alignment

Register to unlock

Collaboration

Premium content — register to unlock

Collaboration

Register to unlock

Author Profile (Selected)

Premium content — register to unlock

Author Profile (Selected)

Register to unlock

References Overview

Premium content — register to unlock

References Overview

Register to unlock

Journal & Source

Premium content — register to unlock

Journal & Source

Register to unlock

Metadata & Integrity

Premium content — register to unlock

Metadata & Integrity

Register to unlock