TY - GEN
T1 - Performance analysis of resource-aware framework classification, clustering and frequent items in wireless sensor networks
AU - Parenreng, Jumadi M.
AU - Syarif, Muhammad Ilyas
AU - Djanali, Supeno
AU - Shiddiqi, Ary Masharuddin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Data Mining
KW - Resource-Aware
KW - WSN
UR - http://www.scopus.com/inward/record.url?scp=84857316406&partnerID=8YFLogxK
U2 - 10.1109/ICeEEM.2011.6137858
DO - 10.1109/ICeEEM.2011.6137858
M3 - Conference contribution
AN - SCOPUS:84857316406
SN - 9781457713811
T3 - Proceeding of the International Conference on e-Education Entertainment and e-Management, ICEEE 2011
SP - 117
EP - 120
BT - Proceeding of the International Conference on e-Education Entertainment and e-Management, ICEEE 2011
T2 - 2011 International Conference on e-Education, Entertainment and e-Management, ICEEE 2011
Y2 - 27 December 2011 through 29 December 2011
ER -