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

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.