Enhanced Teaching Learning Based Optimization for Localization in Wireless Sensor Network

Wireless Sensor Networks (WSN) is a non-wired infrastructure implemented in a huge number of nodes which can sense the environment such as humidity, temperature, sound, and many others. The main issue in Wireless Sensor Networks is to determine the position of nodes called the localization. The issue in localization is gained high localization error in estimating node’s location. Previous studies have introduced many meta-heuristic algorithms to solve this issue. However, gaining the lower localization error is still being discovered by the researcher. This research is to introduce Enhanced Teaching Learning Based Optimization (ETLBO) algorithm for node localization using three anchors. In ETLBO algorithm, the Teacher Phase is remained, but the Learner Phase has been added extra process to improve the low knowledge population. This extra process helps to improve the local search process in the searching area. As a result, the low knowledge populations can improve their ability to gain new knowledge. Simulation result shows, the ETLBO enhanced the localization error at 34.16% compared to traditional Teaching Learning Based (TLBO) Algorithm.