Location Finding in Wireless Sensor Network Based on Soft Computing Methods

Sensor Localization is a crucial part of many location-dependent applications that is utilized in wireless sensor networks (WSNs). Several approaches, including range-based and range-free, have been proposed to calculate the position of randomly deployed sensor nodes. With specific hardware, the range-based schemes typically achieve high accuracy based on either node-to-node distances or angles. On the other hand, the range-free mechanisms support less positioning accuracy with less expense. The proposed scheme is based on range-free localization, which utilizes the received signal strength (RSS) from the anchor nodes. In this work, genetic fuzzy and neuro-fuzzy methods are used to become more accurate localization. For more realistic evaluation, the enhanced localization algorithms are examined in different environmental noises.