Location Determination in Indoor Environment based on RSS Fingerprinting and Artificial Neural Network

Abstract

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. Cheap, smart devices with multiple onboard sensors, networked through wireless links and the Internet and deployed in large numbers, provide unprecedented opportunities for instrumenting and controlling homes, cities, and the environment. One of the crucial issues in wireless sensor networks is position determination. In this work a positioning system based on received signal strength (RSS) and WLAN is presented. In indoor environments, received signal strength is a complex function of distance. In this work artificial neural network is used to establish a relationship between RSS and location. The location determination accuracy of the proposed system has been investigated and promising results have been achieved. Although based on WLAN technology, the same positioning technique can be applied to any wireless mobile device or sensor in a wireless sensor networks.