Available online at Procedia Computer Science 5 (2011) 58–65

The 2nd International Conference on Ambient Systems, Networks and Technologies (ANT)

ZigBee-based indoor location system by k-nearest neighbor

algorithm with weighted RSSI

Chih-Ning Huang, Chia-Tai Chan*

Institute of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112 Taiwan Abstract

With the advances in information and communication technologies, wireless sensor networks has made Ambient Intelligence (AmI) applications possible that can monitor the situation around the persons or objects and give certain responses for their needs. The location awareness is an important technology for AmI applications. The advantages of ZigBee wireless sensor networks such as low cost, high scalability, high availability and supporting dynamic routing topology make ZigBee more suitable for indoor location system. In this research, we propose a ZigBEe-bAsed indoor loCatiON (ZigBEACON) system for the AmI applications. The proposed approach is based on the k-nearest neighbor algorithm. According to the Received Signal Strength Indication’s (RSSI) path loss distribution, the RSSI values are defined into four classes. The signals that belong to differentclasses will be adjusted by the different ratio and will be referred to as weighted RSSI. The use of weighted RSSI can effectively choose the p-nearest reference nodes. Finally, the position of mobile node would be derived by calculating the coordinates of p-nearest reference nodes. Comparing the results with that of ZigBee-based LANDMARC system, our approach has 29% improvement on average error distance. The approach not only improves the accuracy, but also provides less calculation complexity than other improvement methods of LANDMARC. The ZigBEACON approach is an adequate solution to the indoor location system for AmI applications.

Keywords: ZigBee; indoor location system; k-nearest neighbor algorithm

1.Introduction

Ambient Intelligence (AmI) is an electronic environment that can monitor the situation around the persons or objects and give an adequate response for their needs [1]. With the advances in Information and Communication Technologies (ICTs), Wireless Sensor Networks (WSN) has made many AmI applications possible. Context-aware technology gathers the relevant context from sensor data, and then gives a certain response. Three main aspects of context are: where you are, who you are with, and what resources are nearby [2], so the location information is one of the important context information for AmI. For instance, when a person has fallen in the AmI environment, the wearable sensor will detect the fall event and send the alarm to the caregiver who can deliver immediately help. And * Corresponding author. Tel.: +886-2-2826-7371; fax:+886-2-2821-0847.

E-mail address: ctchan@ym.edu.tw.

1877–0509 © 2011 Published by Elsevier Ltd. Open access under Selection and/or peer-review under responsibility of Prof. Elhadi Shakshuki and Prof. Muhammad Younas.doi:10.1016/j.procs.2011.07.010