ZigBee-based indoor location system by K-nearest neighbor algorithm with weighted RSSI

Chih Ning Huang, Chia Tai Chan*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

71 Scopus citations


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 different classes 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.

Original languageEnglish
Pages (from-to)58-65
Number of pages8
JournalProcedia Computer Science
StatePublished - 2011
Event2nd International Conference on Ambient Systems, Networks and Technologies, ANT-2011 and 8th International Conference on Mobile Web Information Systems, MobiWIS 2011 - Niagara Falls, ON, Canada
Duration: 19 Sep 201121 Sep 2011


  • Indoor location system
  • K-nearest neighbor algorithm
  • Zigbee


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