Chih-Ning Huang and Chia-Tai Chan / Procedia Computer Science 5 (2011) 58–6559

the alarm information about where the person fell is important for caregiver that can reduce the time of finding the faller [3]. Nowadays, Global Position System (GPS) provides reliable outdoor location information. Unfortunately, the characteristic of line-of-sight transmission causes that the GPS is not workable for in-building location-based services. The main technologies of indoor location system include Radio Frequency IDentification (RFID), Wireless Local Area Network (WLAN), Bluetooth and ZigBee et al [4]. The advantages of ZigBee such as low cost, high scalability, high availability and supporting dynamic routing topology make ZigBee more suitable for indoor location system.

In this paper, we propose a ZigBEe-bAsed indoor loCatiON (ZigBEACON) system for the AmI environment. The proposed approach is based on the k-nearest neighbor algorithm which is adopted by the famous RFID-based LANDMARC system [5]. Ideally, the path loss distribution of Received Signal Strength Indication (RSSI) conforms to the equation of path loss model, but the interference like multi-path delivery would affect the real RSSI’s distribution. According to the path loss distribution of RSSI, the RSSI values are defined into four classes. The original RSSI value will be adjusted on the basis of different classes that can effectively select the p-nearest reference nodes of mobile node by Euclidean distance. Finally, the position of mobile node would be derived by calculating the coordinates of p-nearest reference nodes. The ZigBEACON system not only is deployed easily but also improve the accuracy of indoor location system.

The rest content of this paper is organized as follows: Section 2 will introduce the related work of existing LANDMARC system and some improved algorithms for LANDMRAC, and then show one example of indoor localization in ZigBee WSN. In Section 3, the materials and proposed methodology will be described in detail. Section 4 discusses the results of ZigBEACON system and compares the results with that of ZigBee-based LANDMARC system. Finally, the conclusions will be listed in Section 5.

2.Related Work

Traditionally, indoor location system used the RSSI feature to estimate the distance between two objects or establish fingerprinting database. Trilateration is the regular algorithm to calculate the object’s position using at least three known reference points. But the environment interference would affect the accuracy severely, like multi-path fading, temperature and humidity. In order to avoid these problems, Lionel M. Ni et al. [5] proposed LANDMARC system that used real-time RSSI values of fixed reference tags and tracking tag receiving by the fixed RF readers to calculate the Euclidian distance between reference tags and tracking tag. The real-time RSSI of all devices suffer from the same noise influence so the environmental factors can be accommodated. Then, the LANDMARC system chose the k-nearest reference tags to estimate the position of tracking tag. The weights of reference nodes depend on the ratio of the square Euclidian distance’s reciprocal, in other words, the shorter Euclidian distance has larger weight value. However, the variations of tag behavior and the dynamic indoor environment result in a biased estimation.

Table 1. A comparison of the studies on improving the accuracy of LANDMARC system

Euclidean Mahalanobis Off-line

distancedistancelearning

Zhang T et al. [6]

Chen WH et al. [7]

Jiang XJ et al. [8]

Hsu PW et al. [9]

Jain S et al. [10]

Chen X et al. [11] Regionallimit Recursive Reduced error (comparing with LANDMARC) 0.34m (average) 0.076~0.344m 0.3~1m 0.663~1.049m 0.1~0.3m 0.0871~0.3616m

Recently, many researches devoted to improving the accuracy of LANDMARC system [6-11] that are listed in the Table 1. Based on the methodology of LANDMARC system, most of those researches tried to obtain the diversity between reference tags and tracking tag by the Euclidean distance [6-10], but Chen X et al. [11] used the Mahalanobis distance to estimate the similarity between an unknown sample set and a known one. But the