60 Chih-Ning Huang and Chia-Tai Chan / Procedia Computer Science 5 (2011) 58–65

disadvantages of calculating Mahalanobis distance, such as high complexity and the learning process through collecting amount of RSSI values before positioning, are not suitable for the dynamic indoor environment. Some algorithms revised the original k-nearest reference tags of LANDMARC. Shape Constraint Approach [7] utilized a shape constraint factor to find the k-nearest reference tags based on the geometrical correlation properties. Others, such as Jiang XJ et al. [8] calibrated the tracking tag’s coordinate by proceeding the error corrections until the tracking tag’s coordinate was stable, and Jain S et al. [10] used a backtracking algorithm to find the optimal colonies that based on the spatial co-relation between reference tags. But the results would poor if the original k-nearest reference tags were far from the mobile object; moreover, the recursion method would increase the latency that is not suitable for the real time positioning. Except correcting the original k-nearest reference tags of LANDMARC, some researches tried to increase the accuracy by adjusting the values of original RSSI values, such as using the tracking tag as the criterion to get the modified signal strength [6] or averaging the values of a historical RSSI sequence [9]. The reduced error distance was from 0.076 m to 0.3616 m, although those adjusting method were simple, the improvements were less significant.

The WSNs provide ubiquitous sensing, computing communication capability that facilitate the development of AmI application. More and more researchers are devoted to the study of indoor position system based on WSN like ZigBee. Mendalka M et al. [12] implemented the Pattern Matching (PM) localization in ZigBee wireless sensor networks. The concept of comparing a set of RSSI with all reference tags and measured samples is similar to LANDMARC methodology. But the non-real-time learning process of PM algorithm is not practical; what’s more, the tracking tag needs to send the series of localization beacons on localization phase that would increase the power consumption.

3.Materials and Methodology

3.1.Hardware

The sensor, SuperNode, consists of a MSP430 microcontroller and an UZ2400 ZigBee chip. UZ2400 will transform the power value (dBm) into the RSSI value linearly that is from 0 to 255 [13]. The RSSI value will decrease when the distance increases. Then the ZigBEACON system will use the RSSI values to do positioning. The size of SuperNode is about 5cm (length) × 3cm (width) × 0.5cm (height).

3.2.Methodology

Fig. 1. The flow chart of ZigBEACON system

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In ZigBEACON system, SuperNode can be categorized according to their functions: Gateway: it is connected to the computer and delivers the data from all the nodes to computer. Mobile node: the one that should be known its position. It receives signals from RF generators and sends RSSI to computer through gateway. RF generator: it sends the broadcast signal to mobile node and reference nodes. Reference node: it receives signals from RF generators and sends RSSI to computer through gateway.