Practical indoor tracking using Wireless Sensor Networks

RF techniques have attracted much attention for the indoor tracking, because of its better penetrability than traditional techniques, such as infrared or ultrasound. Generally, existing systems mostly focuses on the localization of the mobile entity. In fact, area information is also an important requirement for some applications, such as intelligent museum explanation, etc. Also, the area information may help to improve the location precision. This paper introduces the Area-Point Indoor Tracking (AIT) system which we have designed and developed on the platform with 22 MicaZ motes. The significant difference from the previous systems is that the proposed tracking procedure is divided into two major steps, i.e., area decision and point localization. And the beacon-correlation algorithm (BCA) and the shadowing-grid localization (SGL) algorithm are designed to solve the above two challenging problems respectively. The evaluation results on the platform show that the BCA algorithm can improve the precision of area decision of MERIT from 71% to 86%, and the SGL algorithm decreases 32% location error compared with Ecolocation algorithm.