Wireless Sensor Network Indoor Localization System: Hardware Design, Deployments, Measurements, and Improvements

• Main Authors: Seng-Yong Lau, 劉承榮
• Other Authors: Polly Huang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/41164717169756148288

博士 === 國立臺灣大學 === 電機工程學研究所 === 100 === With an expected market value of $2.71 billion in 2016, supporting daily use of real-time location systems in households and commercial buildings is an increasingly important subject of study. Most indoor localization systems employ an RSSI-signature-based approach which exploits temporal stability in the received signal strength indication (RSSI) from a set of pre-deployed beacons at identified locations, which is referred to as the RSSI signature. In this dissertation, three generations of the location tracking node are designed to address the need of real-time indoor location system. Tightly cooperate with the potential users from elderly care facility, several application specific requirements are considered in the hardware design. Three wireless sensor network deployments in different daily environments including office building, hospital, and exhibition hall are demonstrated. These deployments provide location service to the users and serve as an experimental testbeds. Several measurement studies are conducted on the testbeds to gain a better understanding of the performance of indoor location system. With in-depth analysis of the measurement results, major factors that influence the performance are identified. These factors are WiFi interference, mobility pattern variation, and human body obstacle. A frequency hopping mechanism is proposed to cope with WiFi interference problem. Experimental results show that the 80th-percentile localization error can be reduce from 2.74 meters to 1.24 meters (55%) when 802.11 traffic rate is at its peak. Another gyro assisted orientation aware method is proposed to solve the human bod obstacle problem. With this mechanism, the 80-percentile localization accuracy can be improved by 46%. Finally, an accelerometer assisted adaptive particle filter is proposed to target the mobility pattern variation problem. The experiment result shows the location system can adapts to the walking speed variation even in the extreme case like running.