Wireless Sensor Networks for Indoor Location Using Distributed Geometrical Algorithm
碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === The development of wireless communication technology brings more possibility in environment monitoring and health-care system. But the absence of sensor location information reduces the reliability of sensed environmental and biomedical data. Therefore, many lo...
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| Format: | Others |
| Language: | en_US |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/29542063240622234604 |
| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === The development of wireless communication technology brings more possibility in environment monitoring and health-care system. But the absence of sensor location information reduces the reliability of sensed environmental and biomedical data. Therefore, many localization algorithms have been proposed based on Time of Arrival (TOA) or Received Signal Strength Indicator (RSSI) in the indoor environment. This study designed and implemented a RSSI-based distributed localization algorithm in a ZigBee-based Wireless Sensor Network (WSN). A geometrical localization, Bounding boxes and Mass spring optimization for refinement is presented in this thesis. Because of the disturbance and chip-to-chip variation in RSSI measurement, Kalman filter and Maximum likelihood estimator and RSSI calibration have been applied in the system.
The proposed localization algorithms and improving methods were verified and evaluated through simulations and experiments. The average error of bounding boxes and that with mass spring optimization in simulation are 1.1517m and 0.67m respectively in virtual square space of 5m edge. The collected RSSI data establish the model of RSSI to distance by linear regression. The average error of bounding boxes algorithm with RSSI filtering and calibration in the experiment are 1.035m in a rectangle space of 7m and 10m edges.
The distributed geometrical localization algorithm implemented on the sensor device provides reasonable estimation accuracy, reduces the packet traffic load and extends the battery life. The processes for RSSI enhance the accuracy of localization. The lower computation and packet traffic load reveal the potential to merge with environment monitoring and heal-care systems.
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