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Department of Computer Science and Information Engineering

Department of Computer Science and Information Engineering

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Department of Computer Science and Information Engineering

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  1. INDOOR LOCALIZATION SYSTEM USING RSSI MEASUREMENT OF WIRELESS SENSOR NETWORK BASED ON ZIGBEE STANDARD Authors:Masashi Sugano, Tomonori Kawazoe, Yoshikazu Ohta, and Masayuki Murata Publisher:Wireless Sensor Networks 2006 Present:Yu-Tso Chen Date:March, 25, 2009 Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

  2. Outline • 1. Introduction • 2. Localization System Model • 3. Effective Data Collection • 4. Experiment Setup and Results • 5. Conclusions and Future Works

  3. Introduction • Sensingdata are meaningless if the sensor location is unknown. • RSSI has a larger variation because itis subject to the deleterious effects of fading or shadowing. • RSSI-based approach therefore needs more data than other methods to achieve higher accuracy. • Collecting a amount of data causes an increase in traffic & in the energy consumption. • We devised a data-collectiontechnique in which sensors recognize the number of surrounding sensors.

  4. Localization System Model

  5. Sensor Node Placement • There is two ways in which a sensor node can learn its position. • Sensor node’s position in the sink node’s database • Can’t handle large number of randomly placed sensor nodes. • Places a few beacon nodes that know their own positions

  6. Data Collection 1. Measure the power of the packet and transform the RSSI into Distance

  7. Position Estimation Calculation at the Sink Node 2. Sensors send the following data to sink node Sensor ID, Target ID, Packet Number, and Sensor-to-Target Distance 3. Use a maximum-likelihood (ML) estimation to estimate the position of a target 4. ML estimation of a target’s position can be obtained using the Minimum Mean Square Error (MMSE) [18]

  8. C P A B Position Estimation Calculation at the Sink Node

  9. Position Estimation Calculation at the Sink Node

  10. Effective Data Collection • A user can decide the number of data to collect based on prior knowledge. • Targets can inform sensors of the number of data by sending packets • Sensor nodes send data depends on the deployment density of sensor nodes itself and the distance between the sensor node and the target.

  11. Effective Data Collection(cont.) • R is the communication range and Mi is the number of sensor nodes. • Define the number of data required by the system as Z • Sensor node i sends data if the measured distance is less than Di • Di depends on the density around sensor node i

  12. Implementation of Localization System • We set the threshold value of RSSIin each sensor node • Sensor node decides to transmit a packet to a sink node only when the received signal from a target exceeds this value • We can change the number of data to collect by changing this threshold value

  13. Position Estimation Procedure • 1. Sensor nodes’ positions are stored in a database on a PC. • The RSSI threshold is set in sensor nodes. • 2. A measurement demand message is broadcast to sensor nodes from a target. • 3. Sensor node measures RSSI, if exceeds the preset threshold value, a sensor node transmits the target ID and sequence number to the sink node

  14. Position Estimation Procedure (cont.) • 4. Sink node collects the ID and sequencenumber of the target, and the ID and RSSI of each sensor node. • If three or more RSSI values with the same target ID and sequence number are collected, the target’s position can be estimated.

  15. Experimental Results

  16. Positions of sensor nodes and targets in the conference room

  17. Predicted & Actually Obtained Data Collection Numbers

  18. Conclusions & Future Works • Density of sensor nodes wasset to 0.27 nodes/m2 the position estimation error could be reduced to 1.5-2 m. • Thecollected numbers of data could be controlled by changing the RSSI threshold.