METHOD OF SENSOR NETWORK LOCALIZATION THROUGH RECONSTRUCTION OF RADIATION PATTERN

• US 20100085242A1
• Filed: 02/17/2009
• Published: 04/08/2010
• Est. Priority Date: 10/07/2008
• Status: Active Grant

• Current Assignee: Intellectual Discovery Co., Ltd.
• Original Assignee: Sungkyunkwan University Foundation For Corporate Collaboration (SungKyunKwan University)
• Inventors: Kim, Jong Tae, Lim, Chae Seok
• Granted Patent: US 8,416,120 B2
• Time in Patent Office: Days
• US Class Current: 342/145
• CPC Class Codes: G01S 5/0289 of multiple transceivers, e.g. in ad hoc networks

Abstract

Disclosed herein is a method of sensor network localization through reconstruction of a radiation pattern with a characteristic value of an antenna depending on orientation thereof. The method can minimize errors using an antenna characteristic value and a signal strength depending on the orientation. In addition, the method can minimize errors using an artificial neural network to characterize a distorted radiation pattern of an antenna and using it for the localization of a triangulation method. Furthermore, the method can increases the localization rate even in a passive localization method by characterizing an asymmetric antenna radiation pattern and constructing the antenna characteristic through an artificial neural network.

Claims

1. A method of sensor network localization through reconstruction of a radiation pattern, the sensor network including a plurality of sensor nodes and a gateway communicating with each other through an antenna, the method comprising:

measuring a radiation characteristic value of the antenna;

measuring radiation patterns of first and second sensor nodes of the plurality of sensor nodes; and

measuring a position of a third sensor node with the radiation characteristic value of the antenna and the radiation patterns of the first and second sensor nodes.

2. The method according to claim 1, wherein the measuring a radiation characteristic value of the antenna comprises:

measuring a radiation characteristic value according to an angle of the antenna; and

deriving a radiation characteristic value of the antenna over a full range of direction using learning and reasoning functionalities of an artificial neural network based on the radiation characteristic value according to the angle of the antenna.

3. The method according to claim 2, wherein the measuring a radiation characteristic value according to an angle of the antenna comprises:

arranging the plurality of sensor nodes in a radial pattern at a constant distance from the gateway;

comparing a signal strength transmitted from the gateway with an actual signal strength received by each of the plural sensor nodes; and

comparing a signal strength transmitted from each of the plural sensor nodes with an actual signal strength received by the gateway.

4. The method according to claim 3, further comprising:

converting, by each of the sensor nodes, the actually transmitted or received signal strength into a signal strength in dBm; and

determining an antenna characteristic value from the signal strength in dBm using a signal attenuation equation;

${\mathrm{RxPower}}_{n<br><br><br><br>2}=\frac{{\mathrm{TxPower}}_{n<br><br><br><br>1}}{1+{\mathrm{distance}}_{n<br><br><br><br>1-n<br><br><br><br>2}^{\mathrm{\beta <br><br>}}},$

where RxPower_n2 indicates a signal strength of the first sensor node which is actually received by the second sensor node,

TxPower_n1 indicates a signal strength transmitted from the first sensor node, and distance_n1-n2 indicates a distance of the second sensor node which is calculated at the first sensor node.

5. The method according to claim 2, wherein the deriving a radiation characteristic value of the antenna over a full range of direction using learning and reasoning functionalities of an artificial neural network based on the radiation characteristic value according to the angle of the antenna comprises:

deriving the antenna characteristic value over the full range of direction using an artificial neural network comprising an input node, a hidden node and an output node, the artificial neural network receiving the angle of the antenna at the input node and returning the antenna characteristic value according to the angle of the antenna at the output node.

6. The method according to claim 2, wherein the measuring radiation patterns of first and second sensor nodes of the plurality of sensor nodes comprises:

measuring positions and angles of the first and second sensor nodes and a distance between the first and second sensor nodes;

generating a first data pair by comparing a signal strength transmitted from the first sensor node with an actual signal strength received by the second sensor node; and

generating a second data pair by comparing a signal strength transmitted from the second sensor node with an actual signal strength received by the first sensor node.

7. The method according to claim 6, wherein the measuring a position of a third sensor node with the radiation characteristic value of the antenna and the radiation patterns of the first and second sensor nodes comprises:

deriving an expected position of the third sensor node from the radiation characteristic value of the antenna, the first data pair, and the second data pair; and

determining an actual position of the third sensor node using an antenna characteristic value according to the expected position of the third sensor node, the artificial neural network, and triangulation.

8. The method according to claim 7, wherein the determining an actual position of the third sensor node using an antenna characteristic value according to the expected position of the third sensor node, the artificial neural network and the triangulation comprises:

forming a first triangle from the first sensor node, the second sensor node, and an expected position of the third sensor node according to the first sensor node;

forming a second triangle from the first sensor node, the second sensor node, and an expected position of the third sensor node according to the second sensor node;

deriving a first antenna characteristic value of the third sensor node with respect to the first sensor node using triangulation of the first triangle and the second triangle;

deriving a second antenna characteristic value of the third sensor node with respect to the second sensor node using triangulation of the first triangle and the second triangle; and

determining, as a position of the third sensor node, an expected position of the third sensor node satisfying an artificial neural network module among expected positions of the third sensor node,

the artificial neural network module being

FindBetaFromAngle(θ

_init)=β

<sub>n−

1</sub>

FindBetaFromAngle(θ

_init+θ

_n)−

β

<sub>n−

2</sub>

where β

<sub>n−

1</sub> indicates the first antenna characteristic value,

β

<sub>n−

2</sub> indicates the second antenna characteristic value,

θ

_n indicates an angle formed by both a side between the third sensor node and an expected position of the third sensor node according to the second sensor node and a side between the third sensor node and an antenna direction of the third sensor node, and

θ

_init indicates an angle formed by both a side between the third sensor node and an expected position of the third sensor node according to the first sensor node and a side between the third sensor node and an antenna direction of the third sensor node.

9. The method according to claim 1, wherein the third sensor node is located within a signal transmission range of the first and second sensor nodes.