Location Estimation for Wireless Sensor Networks n Location

Location Estimation for Wireless Sensor Networks n Location estimation algorithms can be categorized as n Range-based schemes n n TOA, TDOA, AOA, RSSI Range-free schemes n n DV-based scheme Convex Position Estimation (CPE)

Convex Position Estimation (CPE)

Distributed Location Estimation Algorithm n n n A few nodes have known position – equipped with GPS or placed deliberately (Beacon node) The remainder nodes estimate position from knowledge about communication links The beacon node has the ability of modifying the power level

Distributed Location Estimation Algorithm P O 2 (x 2, y 2) r O 2 (x 2, y 2) O 1 (x 1, y 1) O 3 (x 3, y 3) O 1 (x 1, y 1) Ｑ O 4 (x 4, y 4) O 2 (x 2, y 2) O 1 (x 1, y 1) O 3 (x 3, y 3)

Reduce the Range of ER n If a normal node have farther neighbor beacon, the ER can be reduced to smaller one O 4 (x 4, y 4) O 2 (x 2, y 2) O 1 (x 1, y 1) O 3 (x 3, y 3)

Reduce the ER: Rule 1 n Rule 1: The cut point is based on the intersection point of circle and the borders of estimative rectangle

Reduce the ER: Rule 2 n Rule 2: The cut point is based on the midpoint of intersection arc

Reduce the Computation Complexity n n We need to use some trigonometric functions, such as sin, cos, asin and acos to find the midpoint of an arc A line segment is used to instead of the arc

How to Cut the ER? n We need to define complete rules to decide what border must be cut (a) (b) (c)

Slope of Line Segment n We can divide the slope range of a line segment into four regions as the figure show. tan 112. 5° = -2. 4142 tan 67. 5° = 2. 4142 tan 22. 5° = 0. 4142 tan -22. 5° = -0. 4142 tan -67. 5° = -2. 4142

Real networks environment