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1 Sensor Deployment and Target Localization Based on Virtual Forces Y. Zou and K. Chakrabarty IEEE Infocom 2003 Conference, pp. 1293-1303,. ACM Transactions.

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Presentation on theme: "1 Sensor Deployment and Target Localization Based on Virtual Forces Y. Zou and K. Chakrabarty IEEE Infocom 2003 Conference, pp. 1293-1303,. ACM Transactions."— Presentation transcript:

1 1 Sensor Deployment and Target Localization Based on Virtual Forces Y. Zou and K. Chakrabarty IEEE Infocom 2003 Conference, pp ,. ACM Transactions on Embedded Computing Systems, vol. 3, pp , February 2004.

2 2 Outline Introduction Virtual Force Algorithm (VFA) Target localization Simulation Conclusion

3 3 Introduction This paper focus on : sensor placement strategies that maximize the coverage Virtual Force Algorithm (VFA) 2 sensor detection models Binary sensor detection model Low detection accuracy Probabilistic sensor detection model High detection accuracy

4 4 Virtual Force Algorithm (VFA) System assumptions All sensor nodes are able to communicate with the cluster head. The cluster head is responsible for executing the VFA algorithm and managing the one-time movement of sensors to the desired locations. Sensors only send a yes/no notification message to the cluster head when a target is detected. The cluster head intelligently queries a subset of sensors to gather more detailed target information.

5 5 Binary sensor detection model n * m grid sensor field k sensors randomly deployed the detection range of sensor : r sensor Si is deployed at point (xi, yi) For any point P at (x, y), the Euclidean distance between Si and P the coverage Cxy(si) of a grid point P by sensor Si

6 6 Probabilistic sensor detection model Range detection error : re (re < r) a = d(si, P) − (r−re)

7 7 Virtual forces Obstacles exert Repulsive (negative) forces on a sensor Preferential coverage exert Attractive (positive) forces on a sensor

8 8 Virtual forces(Cont1.) The total force action on sensor Si be denoted by Fi. Fi is a vector whose orientation (angle) is determined by the vector sum of all the forces acting on Si. Let the force exerted on Si by another sensor Sj be denoted by Fij.

9 9 Virtual forces(Cont2.) dij is the Euclidean distance between sensor si and sj dth is the threshold on the distance between si and sj The threshold distance dth controls how close sensors get to each other αij is the orientation (angle) of a line segment from si to sj wA (wR) is a measure of the attractive (repulsive) force

10 10

11 11 Minimize “ wasted overlap ”  dij ~ 2r

12 12 Cth be the desired coverage threshold for all grid points

13 13

14 14 Data structure of the VFA algorithm

15 15 VFA algorithm

16 16 Target localization Detection probability table Score-based ranking Selection of sensors to query

17 17 Detection probability table

18 18 Si detect a target at gird point P(x,y) Si doesn ’ t detect a target at gird point P(x,y)

19 19

20 20 Score-based ranking Srep(t) : the set of sensors that have reported the detection of an object Srep,xy(t) : the set of sensors that can detect point P(x, y) and have also reported the detection of an object.

21 21 Selection of sensors to query Select sensors based on a score-based ranking. The sensors selected correspond to the ones that have the shortest distance to those grid points with the highest scores.

22 22 Generate the probability table

23 23 the pseudocode of the localization algorithm

24 24 Simulation Randomly deploy 20 sensors are placed in the sensor field. detection radius as 5 units (r = 5), Range detection error as 3 units (re = 3) for the probabilistic detection model. The sensor field is 50 by 50 in dimension. Simulation tool : MATLAB

25 25 Binary Sensor Detection Model

26 26 Probabilistic Sensor Detection Model

27 27 Sensor Field with a Preferential Area and an Obstacle

28 28 Probability-based Target Localization

29 29 Conclusion Propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage.

30 30 Related references Y. Zou and K. Chakrabarty, "A distributed coverage- and connectivity-centric technique for selecting active nodes in wireless sensor networks", IEEE Transactions on Computers, vol. 54, pp , August 2005.A distributed coverage- and connectivity-centric technique for selecting active nodes in wireless sensor networks" Guiling Wang; Guohong Cao; La Porta, T.; “ Movement-assisted sensor deployment ”, IEEE INFOCOM Volume 4, 7-11 March 2004 Page(s): vol.4 Wu, J.; Yang, S, “ SMART: a scan-based movement-assisted sensor deployment method in wireless sensor networks ”.; IEEE INFOCOM Proceedings IEEE Volume 4, March 2005 Page(s): vol. 4


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