Intentional Mobility in Wireless Sensor Networks Deployment, Dispatch, and Applications

1. Intentional Mobility in Wireless Sensor NetworksDeployment, Dispatch, and Applications Dr. You-Chiun Wang (王友群) Department of Computer Science, National Chiao-Tung University 2010/10/22 - 1 -

2. Wireless Sensor Networks SENSROS ARE STATIC!

3. “Mobile” Sensor Networks • Some sensor nodes can move around (e.g., robots). • Purpose: automatic deployment, network repairing, and sensor dispatch

4. Topics • Automatic Deployment • Mobile Sensor Dispatch • Systems & Applications • iMouse System • VSN (Vehicular Sensor Network) System

5. SENSROS ARE MOBILE! Automatic Deployment - 5 -

6. In a “Perfect” World - 6 -

7. In a “Real” World network partition partial coverage - 7 -

8. Can Sensors Reorganize a WSN “by Themselves”? network partition partial coverage - 8 -

9. Question • Given a sensing field A possibly with obstacles, how can we make mobile sensors automatically deploy a network in an efficient way? • Use the smallest number of sensors. • Sensors can consume the minimum energy to reorganize the network. - 9 -

10. Overview of Solutions • We first calculate the locations to place sensors and then dispatch mobile sensors to these locations. • Placement solution should use fewer sensors. • Dispatch solution should move sensors so that they can remain the maximum energy after movement. Energy placement dispatch

11. Placement Algorithm • Partition a sensing field A into sub-regions and then place sensors in each region: • Single-row regions • A belt-like area between obstacles whose width is NOT larger than , where rmin= min(rs, rc). • We can deploy a sequence of sensors to satisfy both coverage and connectivity. • Multi-row regions • We need multiple rows of sensors to cover such areas. • Note: obstacles may exist in such regions. - 11 -

12. Step 1: Partition the Sensing Field • From A, we first identify all single-row regions. • Expand the obstacles’ perimeters outwardly and A’sboundaries inwardly by a distance of . • If the expansion overlaps with obstacles, we take a projection to obtain single-row regions. • The remaining regions are multi-row regions. - 12 -

13. Step 2: Place Sensors in a Single-Row Region • Place sensors along the bisector of region. - 13 -

14. Step 3: Place Sensors in a Multi-Row Region • Place sensors row by row. • A row of sensors guarantee coverageandconnectivity. • Adjacent rows guarantee continuous coverage. - 14 -

15. Step 4: Handle the Boundary Case • Three unsolved problems • Some areas near the boundaries are NOT covered. • Connectivity between adjacent rows needs to be maintained. • Connectivity to neighboring regions should be maintained. • Solutions • Sequentially place sensors along the boundaries. • Not all boundaries should be placed with sensors.

16. I A Dispatch Algorithm (1/5) • Find a maximum-weightmaximum matchingin a weighted complete bipartite graph. • Sensors vs. locations • We should take care of the obstacles inside the sensing field. - 16 -

17. 1 2 3 4 Dispatch Algorithm (2/5) • Run sensor placement algorithm on I to get the target locations. L={(x1, y1), (x2, y2), (x3, y3), (x4, y4)} I C • Compute energy cost A D B E

18. Dispatch Algorithm (3/5) • Construct the weighted complete bipartite graph. A 1: needs 9 energy weight (A,1) = 40 – 9 = 31 • Weights of edges: w(si,lj) = 40 –c(si,lj) • - objective function: remaining energy • - all sensors have initial energy of 40 A 1 B 2 C 3 D 4 E Locations Sensors - 18 -

19. Dispatch Algorithm (4/5) Find the maximum-weight maximum matching. Hungarian Method: finds the optimal solution inO(n3). A 1 B 2 C 3 D 4 E Locations Sensors - 19 -

20. I C 1 2 3 4 A D B E Does not move Dispatch Algorithm (5/5) • Sensors are dispatched to the matched locations. A 1 B 2 C 3 D 4 E Locations Sensors - 20 -