1. A Near-Optimal Sensor Placement Algorithm to Achieve Complete Coverage/Discrimination in Sensor Networks
Authors : Frank Y. S. Lin and P. L. Chiu, Student Member, IEEE
Presenter : Mun Chang-min
Korea University of Technology and Education
department of electrical & electronic engineering
UoC lab

2. distributed sensor networks (DSNs)
◊ random placement(deployment)
- when the environment is unknown, random placement is the only choice.
◊ with grid-based placement
- to guarantee a particular quality of service, if the properties of the terrain are predetermined. The field is generally divided into grids and sensors are carefully deployed at the grid points.

3. Grid-based placement
◊ It can be represented as a collection of two- or three-dimensional grid points
Grid
point 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

4. Grid-based placement
◊ A set of sensors can be deployed on the grid points to monitor the sensor field.
Grid
point
Sensors
coverage 1 2 3 4 5
Sensor 6 7 8 9 10 11 12 13 14 15

5. Completely discriminated field
◊ Power vector
- is defined for each grid point to indicate whether sensors can cover a grid point in a sensor field.
ex) power vector of grid point 8
= (0, 0, 1, 1, 0, 0) corresponding to sensor 4, 6, 7, 9, 10 and 12
◊ Completely discriminated sensor field
- each grid point is identified by a unique power vector.

6. Not completely discriminated field
◊ Indistinguishable grid points
- but, the field is completely covered.
power vector of grid point 1 ║
power vector of grid point 2 1 7 6 2

7. Optimal Sensor Placement
◊ If complete discrimination is not possible, High discrimination requires that the maximum distance error be minimized.
◊ In this case, optimal sensor placement problem, therefore, defined as a min-max model.

8. Min max model ◊ Given Parameters:
- A = {1, 2, ...,m} : Index set of the sensors’ candidate locations.
- B = {1, 2, ..., n} : Index set of the locations in the sensor field, m ≤ n
- rk : Detection radius of the sensor located at k, k ∈ A.
- dij : Euclidean distance between location I and j, i, j ∈ B.
- ck : The cost of the sensor allocated at location k, k ∈ A.
- G : Total cost limitation.
◊ Decision Variables:
- yk : 1, if a sensor is allocated at location
- k and 0 otherwise, k ∈ A.
- vi = (vi1, vi2, ..., vik) : The power vector of location I, where vik is 1 if the target at location i can be detected by the sensor at location k and 0 otherwise, where i ∈ B, k ∈ A.

9. Min max model

10. Proposed algorithm

11. Eaxperiment 1 K = 10000 ∀Ci = 1, (1 ≤ i ≤ n)
◊ Find a minimum sensor density for a complete covered and discriminated
(Parameters of the cooling schedule)
α = 0.75
β = 1.3
R = 5n
t = 0.1
n = amount of grids
tf = t0 / 30
K = 10000
∀Ci = 1, (1 ≤ i ≤ n)
Exhaustive search
Proposed algorithm
65minutes
0.1second
To find the solution for the 10x3 area

12. Experiment 2
◊ Larger sensor fields, with 10x10 and 30x30 grid points, are considered.
◊ Find a minimum sensor density for a complete covered and discriminated compared with random placement approach.

13. Experiment 2 25% 44% Completely discriminated Completely covered
25% %
Completely discriminated
42% %
Completely covered
24% %
44% %

14. Thank you for listening
conclusions
◊ the proposed algorithm is very effective, scalable, and robust.
Thank you for listening