1. Movement-Assisted Sensor Deployment in WSN
Heon-jong Lee
Korea University of Technology and Education Laboratory of Intelligent Networks

2. Today’s Topic : Sensor Deployment
Deploying more static sensors cannot solve the problem due to wind or obstacles
Not Covered!!
Coverage under random deployment
Mobile Sensor Robots

3. Today’s Topic : Sensor Deployment
Solution : Utilizing Mobile Sensors!
Direct the movement of sensors to increase coverage
General idea: detecting coverage hole  move to heal the hole
Utilizing Voronoi Diagram
Utilizing Virtual Grid
Mobile Sensor Robots

4. Mobility-Assisted Sensor Deployment
Guiling Wang, Guohong Cao, and Tom La Porta
IEEE INFOCOM 2004
4/30
Mobile Sensor Robots

5. Problem statement Problem statement is: Scenario
given the target area, how to maximize the sensor coverage with less time, movement distance and message complexity
Scenario
discover the existence of coverage holes (the area not covered by any sensor) in the target area
the proposed protocols calculate the target positions of these sensors, where they should move
VEC(VECtor-based), VOR (VORonoi-based), and Minimax based algorithm
Mobile Sensor Robots

6. Voronoi Polygon Voronoi polygon O is the set of Voronoi vertices of O
is the set of Voronoi edges of O
is the set of Voronoi neighbors of O
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7. VEC(VECtor-based Algorithm)
Motivated by the attribute of electro-magnetic particles : expelling forces
Virtual Forces pushes sensors away from dense area B C A B C A
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8. VEC(VECtor-based Algorithm)
Notation
: distance between si, sj
: average distance between two sensors(calculated beforehand)
Three types of Virtual Force( ) A B B A B A
Voronoi Polygons are not completely covered
a Voronoi Polygon is not completely covered
Voronoi Polygons are completely covered
Mobile Sensor Robots

9. VEC(VECtor-based Algorithm)
Field boundary also exert force( ) :
will push sensor to move
Algorithm
distance of si to the boundary
if the local coverage is not increased, the sensor should not move the target location
Mobile Sensor Robots

10. VEC(VECtor-based Algorithm)
Execution of VEC
35 sensors in a 50m by 50m flat space
Sensing range is 6m
Round 0 (75.7%)  Round 1 (92.2%)  Round 2 (94.7%)
Mobile Sensor Robots

11. VOR(VORonoi-based Algorithm)
Pull-based algorithm : pulls sensors to their maximum coverage hole
move towards its farthest Voronoi vertex( )
d(Si, A) > Sensing range
moves along line SiA to Point B, where d(A, B) is equal to the Sensing range B M B M
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12. VOR(VORonoi-based Algorithm)
limit the maximum moving distance to be at most half of the communication range
Si can’t see me(Sj)
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13. VOR(VORonoi-based Algorithm)
check whether its moving direction is opposite to that in the previous round
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14. VEC(VECtor-based Algorithm)
Execution of VOR
35 sensors in a 50m by 50m flat space
Sensing range is 6m
Round 0 (75.7%)  Round 1 (89.2%)  Round 2 (95.6%)
Mobile Sensor Robots

15. Minimax Concept Notation
chooses the target location inside the Voronoi polygon whose distance to the farthest Voronoi vertex (Vfar) is minimized
a more regular shaped Voronoi polygon
Notation
Om : Minimax point
C(O, r) : A circle centered at point O with radius r
C(Vu, Vv, Vw) : The circumcircle of three points Vu, Vv, Vw B M N B M N
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16. Minimax
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17. Minimax
To find the Minimax point, we only need to find all the circumcircles of any two and any three Voronoi vertices.
Among those circles, the one with the minimum radius covering all the vertices is the Minimax circle. The center of this circle is the Minimax point.
Mobile Sensor Robots

18. Minimax
Algorithm (O(n3))
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19. Minimax Execution of Minimax 35 sensors in a 50m by 50m flat space
Sensing range is 6m
Round 0 (75.7%)  Round 1 (92.7%)  Round 1 (96.5%)
Mobile Sensor Robots

20. Performance Evaluation
Measure the performance from two aspects:
deployment quality : measured by the sensor coverage and the time(the number of round) to reach this coverage
cost : sensor cost, energy consumption(moving distance)
Environment
30, 35, 40,and 45 sensors
50m ∗ 50m or 150m * 150m
initial deployment : random or normal
communication range : 10m to 28m (normal: 20m)
sensing range : 6m
using NS2, , DSDV
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21. Performance Evaluation
Coverage Moving distance
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22. Performance Evaluation
Communication range
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23. Mobility-Assisted Sensor Networking for Field Coverage
Dan Wang, Jiangchuan Liu, Qian Zhang
IEEE GLOBECOM 2007
23/30
Mobile Sensor Robots

24. Motivation and Previous Work
Concept : A mix of mobile and static sensors
Motivation
Mobile sensors are expensive
When all sensors are in random motion, packet routing and information dissemination will be much more complicated
[13] suggested a one-time reposition of the mobile sensors after the initial deployment, but the coverage is still unbalanced
black : static sensors
white : mobile sensors
Mobile Sensor Robots

25. The goal
The mobile sensors are always in motion to assist the static sensors
Main challenges
(1) to clarify the necessary coverage contributions from the static and mobile sensors
(2) to find a mobility model for the mobile sensors to achieve their desired coverage contribution
Mobile Sensor Robots

26. Mobility-Assisted Architecture
n2 virtual grid (0, 1, …, n2-1)
The sensors are aware of their location information (using GPS)
The size of each grid is
 any active sensor in a grid can cover the whole grid
The sensing range of a mobile sensor can be smaller. e.g.,
The static sensors in one grid are equivalent, they do not have to be active
The mobile sensors are always active, and can stay in a grid or move to other grids  covering the holes
Mobile Sensor Robots

27. Mobility-Assisted Architecture
Static sensors: a random activation scheduling Mobile sensors: a random walk model
Two stages
Parameter Initialization (1~4)
Field Monitoring (each time slot) (5~6)
initial deployment
one or more MS travel (collect information of SS)
Determine the activation probability of SS and notify them
SS activates itself with p and then monitors its grid
MS decides to move into one neighboring grid or to stay in the current grid, and then monitors its grid p p p p p p p p p p
Mobile Sensor Robots

28. Mobility-Assisted Architecture
The advantages of using a probabilistic operation
easier to implement
involves simple optimization in the initial stage
substitute mobile sensor can easily follow the mobility model
the behavior of each type of the sensors are statistically identical
this is useful especially for recharging or replacement of mobile sensors
more resistant to intruders that try to learn the sensor behavior
Mobile Sensor Robots

29. Objective and Optimization
Define a measure of how well a location is covered
(δ is minimum coverage ratio required by the user)
Our objective is to ensure this quality(δ-covered), while maximizing the lifetime of the network.
lifetime : the death of the first sensors serves as a good signal to the end of the steady-state operation(because of domino effect)
Mobile Sensor Robots

30. Objective and Optimization
Notation
assign static sensors with an identical activation probability p
The probability that mobile sensor travels to the grid i, πi π =
The density of grid i, d(i). i.e., the number of static sensors in i
The number of mobile sensors in the network, M
Formulation
maximizes the network lifetime
contribution constraint of each mobile sensor
ensure the coverage ratio of the grids
Mobile Sensor Robots

31. Objective and Optimization
Heuristic Algorithm (O(N2))
The grid to have a low activation probability p
Let li is the index of the grid in terms of density, i.e.,
Iteration (until (fully exploited) or K == n2)
find key parameter K (start from 0), the index after which the grids are dense enough to be covered by the static sensors only
evaluate p by
with p, find a valid
increase index K
We have an upper bound on p corresponding to K-1 and a lower bound on p corresponding K
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32. A Random Walk Mobility Model
A mobile sensor will either stay in a grid, or move into an adjacent grid along four direction
in a boundary grid, it has 3 or 2 direction
Consider decisions depending only on the current grid (Markov chain : given the present state, future states are independent of the past states(wikipedia))
Let Pij is the transition probability from grid i to grid j
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33. A Random Walk Mobility Model
Given the long-run distribution π
Markov chain obeys the following balance equations
standard steady-state constraints for Markov chain
no transition is possible for two non-adjacent grids
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34. A Random Walk Mobility Model
The mobile sensors cannot be trapped in a grid
Add another conditions
Summary
the probability that it will stay in this grid should be less than 1
mobile sensor always has chance to move into a neighboring grid.
Mobile Sensor Robots

35. Performance Evaluation
Focus on coverage quality and network lifetime
Environment
1000 static sensors in a field 140m x 140m
100 virtual grids
10000mAh battery power for each sensor (it can last for one day)
coverage quality δ = 0.85
time slot is 1 minute
the average of 100 independent experiments
The cumulative distribution curve of the residual energy after the death of the first sensor
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36. Performance Evaluation50
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37. Performance Evaluation
A small set of MS assists load balancing while the cheaper SS carries the main duty for field coverage
randomly and uniformly generated from time to time
Mobile Sensor Robots