1. Wireless Sensor Networks: nodes localization issue
Jean Philippe MONTILLET
Centre for Wireless Communications
University of Oulu, Finland

2. Summary
Introduction
Positioning techniques: How to localize tiny sensors nodes in wireless sensor networks
Nodes localization issue (error in received signal \ error in space)
Beacon nodes and how they can reduce the error on positioning the nodes
Results
Conclusion
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3. Introduction (1)
In a Wireless sensor nodes thousands of sensors need to know their position
Many applications need position info:
in-home
forest-fire detection
atmospheric (temperature, pressure, … )
military (target detection, …)
police
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4. Introduction (2)
Sensors used in WINS (Wireless Integrated Network Sensors) are low-cost, low bit rate, and low-power consumption.
Allows mass production
no maintenance of sensors
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5. Positioning techniques (1)
Why does GPS system not use?
Expensive solution to have accuracy <1 m
Not work everywhere (dense vegetation)
Constraint on the energy …
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6. Positioning techniques (1)
Instead of using GPS, various techniques can be used
Algorithms are based on:
Cooperative ranging: ( TOA, TDOA, RSSI, AOA)
Or/and DV-Hop count method
Present work based on direct method and quasi-Newtown algorithm
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7. Positioning Techniques (2)
Direct Method
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8. Positioning Techniques (3)
Equations in 3D:
Solution:
(i.e. Article for the details of the coefficients)
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9. Positioning Techniques (4)
Non linear method (DFP)
Where,
The solution p is found iteratively:
,with :
And Bk is the inverse Hessian
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10. Positioning Techniques (5)
Nonlinear Optimization
Gauss-Newton Method on Rosenbrock's Function
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11. Nodes localization issue (1)
Range estimation degraded due to several factors:
Multipath
Inaccuracy of the clock
Channel fading
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12. Nodes localization issue (2)
Error in the received signal
Ex:RSSI
Xσ : medium scale fading (zero-mean gaussian distributed)
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13. Nodes localization issue (3)
Error in space
Model the fact that after running algorithm, a residual error is still left.
Difficult to model
One way is to take a gradient descent error:
Or on the coordinates:
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14. Beacon nodes (1)
Beacon may help to reduce the error on the node localization.
Boundaries:
Cost of beacon nodes precludes a dense beacon placement.
Very high density self-interferences will appear
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15. Beacon nodes (2) Arithmetic method (u= unknown, b= beacon) 16/01/2019
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16. Beacon nodes (3)
The more the beacon nodes are around the unknown node which wants to know its position, the less the error is on its coordinate.
Idea is through this simple example,
To see how the error on the coordinates of the
unknown nodes behaves.
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17. Results (1) 1 set of simulations Monitored area size: 70mX70mX10m
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18. Results (2)
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19. Results (3)
The graphs with higher number of anchor nodes are not always on the top of those ones with low number of anchor nodes.
Maybe due to spatial localization of anchor nodes
Second set of simulations: area monitored limited (30mX30mX10m).
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20. Results (4)
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21. Results (5)
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22. Results (6) Same results than in the first set of simulations
Maybe due to the fact we chose an error randomly distributed on the coordinates
Or beacon nodes are to be put only where the error is maximal (key point)
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23. Results (7)
Last set of simulations: to check if the error is gradient descent (as we model in the error in space)
Different topology, but only if the nodes are distributed in a ring, spatial localization can be seen.
Simulations parameters:
4 anchors nodes (at the centre)\ 100 unknown
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24. Results (8)
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25. Conclusion
Two types of error: error in received signal (or time) and error in space
To figure out how beacon nodes can help to reduce the error on the position.
The gradient descent method to model the error in space shows good results
Future work: take into account self-interferences between beacon nodes (MAC layer)
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26. Thanks for coming!
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