The document discusses a sensor network project called InfraWatch that monitors a bridge in Holland. Over 145 sensors installed on the bridge collect stress, vibration, temperature and other data at 100Hz, generating over 5GB of data per day. The goals of the project are to model bridge behavior, identify effects of traffic, weather and decay on the bridge's structure over time, and determine an optimal sensor configuration for new bridges. The large volume of terabyte-scale sensor data will be analyzed using Hadoop and MapReduce since it is I/O bound. However, new building blocks are needed for Hadoop to perform signal processing tasks on the data like convolution, Fourier transforms and segmentation.

1. InfraWatch
terabyte-scale sensor network analysis
dr. ir. Joaquin Vanschoren

2. Hollandse Brug

3. Hollandse Brug
Bridge deck weakened, heavy traffic banned
Complete deck replaced

4. Sensor network to monitor bridge behavior

6. Stress (longitudinal)

7. Stress (longitudinal)

8. Stress (longitudinal)
Stress (transverse)

9. Stress (longitudinal)
Stress (transverse)

10. Stress (longitudinal)
Vibration
Stress (transverse)

11. Stress (longitudinal)
Vibration
Stress (transverse)

12. Stress (longitudinal)
Vibration
Stress (transverse)
Temperature

13. Stress (longitudinal)
Vibration
Stress (transverse)
Temperature

14. 145 sensors, 100Hz: 5GB/day
Stress (longitudinal)
Vibration
Stress (transverse)
Temperature

15. InfraWatch project
• Model bridge behavior
• Effects of traffic, weather,... (short term) - warnings
• Effects of decay (long term)
• Interplay between effects (e.g. temp vs stress)
• Optimal sensor network (for new bridges)
• Additional sensors (e.g. chloride)

16. Enter Hadoop
• Terabyte-scale data analysis (I/O bound)
• Typical MapReduce (Hadoop) case!
• No Hadoop libraries for signal processing?
• Mahout (in progress) general data mining
• Some math libraries (in progress, mostly for
calculating Pi(?))

17. Enter Hadoop
• We need new building blocks:
• Convolution
• Discrete Fourier Transform
• Segmentation
• Modified classification, clustering,... algorithms
• Let’s start coding...

18. A typical day
• Cars, trucks, traffic jams
• Lots of noise

19. Smoothing
• Remove noise by convolution with Gaussian response
function

20. Smoothing
• Remove noise by convolution with Gaussian response
function

21. Smoothing
• Remove noise by convolution with Gaussian response
function