1. Cyber-Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks Gregory Hackmann*, Weijun Guo*, Guirong Yany, Chenyang Lu*, Shirley Dykey *Department of Computer Science and Engineering, Washington University in St. Louisy School of Mechanical Engineering, Purdue University Presented By: Ayush Khandelwal

2. About the Authors: Gregory Hackmann : Postdoctoral Research Assistant, Washington University in St. Louis.Department of Computer Science and Engineering Weijun Guo: Research Associate at North Carolina State Univ. Guirong Yany: Researcher in Mechanical Engineering, Purdue University Chenyang Lu: Professor of Computer Science and Engineering,Washington University in St. Louis Shirley J. Dyke : Purdue University, Professor of Mechanical and Civil Engineering

3. Acknowledgements : This work is supported by NSF NeTS-NOSS Grant CNS-0627126 and CRI Grant CNS-0708460

4. Content : 1.Abstract 2.Introduction 3.Previous/Related Works 4.Damage localization approach 5.Distributed architecture 1.Multi-Level Damage Localization 2.Network Hierarchy 3.Enhanced FDD 6.Implementation 1.Hardware Platform 2.Software Platform 7.Evaluation 1.Cantilever Beam 2.Truss 8.Conclusion

5. Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical co- design approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates (1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and (2) an energy-efficient, multi-level computing architecture specially designed to leverage the multi-resolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a physical beam and simulations of a truss structure demonstrate the system's efficacy in damage localization and energy efficiency. Abstract:

6. Deteriorating Civil Infrastructures Problems with sensors in Wired Technology Growth in Wireless Sensor Networks (WSN’s ) Problems With Centralized Systems viz. High latency and high Energy consumption. Best Solution : Usage of CPS to provide Structural Health Monitoring using de-centralized systems. Lets get started…

7. Related Works.. UC Berkley Project to monitor Golden Gate Bridge Clarkson’s University Implementation on a bridge structure In New York. Problems: Limited data Collection in a time frame. Inadequacy for time constraint events due to large time for data analyzation and collection. Solution: Usage of Distributed Approach based on Damage Localization

8. Damage localization approach :  Physical Aspect using Flexibility based Algorithm  Two stages of Flexibility Algorithm Baseline Structural Model Identification (F b ) Repeatedly collecting data over the passage of time (F)

10. The data flow of a traditional flexibility-based method

11.  Methods of Flexibility-Based Algorithm : Angles-Between-String-and-Horizon flexibility-based method (ASHFM) Axial Strain flexibility-based method (ASFM) Formula for difference in matrix for ASHFM: ∆F = |F b – F| F b is the flexibility matrix on baseline F is computed the newly computed flexibility matrix ∆F is damage matrix

12. Damage Indicator:

13. Distributed Architecture: Described method is good for Centralized networks. But is not energy efficient and good for localization Multi-Level damage Localization: Uses multi level search If damage not found return nodes to sleep If found, Multi-level search is performed and identify adjacent sensors. Key feature: doesn’t activate all sensors at once.

15. Damage localization results on the cantilever beam

16. Network Hierarchy: Roles of nodes: Cluster Member Cluster Head Base Station Accelerometers are used to collect information.

18. Enhanced FDD: Problem: High number of outputs from CSD and SVD which is not energy efficient Solution: Peak Picking Routine in FDD stage which allows each node to independently identify these P natural frequencies solely from local data.

19. Implementation: Hardware: Imote2 wireless Sensor PXA271 Xscale processor 256kb SRAM, 32 MB SDRAM Dynamically clocking from 13-416 MHz Modular stackable platform providing add-on accelerometers

20. Software:  Components: nesC Programming Language TinyOS Operating System ISHM’s ReliableComm DistributedDataAcquireApp  The Two stage Search  Usage of TDMA for time synchronization of collected samples

22. Evaluation/ Deployment : On Cantilever Beam (using ASHFM) On Truss (using ASFM)

23. Cantilever Beam Deployement:

24. Damage localization results on the cantilever beam

25. Truss Deployement: 1.Damage Localization:

26. 2. Energy Consumption:

27. Conclusion: Flexibility-based structural engineering methods that can localize damages at different resolution and costs An efficient, multi-level computing architecture that leverage on the multi-resolution feature of flexibility-based methods