Optimizing Sensor Network Boundary Estimation and Localization Algorithms for TMS320C6x

1. Optimizing Sensor NetworkBoundary Estimation and Localization Algorithms for TMS320C6x Kamal Srinivasan Niveditha Sundaram

2. Outline • Motivation • Background • Boundary Estimation • Localization • Project Scope • Approach • Conclusion

3. Motivation • Sensor Networks: Network of wireless enabled sensors • Applications • Environmental monitoring • Constraints • Delay Sensitive • Small memory footprint

4. Boundary Estimation • Estimating boundary of a contaminated field • Oil spill • Biochemical attack • Radioactive and hazardous gas leaks • Algorithms • K-mean • Support Vector Machine (SVM)

5. K-mean clustering Iteratively, • Partition data into K=2 subsets based on initial seeds • Calculate centroid locations Boundary Point Detection

6. Support Vector Machines • Partition data using 2 support lines that maximizes the distance between the 2 data sets Support line equations • w1x + w2y + b = ±1 Boundary line detection • w1x + w2y + b = 0

7. Simulations Estimation using SVM for a rectangular boundary

8. Location Estimation using neighbors (uxi, uyi) F(i) of node i L(i) of node i New L(A) (uxA, uyA) (lxi, lyi) L(i) : location rectangle of node i F(i) : neighbor rectangle of node i (lxA, lyA) Localization

9. Project Scope • Optimizing for TMS320C6x DSP processor • TMS320C6x architecture • 8-way VLIW architecture • 8 functional units

10. Approach • Understanding the dependencies of the algorithm – DG • Software ILP • Loop Transformations • Loop Unrolling • Loop Interchange • Avoiding function call within loops – use function pointers • Tool – Code Composer Studio

11. Sample CCS Snapshot

12. Result preview

13. Conclusion • Analyzed sensor network boundary estimation and localization algorithms • Code optimization is useful for sensor networks