IN-NETWORK VS CENTRALIZED PROCESSING FOR LIGHT DETECTION SYSTEM USING WIRELESS SENSOR NETWORKS

1. IN-NETWORK VS CENTRALIZED PROCESSINGFORLIGHT DETECTION SYSTEMUSINGWIRELESS SENSOR NETWORKS Presentation by, Desai, Bhairav Solanki, Arpan

2. Outline • Introduction • Algorithm and Methodology • Formation of routing topology • In-network aggregation • Centralized aggregation • Experiments and Results • Conclusion • References

3. Introduction

4. Databases Vs Sensor Networks Range Queries – much better idea for sensor networks Additional operators have to be added for Query Language e.g. epoch and duration Continuous long running Queries

5. Data Centric Networking Combination of Querying, storage and routing techniques Works efficiently if we use the combination as application specific rather than generalized like traditional IP based techniques.

6. Challenges Volatile System Append Only Streams High Energy cost of communication Variable data arrival rate at different nodes Limited Storage on nodes

7. Centralized Processing

8. In Network Processing

9. Objective • Implementing In-network aggregation in real environment for a Data-centric application • Comparing In-network and Centralized aggregation approach

10. Algorithm and Methodology

11. Topology Formation Collection Tree Protocol Base Station – Root of the Collection Tree EXTnode = EXTparent + EXTlink to parent where EXT root = 0 Detecting Routing Loops

12. In-network Aggregation • Data aggregation at in-network nodes • Steps required to overcome change in topology

13. Network Behavior

14. Two phases • Node discovery phase • Discovery of topology • Assigning time interval • Aggregation phase • Sense • Aggregate • Forward

15. Assigning time interval

16. Calculate time interval Where Tnode – Time duration of a node D – Total depth of the tree Lnode – Level of the node in the routing tree T – Total epoch duration

17. Processing Plans (a) Sensing leaf node (b) Non-sensing intermediate node (c) Sensing intermediate node

18. Node Operation (Sensing leaf nodes)

19. Node Operation (Sensing intermediate nodes)

20. Node Operation (Non-sensing intermediate nodes)

21. Nodes divided in groups

22. Change in topology

23. Consequences Causes change in depth of the tree That’s why topology reformation is required

24. Centralized Aggregation • No discovery of topology • No assignment of time interval • No steps to overcome change in topology • Aggregation of data at the base-station

25. Node Operation (Sensing leaf nodes)

26. Node Operation (Sensing intermediate nodes)

27. Node Operation (Non-sensing intermediate nodes)

28. Job of the base station • Collect data from all the nodes • Perform aggregation

29. ExperimentsandResults

30. In-network aggregation

31. In-network aggregation

32. In-network aggregation

33. In-network aggregation

34. In-network aggregation

35. In-network aggregation

36. Centralized aggregation

37. Comparing both approaches

38. Comparing Bytes Transmitted

39. Conclusion • Lesser number of Hop counts • Low amount of bytes transmitted • Lower energy consumption

40. References C. Intanagonwiwat, R. Govindan, and D. Estrin, Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks, In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (MobiCO, August 2000) David Gay, Phil Levis, Rob Von Behren, Matt Welsh, Eric Brewer, and David Culler, “The nesC language: A holistic approach to networked embedded systems,” in SIGPLAN Conference on Programming Language Design and Implementation (PLDI’03), June 2003. J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan, “Building Efficient Wireless Sensor Networks with Low-Level Naming,” Proceedings of the ACM Symposium on Operating Systems Principles (SOSP), October 2001. Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, Energy-Efficient Communication Protocols for Wireless Microsensor Networks, Proc. Hawaaian Int'l Conf. on Systems Science, January 2000. Z. Cheng and W. Heinzelman, “Flooding Strategy for Target Discovery in Wireless Networks,” Proceedings of the Sixth ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), September 2003. D. Braginsky and D. Estrin, “Rumor Routing Algorithm for Sensor Networks,” Proceedings of ACM WSNA, September 2002.

41. References J. Bonfils and P. Bonnet, Adaptive and Decentralized Operator Placement for In-Network Query Processing, Telecommunication Systems - Special Issue on Wireless Sensor Networks, January 2004 S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks, 5th Symposium on Operating System Design and Implementation (OSDI 2002), December 2002 Y. Yao and J. Gehrke, The cougar Approach to In-Network Query Processing in Sensor Networks, SIGMOD, March 2002 S. Madden, R. Szewczyk, M.J. Franklin, and D. Culler, Supporting Aggregate Queries Over Ad- Hoc Wireless Sensor Networks, Mobile Computing Systems and Applications, June 2002 S. Ganeriwal, R. Kumar, and M. B. Srivastava, Timing-Sync Protocol for Sensor Networks, Proceedings of ACM SenSys’03, November 2003 TinyOS Mailing list, http://www.tinyos.net/ TinyOS Naming Conventions, http://www.tinyos.net/tinyos-1.x/doc/tutorial/naming.html (TinyOS Introduction 2003) Getting Started with TinyOS and nesC, http://www.tinyos.net/tinyos-1.x/doc/tutorial/lesson1.html (Dissemination Protocol 2004) Dissemination, http://www.tinyos.net/tinyos-2.x/doc/html/tep118.html

42. References (Collection Protocol 2004) Collection, http://www.tinyos.net/tinyos-2.x/doc/html/tep119.html (The Collection Tree Protocol 2004) CTP-Collection Tree Protocol, http://www.tinyos.net/tinyos-2.x/doc/html/tep123.html “Networking Wireless Sensors” by Bhaskar Krishnamachari. Cambridge University Press, 2005 “Wireless Sensor Networks – An Information Processing Approach” by Feng Zhao, Leonidas Guibas. Morgan Kaufmann Publishers, 2004