1. Delay Analysis of Large-scale Wireless Sensor Networks Jun Yin, Dominican University, River Forest, IL, USA, Yun Wang, Southern Illinois University Edwardsville, USA Xiaodong Wang, Qualcomm Inc. San Diego, CA, USA 1

2. Outline Introduction Delay analysis – Hop count analysis One –dimensional Two –dimensional – Source – destination delay analysis Random source –destination Delay from multi-source to sink – Flat architecture – Two-tier architecture Conclusion

3. 1-3 “Cool” internet appliances World’s smallest web server http://www-ccs.cs.umass.edu/~shri/iPic.html IP picture frame http://www.ceiva.com/ Web-enabled toaster + weather forecaster http://news.bbc.co.uk/2/low /science/nature/1264205.st m Internet phones

4. Wireless Sensor network : The next big thing after Internet Recent technical advances have enabled the large-scale deployment and applications of wireless sensor nodes. These small in size, low cost, low power sensor nodes is capable of forming a network without underlying infrastructure support. WSN is emerging as a key tool for various applications including home automation, traffic control, search and rescue, and disaster relief.

5. Wireless Sensor Network (WSN) WSN is a network consisting of hundreds or thousands of wireless sensor nodes, which are spread over a geographic area. WSN has been an emerging research topic – VLSI  Small in size, processing capability – Wireless  Communication capability – Networking  Self-configurable, and coordination

6. WSN organization Flat vs. hierarchical Homogenous vs. Heterogeneous

7. Delay is important for WSN It determines how soon event can be reported. Delay is determined by numerous network parameters: node density, transmission range; the sleeping schedule of individual nodes; the routing scheme, etc. If we can characterize how the parameters determine the delay, we can choose parameters to meet the delay requirement. 7

8. Outline Introduction Delay analysis – Hop count analysis One –dimensional Two –dimensional – Source – destination delay analysis Random source –destination Delay from multi-source to sink – Flat architecture – Two-tier architecture Conclusion

9. Our approach Firstly, we try to characterize how network parameters such as node density, transmission range determine the hop count; Then we consider typical traffic patterns in WSN, and then characterize the delay. Random source to random destination Data aggregation in two-tier clustering architecture

10. Outline Introduction Delay analysis – Hop count analysis One –dimensional Two –dimensional – Source – destination delay analysis Random source –destination Delay from multi-source to sink – Flat architecture – Two-tier architecture Conclusion

11. Modeling Randomly deployed WSN is modeled as: – Random geometric graph – 2-dimensional Poisson distribution Nodes are deployed randomly. The probability of having k nodes located with in the area of around the event :

12. Shortest path routing: One dimensional case At each hop, the next hop is the farthest node it can reach. :Transmission range r: per-hop progress 12

13. Two-dimensional case Per-hop progress

14. 14/50 Average per-hop progress in 2- D case Average per-hop progress as node density increases

15. Numeric and simulation results Hop count between fixed S/D distance under various transmission range It shows that our analysis can provide a better approximation on hop count than. 15

16. Hop count simulations Hop count between various S/D distance It shows that our analysis can provide a better approximation on hop count than.

17. Outline Introduction Delay analysis – Hop count analysis One –dimensional Two –dimensional – Source – destination delay analysis Random source –destination Delay from multi-source to sink – Flat architecture – Two-tier architecture Conclusion

18. Per-hop delay and H hop delay In un-coordinated WSN, per-hop delay is a random variable between 0 and the sleeping interval (T s ). Per-hop delay is denoted by d:

19. Random source/dest traffic Hop count between random S/D pairs Distance distribution between random S/D pairs in a square area of L*L: 19

20. Heterogeneous WSN Sensor nodes might have different capabilities in sensing and wireless transmission. http://intel-research.net/berkeley/features/tiny_db.asp

21. Random deployment of heterogeneous WSN N 1 = 100 N 2 = 300 L = 1000m 21

22. 22/50 Modeling The deploying area of WSN: a square of (L*L). The probability that there are m nodes located within a circular area of is: Node density of Type I and Type II nodes:

23. 2-tier structure Type II node chooses the closest Type I node as its clusterhead: Voronoi diagram 23

24. 24/50 Distance distribution PDF of the distance to from Type II sensor node to its clusterhead Distance distribution between a Type II sensor node to its closest Type I sensor node: Average distance:

25. Average delay in 2-tier WSN Average delay: Per-hop progress 25

26. 26/50 Summary on delay analysis The relationship between node density, transmission range and hop count is obtained. Per-hop delay is modeled as a random variable. Delay properties are obtained for both flat and clustering architecture.

27. 27/50 Conclusion Analysis delay property in WSN; It covers typical traffic patterns in WSN; The work can provide insights on WSN design.

28. Thanks. Questions? 28

29. Random source to central sink node 29

30. Incremental aggregation tree 30

31. 31 Hop count analysis (Key assumptions)