Delay Analysis of Large-scale Wireless Sensor Networks

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

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

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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 wirelesssensor 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.

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

13. Two-dimensional case • Per-hop progress

14. 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 .

16. Hop count between various S/D distance Hop count simulations 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 (Ts). • Per-hop delay is denoted by d:

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

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

21. Random deployment of heterogeneous WSN N1 = 100 N2 = 300 L = 1000m

22. 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

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

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

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

28. Thanks.Questions?

29. Random source to central sink node

30. Incremental aggregation tree

31. Hop count analysis (Key assumptions)