Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks

1. Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks Xueyan Tang Jianliang Xu Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore; Parallel and Distributed Systems, IEEE Transactions onJune 2008

2. Outline • Introduction • Problem Formulation • Single-hop networks • Optimal Data Update Solution (Off-line) • Adaptive Data Update Strategy (On-line) • Adaptive Aggregate Data Update • Multi-hop networks • Performance Evaluation • Conclusion

3. Data Report Problem (1/3)-Single-hop Networks • Consider 10 solar radiation readings 369, 330, 264, 266, 274, 279, 260, 233, 225 • Assume the total energy budget of a sensor is three updates (i.e., send only three updates) • Periodically update strategy • Sends the 1-th, 4-th, and 7-th readings 369, skip, skip, 266, skip, skip, 260, skip, skip • Approximate readings 369, 369, 369, 266, 266, 266, 260, 260, 260 Reconstructed data

4. Data Report Problem (2/3)- Single-hop Networks • Data Error (Deviation) • Exact readings: • 369, 330, 264, 266, 274, 279, 260, 233, 225 • Approximate readings: • 369, 369, 369, 266, 266, 266, 260, 260, 260 • Error = 0+39+105+0+8+13+0+27+35 = 227. error

5. Data Report Problem (3/3)- Single-hop Networks • Better Update Strategy • sends the 1-th, 4-th, and 8-th readings 369, skip, skip, 266, skip, skip, skip, 233, skip • approximate readings 369, 369, 369, 266, 266, 266, 266, 233, 233 • Error = 0+39+0+2+10+15+4?+0+ 8 = 78 error

6. Problem Formulation (1/3)-Single-hop Networks • Problem: Exact readings: 369, 330, 264, 266, 274, 279, 260, 233, 225………… Find M updates such that root-mean-square of collected data error is minimized.

7. Problem Formulation (2/3)- Single-hop Networks • Assume • Exact readings (T: given network lifetime): d1, d2, …, dT • Energy budget (at most): M updates • Data updates at times: v1=1, v2, v3,…, vM • Ex: v1=1  1-th reading (first update) v2=3  3-th reading (second update) • Approximate readings:

8. Problem Formulation (3/3)-Single-hop Networks • Find v1=1, v2, v3,…, vM such that is minimized. where

9. Optimal Data Update Solution (Off-line Version) • Assume that all sensor readings are known a priori • Exact readings d1, d2, …, dT are known • Solve by a dynamic programming algorithm.

10. Dynamic Programming (1/4) • Let be an optimal solution to the (t, m)-optimization problem. • Claim: must be an optimal solution to the (t -1, m -1)-optimization problem.

11. Dynamic Programming (2/4) • Proof • Assume there exists a better solution

12. Dynamic Programming (3/4)

13. Dynamic Programming (4/4) • Let A(t, m) be the minimal achievable total square error to the (t, m)-optimization problem. • Let B(t, m) be the time of the last data update in the optimal solution.

14. Adaptive Data Update Strategy (On-line Version) • Idea • Let the sensor node update a new reading with the base station only when the new reading substantially differs from the last update. • i.e., update only if Example: W = 40 369, 330, 264, 266, 274, 310, 260, 233, 225

15. Adaptive Data Update Strategy (On-line Version) • Issues • The number of updates are decided by W • How to dynamically adjust W • Assume that the energy budgets: 3 updates • Expected data update period : • Once every 3 time units 369, 330, 264, 266, 274, 279, 260, 233, 225

16. Adaptive Data Update Strategy (On-line Version) • Measure the data update period every time a new reading is updated. • Estimate of data update period • Compare with the expected data update period IE :

17. Adaptive Data Update Strategy (Algorithm) Initialization

18. Adaptive Data Update Strategy (Algorithm)

19. Adaptive Aggregate Data Update-Multi-hop networks • Problem in multi-hop networks Node A : receive 6 updates sends 3 updates bottleneck

20. Adaptive Aggregate Data Update-Multi-hop networks Node A : receive 6 updates sends 8 updates Node A : receive 6 updates sends 3 updates

21. Allocating Number of Updates The number of updates that node can send is bottleneck Total energy receive send

22. Allocating Number of Updates-Idea Assume thresholds WA = 3, WB=2, WC=2 22 24 22 |22-20.3| < WA 22 20 19 22 19 21 20 |22-19| > WB |21-20| < WC Round t Round t+1

23. Goal • The objective is to let the sensor nodes send as many updates as possible subject to the energy constraints 6 3 3 3 3 3 3 3 3 6 6 6

24. Update Allocation Algorithm-An Example • ui : unused energy budget • xi: min(xi , xpi) • ci: allocated number of updates • Assume that s = 1 units (send) and v = 1 units (receive) A: ui = 12 (initial) xi= 12/(2+1) = 4 ci = min(4, ∞)=4 ui/xi/ci Round 1

25. Update Allocation Algorithm-An Example • ui : unused energy budget • xi: min(xi , xpi) • ci: allocated number of updates • Assume that s = 1 units (send) and v = 1 units (receive) B: ui = 12 (initial) xi= 12/(3+1) = 3 ci = min(4, 3) = 3 ui/xi/ci Round 1

26. Update Allocation Algorithm-An Example • ui : unused energy budget • xi: min(xi , xpi) • ci: allocated number of updates A: ui = 12-4-6 = 2 xi= 2/(0+1) = 2 ci = min(2, ∞)+4=6 Round 2

27. Update Allocation Algorithm

28. Performance Evaluation • Experimental Setup

29. Performance Evaluation-Single-hop (without aggregation)

30. Performance for Parameter Settings

31. Performance Evaluation-Multi-hop (MAX Aggregation)

32. Performance Evaluation-Multi-hop (Average Aggregation)

33. Conclusion • This paper developed adaptive strategies for both individual and aggregate data collections to make full use of the energy budgets of sensor nodes. • Experimental results show that, compared to the periodic strategy, adaptive strategies significantly improve the accuracy of collected data.