1. Department of Computer Science City University of Hong Kong Department of Computer Science City University of Hong Kong 1 A Statistics-Based Sensor Selection Scheme for Continuous Probabilistic Queries in Sensor Networks Song Han 1, Edward Chan 1, Reynold Cheng 2, and Kam-Yiu Lam 1 Department of Computer Science 1, City University of Hong Kong 83 Tat Chee Avenue, Kowloon, HONG KONG Department of Computing 2 Hong Kong Polytechnic University PQ706, Mong Man Wai Building Hung Hom, Kowloon, Hong Kong

2. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 2 Agenda Introduction Objective System Model Methodology Performance Analysis Conclusion

3. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 3 Introduction Constantly-evolving Environment Uncertainty of Sensor Data Sensor Data are erroneous, unreliable and noisy Database may store inaccurate values Query results can be incorrect

4. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 4 Introduction Statistical Model of Sensor Uncertainty A sensor value can be described more accurately as a Gaussian Distribution Mean µ Variance σ 2 Gaussian Distribution ( ,  2 )

5. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 5 Introduction Probabilistic Queries [SIGMOD03] Represent the imprecision in the value of the data as a probability density function. e.g., Gaussian Augment query answers with probabilities Give us a correct (possibly less precise) answer, instead of a potentially incorrect answer

6. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 6 Introduction Query Quality and Variance Query quality can be improved with lower variance To obtain a smaller σ 2, a simple idea is to use more sensors Get an average of these readings N(µ,σ 2 ) becomes N(µ,σ 2 /n s ), where n s is the number of “redundant” sensors

7. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 7 Introduction Deploying Redundant Sensors Exploit the fact that sensors are cheap Example: 1000 sensors in the room to obtain average temperature Variance decreased by a factor of 1000 Resource Limitation Problem Wireless network has limited bandwidth Sensors have limited battery power Can’t afford too many sensors!

8. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 8 Introduction The Sensor Selection Problem How to decide sensors’ sampling period How many sensors to use for the guaranteed level of query quality? Select which sensors?

9. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 9 Objective Adaptive Sampling Period Decision Scheme Find out the minimum variance of each entity being monitored to meet the probabilistic query quality requirement Select minimum number of “good” sensors to achieve the required variance Decide which sensors should be selected

10. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 10 System Model User Base Station Wireless Network region

11. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 11 System Model User Base Station coordinator

12. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 12 Methodology Adaptive Sampling Period Decision Sensor Selection Process 1. obtain ( , max  2 ) from sensors in region 2. Derive max  2 for each item to satisfy quality 3. Determine sensor nodes to be used

13. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 13 Adaptive Sampling Period Decision The region’s value is changing continuously Periodical Sample will consume excessive system resource Adaptive Sample Scheme for MAX/MIN query ESSENCE: To increase the sampling period for the regions whose values have little effect on the query result.

14. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 14 Adaptive Sampling Period Decision Adaptive Sample Scheme for MAX/MIN query Predicted Sampling Time (PST)

15. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 15 Sensor Selection Process Types of Probabilistic Queries Factors Affecting Query Quality Probabilistic Query Quality An Example: MAX Query Reselection of Sensors for Continuous Queries

16. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 16 Types of Probabilistic Queries MAX/MIN: Which region has max or min temperature? (A, 60%), (B, 30%), (C, 10%) AVG/SUM: What is the average temperature of regions A, B and C? Range Count: How many objects are within 50m from me? COUNT12345 Probability0.10.20.50.150.05

17. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 17 Factors Affecting Query Quality Error distribution of each sensor reading Variance of Gaussian distribution Each query has its own correctness requirement 1.MAX / MIN 2.AVG / SUM 3.Range Count Query

18. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 18 Probabilistic Query Quality Probabilistic queries allow specification of answer quality 1.MIN/MAX: highest probability ≥ P 2.AVG/SUM: variance of answer ≤  T 3.Range count: Top K counts contribute total probability ≥ P

19. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 19 Example: MAX Query Quality requirement: the maximum of p i must be larger than P Let the probability of the i-th region be p i, where f i (s) is the pdf of N(µ,σ 2 )

20. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 20 1.Set the variance of each region (σ 1,σ 2,…, σ n ) to their maximum possible 2.Find p i max, the maximum of p i ’s 3.Find j max, the index of the maximum of i.e., the sensor with greatest impact to p i max Finding variance for MAX

21. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 21 Finding variance for MAX (Cont.) 4.Adjust variance of the j max th sensor σ jmax =σ jmax -∆σ 5.Keep reducing variances until p imax (σ 1,σ 2,…, σ n )  P 6.Return σ 1,σ 2,…, σ n as the variances for the n regions

22. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 22 Deciding Set of Sensors Distribution of n s samples follows normal distribution N(µ,σ 2 /n s ) Compute n s satisfying σ 2 /n s ≤ max variance Compute expected value of E(s) Select n s sensors with the lowest difference of readings from E(s) Only these sensors send their sampled values to the coordinator for computing N(µ,σ 2 /n s )

23. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 23 Reselection of Sensors for CQ Sensor selection runs again when: 1.Probabilistic query quality cannot be met (e.g., due to change of mean) 2.Coordinator detects some sensor is faulty (e.g., its value deviates significantly from the majority) or gives no response after some timeout period

24. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 24 Simulation Model Continuous query length: 1000 sec Sensor sampling interval: 5 sec Number of regions: 4 Number of sensors per region: U [100,150] Sensor error variance range: 5-25% Difference in the values of different regions: 2-10% Quality requirement for MIN/MAX Query : 95% Variance Change Step (∆σ): 0.3

25. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 25 Performance Analysis % in Sensor Selected vs. Difference in Region’s Values Accuracy vs. Difference in Region’s Values

26. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 26 Performance Analysis Accuracy vs. Sensor Error Variance Percentage Percentage of Sensors Selected vs. Sensor Error Variance

27. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 27 Performance Analysis Changes in Value of Regions over Time Percentage of Sensors Selected over Time for Continuous Changes in Values of Regions

28. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 28 Conclusion Accuracy improved through multiple sensors Adaptive Sample Period Decision Scheme Limited network bandwidth allows only limited number of redundant sensors Sensor selection algorithm selects good sensors for reliable readings

29. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 29 Future Work Region Selection Reducing the Computational Complex of the sensor selection progress Differentiating bad sensors from “good ones” that report true surprising events Hierarchical organization of coordinators How to assign coordinators?

30. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 30 References 1. [VSSN04] K.Y. Lam, R. Cheng, B. Y. Liang and J. Chau. Sensor Node Selection for Execution of Continuous Probabilistic Queries in Wireless Sensor Networks. In Proc. of ACM 2nd Intl. Workshop on Video Surveillance and Sensor Networks, Oct, 2004. 2. [SIGMOD03] R. Cheng, D. Kalashnikov and S. Prabhakar. Evaluating Probabilistic Queries over Imprecise Data. In Proc. of ACM SIGMOD, June 2003. 3. [Mobihoc04] D. Niculescu and B. Nath. Error characteristics of adhoc positioning systems. In Proceedings of the ACM Mobihoc 2004, Tokyo, Japan, May 2004. 4. [WSNA03] E. Elnahrawy and B. Nath. Cleaning and Querying Noisy Sensors. In ACM WSNA’03, September 2003, San Diego, California.

31. Department of Computer Science City University of Hong Kong Statistics-Based Sensor Selection Scheme in Sensor Networks 31 Thank you! HAN Song han_song@cs.cityu.edu.hk