1. Top-k Monitoring in Wireless Sensor Networks Minji Wu, Jianliang Xu, Xueyan Tang, and Wang-Chien Lee IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 19, NO. 7, JULY 2007

2. Outline Introduction Filter-based Monitoring Approach FILA Overview Query Reevaluation Filter Setting (Uniform versus Skewed) Filter Update (Eager versus Lazy) Performance Study Simulation Setup Eager versus Lazy Filter Update Performance Comparison against TAG and Range Caching Conclusions

3. Introduction Top-k Query Environmental Monitoring A top-k query is issued to find out the nodes and their corresponding areas with the highest pollution indexes for the purpose of pollution control or research study. Network Management A top-k query may be issued to continuously monitor the sensor nodes with the least residual energy.

4. Introduction In traditional database systems Focused on snapshot top-k queries This paper focuses on continuously monitoring top-k queries in sensor networks. Utilize previous top-k result to obtain a new top-k result.

5. Top-1 query TAG (S. Madden et al., OSDI ’ 02) BS C AB t1t1 t1t1 t1t1 t2t2 t2t2 t2t2 t3t3 t3t3 t3t3 35 38 37 43 45 48 51 56 52 4351 4556 4852 A total of nine messages are sent

6. Top-1 query Range Caching (C. Olston et al., SIGMOD ’ 01) BS C AB t1t1 t1t1 t1t1 t2t2 t2t2 t2t2 t3t3 t3t3 t3t3 35 38 37 43 45 48 51 56 52 48 52 48 A total of four messages are sent [39, 47][47, 80] [20, 39]

7. Problem Definition Consider a top-k monitoring query that continuously requests the (ordered) list of sensor nodes R with the highest readings, that is

8. FILA Overview (1) Filter Setting the base station computes a filter [l i, u i ] for each sensor node i and sends it to the node for installation. (2) Query Reevaluation (3) Filter update

9. Query Reevaluation Sensor-initiated updates (1) Internal update (2) Join update (3) Leave update Internal update Leave update Join update Critical bound

10. A Simple Case Consider a simple case where only one sensor-initiated update is received by the base station Only n 1 needs to be probed

11. A Simple Case Only the sensor nodes whose current readings are higher than v 2 ’ respond to the probe

12. General Cases T internal : the set of internal updates T join : the set of join updates T leave : the set of leave updates T : the old top-k set If |T'| = |T| - | T leave | + | T join |  k the new top-k set must be a subset of T' Otherwise, if |T'| < k the nodes that are not in T' have to be probed.

13. An Example of Top-3 Monitoring

14. Another Example of Top-3 Monitoring

16. Filter Setting Uniform filter setting It is simple and favorable when the readings of all sensor nodes follow a similar changing pattern.

17. Filter Setting Skewed filter setting taking into account the changing patterns of sensor readings. Suppose the average time for the reading of node i to change beyond is f i (  ) 1/f i (  ) : the rate of sensor-initiated updates by node i

18. Filter Setting We let every node measure the average delta change d i of their sensor readings at a fixed rate. Skewed filter setting

19. Filter Update Eager filter update If a new filtering window [l i ', u i '] is different from the old one [l i, u i ] then the new filter [l i ', u i '] is immediately sent to node i Lazy filter update If a new filtering window [l i ', u i '] fully contains the old one [l i, u i ], that is, [l i ', u i ']  [l i, u i ] then the base station delays the filter update until node i’s reading violates the old filter [l i, u i ].

20. Performance Study Simulation Setup Energy cost in transmitting a message s : message size  : distance-independent term (50 nj/b)  : coefficient (100 pj/b/m 2 ) q: distance-dependent term ( 2) d: distance Energy cost in receiving a message  is set at 50 nJ/b

21. Performance Study A Sensor initiated update message: Sensor ID : 4 bytes Sensor Reading: 4 bytes A filtering window is characterized by 8 bytes.

22. Network Layouts

23. Real Data Traces Simulated using the real traces provided by the Live from Earth and Mars (LEM) project at the University of Washington. Two kinds of sensor readings are used temperature (TEMP) Dew point (DEW) logged by the station at the University of Washington from August 2004 to August 2005 Total 500000 sensor readings Extract many subtraces starting at different dates Each subtrace contains 20000 readings The subtraces were used to simulate the physical phenomena in the immediate surroundings of different sensor nodes.

24. Real Data Traces

25. Evaluation Metrics Network Lifetime the network lifetime is defined as the time duration before the first sensor node runs out of power. Average Energy Consumption It is defined as the average amount of energy consumed by a sensor node per time unit. Monitoring Accuracy This is defined as the mean accuracy of monitored results against the real results.

26. Eager versus Lazy Filter Update ( multihop, k =10) Network lifetime. Average energy consumption.

27. Eager versus Lazy Filter Update Energy consumption by layer

28. Performance Comparison against TAG and Range Caching (single hop, k =3) Network lifetime. Average energy consumption.

29. Performance Comparison against TAG and Range Caching (single hop, k =3) Monitoring accuracy

30. Performance Comparison against TAG and Range Caching (Multihop, k =10) Network lifetime. Average energy consumption.

31. Performance Comparison against TAG and Range Caching (Multihop, k =10) Monitoring accuracy

32. Conclusion This paper exploited the semantics of top-k query and proposed a novel energy-efficient monitoring approach called FILA. Two filter setting algorithms (that is, uniform and skewed) and two filter update strategies (that is, eager and lazy) have been proposed.

33. Filter Setting Under random walk model 0.5 l The average time for the reading to change beyond  can be expressed as