1. KDDCS: A Load-Balanced In- Network Data-Centric Storage Scheme for Sensor Networks Mohamed Aly In collaboration with Kirk Pruhs and Panos K. Chrysanthis Advanced Data Management Technologies Lab Dept. of Computer Science University of Pittsburgh ACM CIKM 2006

2. CIKM’06 Mohamed Aly 2 Motivating Application: Disaster Management

3. CIKM’06 Mohamed Aly 3 Disaster Management Sensor Networks Sensors are deployed to monitor the disaster area. First responders moving in the area issue ad-hoc queries to nearby sensors. The sensor network is responsible for answering these queries. First responders use query results to improve the decision making process in the management process of the disaster.

4. CIKM’06 Mohamed Aly 4 Where to Store Sensor Readings?? In-Network storage Events are temporarily stored in the sensor nodes. Best suited for ad-hoc queries issued from users roaming within the network service area. All in-Network storage schemes presented in literature are Data Centric Storage schemes. Why not simply Local Storage??

5. CIKM’06 Mohamed Aly 5 Data-Centric Storage (DCS) Quality of Data (QoD) of ad-hoc queries Define an event owner based on the event value Examples: Distributed Hash Tables (DHT) [Shenker et. al., HotNets’03] Geographic Hash Tables (GHT) [Ratsanamy et. al., WSNA’02] Distributed Index for Multi-dimensional data (DIM) [Li et. al., SenSys’03] Greedy Perimeter Stateless Routing algorithm (GPSR) [Karp & Kung, Mobicom’00] Among the above schemes, DIM has been shown to exhibit the best performance

6. CIKM’06 Mohamed Aly 6 Problem Statement: Storage Hot-Spots in DCS Schemes S1 x є [1,10] S2 x є [10,20] S3 x є [20,30] S4 x є [30,40] 50% 40% 7% 3% Definition: A storage hot-spot occurs when relatively many events are assigned to a small number of sensors. Causes: Irregular data distribution and/or non-uniform sensor deployment.

7. CIKM’06 Mohamed Aly 7 Roadmap Motivating Application: Disaster Management Data-Centric Storage (DCS) Problem Statement: Storage Hot-spots Previous Solutions: The Zone Sharing algorithm (ZS) running on top of DIM The K-D Tree Based DCS scheme (KDDCS) Experimental Results Conclusions

8. CIKM’06 Mohamed Aly 8 Roadmap Motivating Application: Disaster Management Data-Centric Storage (DCS) Problem Statement: Storage Hot-spots Previous Solutions: The Zone Sharing algorithm (ZS) running on top of DIM The K-D Tree Based DCS scheme (KDDCS) Experimental Results Conclusions

9. CIKM’06 Mohamed Aly 9 Distributed Index for Multi-Dimensional Data (DIM) S2 x є [10,20] y є [1,10] S3 x є [10,20] y є [10,20] S1 x є [1,10] y є [1,20] Z = 0 Z = 10 Z = 11 S1 S2S3 A = 0 A = 10 A = 11 1.Distributed physical address assignment 2.Event to bit-code mapping

10. CIKM’06 Mohamed Aly 10 Zone Sharing [Aly et al., DMSN’05] A hot-spot detection and decomposition scheme. Each sensor regularly compares its storage load with that of its neighbors. In case a hot-spot is detected, logical node migration takes place in the K-D tree. Ability to cope with small to medium storage hot-spots.

11. CIKM’06 Mohamed Aly 11 Roadmap Motivating Application: Disaster Management Data-Centric Storage (DCS) Problem Statement: Storage Hot-spots Previous Solutions The Zone Sharing algorithm running on top of DIM The K-D Tree Based DCS scheme (KDDCS) Experimental Results Conclusions

12. CIKM’06 Mohamed Aly 12 K-D Tree Based DCS Scheme (KDDCS) Maps the sensor network into a k-d tree as in DIM. Motivation: Hot-spot “avoidance” instead of “hot-spot detection and decomposition”. How?? Keep the K-D tree balanced all the time.

13. CIKM’06 Mohamed Aly 13 KDDCS versus DIM

14. CIKM’06 Mohamed Aly 14 KDDCS versus DIM Z = 0010Z = 0110 Z = 1000 Z = 11 Z = 000 Z = 00011 Z = 00010 Z = 010 Z = 01111 Z = 1001 Z = 1010Z = 1011 Z = 0011 Z = 100 Z = 000 Z = 0010 Z = 001Z = 0011 Z = 101 Z = 110 Z = 111 DIM resulting k-d tree KDDCS resulting k-d tree with Non-uniform Split Points Orphan Zone Orphan zone

15. CIKM’06 Mohamed Aly 15 KDDCS Balanced Tree Z = 100 Z = 000 Z = 0010 Z = 001Z = 0011 Z = 101 Z = 110 Z = 111 000001010011100101110111

16. CIKM’06 Mohamed Aly 16 KDDCS Abstracted Theoretical Problem The Weighted Split Median Problem: Each sensor s i has an associated value, w i W is the sum of all values w i Goal: sensors to agree on a split value V such that approximately half of the values are larger than V and half of the values are smaller than V A Distributed Algorithm for the Problem: Time complexity:  O(log n) times the network diameter D  O(1) times network diameter if the number of sensors is known a priori within a constant factor Messaging complexity:  Each sensor sends O(log n) sensor IDs

17. CIKM’06 Mohamed Aly 17 Top Level Steps of the Algorithm Elect a leader s l and form a BFS tree T routed at s l of the network The number of sensors n and the aggregate of values W are reported to s l The leader s l collects a log-sized uniform random sample L of the values such that the expected number of times that a value from sensor s i included in the sample is around (w i. Log n) / W The value V is then the median of the reported values in L s l reports V to all sensors

18. CIKM’06 Mohamed Aly 18 KDDCS Components Distributed logical address assignment algorithm 1. Partitioning the region horizontally/vertically by running the algorithm on node y/x coordinates 2. Appending addresses of nodes falling above/below (left/right to) splitting line with 0/1 rightmost bit 3. Reiterating till each node falls by itself in a region (algorithm applied in parallel for different regions) Event to bit-code mapping Use a pre-known distribution of the events or assume storage is uniformly distributed as an easy startup choice Assign ranges to nodes in a way to balance storage among nodes based on the use of non-uniform split points

19. CIKM’06 Mohamed Aly 19 KDDCS Components Definition: Node Storage Its pertinent information and that of all its ancestors in the tree The pertinent information of a node is: Geographic region covered Split line separating its two children The attribute range, attribute split point, associated with this region Incremental event hashing and routing Logical Stateless Routing algorithm (LSR) Operates in O(log n) rounds (depth of the tree) Uses logical addresses of nodes and geographic direction of destination (Unlike GPSR where node addresses are physical)

20. CIKM’06 Mohamed Aly 20 Logical Stateless Routing (LSR)

21. CIKM’06 Mohamed Aly 21 KDDCS Components K-D Tree Rebalancing Algorithm (KDTR) Selection of tree to be rebalanced Find the highest unbalanced node in the K-D tree Rebalancing threshold h O(log n) rounds from the leaves to the root of the K-D tree Tree rebalancing Solve the weighted split median problem on the unbalanced subtree Change the split points intermediate nodes of the unbalanced subtree (the dynamic split points concept)

22. CIKM’06 Mohamed Aly 22 KDTR: K-D Tree Re-balancing Algorithm

23. CIKM’06 Mohamed Aly 23 KDDCS Components 1. Distributed logical address assignment algorithm 2. Event to bit-code mapping 3. Incremental event hashing and routing 4. K-D Tree Rebalancing algorithm (KDTR)

24. CIKM’06 Mohamed Aly 24 Roadmap Motivating Application: Disaster Management Data-Centric Storage (DCS) Problem Statement: Storage Hot-spots Previous Solutions: Zone Sharing Algorithm on top of DIM The K-D Tree Based DCS scheme (KDDCS) Experimental Results Conclusions

25. CIKM’06 Mohamed Aly 25 Simulation Description Compare: KDDCS and DIM. Simulator similar to that of DIM [Li et. al., SenSys’03]. Two phases: insertion & query. Insertion phase Each sensor initiates 5 events (1 event = 1 message). Events forwarded to owners. Query phase Queries of sizes 10% to 80% of the attributes ranges.

26. CIKM’06 Mohamed Aly 26 Experimental Setup ParameterValue Network size50 to 500 sensors Node Capacity10 events Initial energy50 units Energy unitEnergy needed to send 1 event Or Receive 2 events Number of hot-spots1 Hot-spot sizes (X,Y)10% to 80% of the events (X) falling into 5% to 10% of the attribute ranges (Y) Re-balancing threshold3

27. CIKM’06 Mohamed Aly 27 Experimental Results: QoD Result Size of a 50% Query for a network with a (80%, 10%) Hot-Spot

28. CIKM’06 Mohamed Aly 28 Experimental Results: Data Persistence Dropped Events for a network with a (80%, 10%) Hot-Spot

29. CIKM’06 Mohamed Aly 29 Experimental Results: Load Balancing Avg. Node Storage for a network with a (70%, 10%) Hot-Spot Note: Numbers are rounded to the closest integer

30. CIKM’06 Mohamed Aly 30 Experimental Results: Energy Consumption Average Node Energy for a network with a (70%, 10%) Hot-Spot

31. CIKM’06 Mohamed Aly 31 Experimental Results: Event Movements Moved Events for networks with an (X%, 10%) Hot-Spot

32. CIKM’06 Mohamed Aly 32 Conclusions & Future Work Storage Hot-Spots: Serious problem in DCS schemes Previous Solutions: ZS: A storage hot-spots decomposition algorithm running on top of DIM [Aly et al., DMSN’05]. Contribution: KDDCS: A new DCS scheme avoiding the formation of storage hot-spots.

33. CIKM’06 Mohamed Aly 33 Conclusions & Future Work KDDCS Advantages: Achieving a better data persistence by balancing storage responsibility among nodes Increasing the QoD by distributing the storage hot-spot events among a larger number of nodes Increasing the energy savings by achieving a well balanced energy consumption overhead among sensor nodes. Possible Extensions: Compare KDDCS with DIM + ZS Compare KDDCS performance against new DCS schemes like RESTORE [Krishnamurthy et al., INSS’06] Adapt KDDCS to cope with Query Hot-Spots (large number of queries accessing small number of sensors) [Aly et al., MOBIQUITOUS’06]

34. CIKM’06 Mohamed Aly 34 Acknowledgment This work is part of the “Secure CITI: A Secure Critical Information Technology Infrastructure for Disaster Management (S-CITI)” project funded through the ITR Medium Award ANI-0325353 from the National Science Foundation (NSF). For more information, please visit: http://www.cs.pitt.edu/s-citi/

35. CIKM’06 Mohamed Aly 35 Thank You Questions ? Advanced Data Management Technologies Lab http://db.cs.pitt.edu