1. Multi-dimensional Range Query in Sensor Networks Xin Li,Young Jim Kim, Ramesh Govindan (University of Southern California ) Wei Hong (Intel Research Lab at Berkeley) SenSys '03

2. Abstract ► Multi-dimension range query ► event (data) ► attribute -- scalar value ► correlating event ► ► List all events whose temperature lies between 50. and 60., and whose light levels lie between 10 and 15. ► Point query

3. Abstract ► Distributed Index for Multi-dimension data (DIM) (DIM) ► Built upon geographic routing algorithm GPSR (Greedy Perimeter Stateless Routing ► Event insertion and query cost

4. Outline ► Introduction ► Related Work ► The Design of DIM ► Analysis ► Simulation Result ► Conclusion

5. Introduction ► Use multi-dimension range query on attributes to query for event of interest ► Analyzing the growth of marine micro- organisms ► Used by application, for correlating event and trigger action ex: Habit monitor application ex: Habit monitor application

6. Introduction (cont.) ► Pre-computed index ► Centralized index ► In-network distributed data structure for efficiently answering multi-dimensional range queries. efficiently answering multi-dimensional range queries. ► in-network data storage ► locality-preserving geographic hash (this paper focus here) (this paper focus here)

7. The Design of DIM ► Nodes generate Events ► Event: ► Event: ► Multi-dimension range query: ► Goal: Efficiently answer such queries ► DIM function: 1. Locality-preserving geographic hash 1. Locality-preserving geographic hash 2. Underlying geographic routing scheme 2. Underlying geographic routing scheme

8. The Design of DIM ► GPSR algorithm ► Zones ► Associating Zones with Nodes ► Inserting an Event ► Resolving and Routing Queries ► Robustness

9. GPSR ► Enable the delivery to a node at a specified location ► Routing : greedy-mode forwarding ► Perimeter mode traversal -using right hand rule -using right hand rule

10. Zones ► Locality-preserving geographic hash k-d multi-attribute event -> 2-d geographic zone k-d multi-attribute event -> 2-d geographic zone ► Each node owns a zone (part of attribute space) events falling into that space are routed to and store at that node. events falling into that space are routed to and store at that node. ► Rectangle R, sub-rectangle Z ► code(Z) : Zone code ► Sibling zone ► Backup zone

11. Zones (cont.)

13. Associating Zones with Nodes ► Each zone assigned to a single node (ownership) ► Different size zone ► Empty zone owner = its backup zone owner ► Undecided boundary -- Data driven. Later resolved by GPSR ’ s perimeter -- Data driven. Later resolved by GPSR ’ s perimeter traversal when an event is inserted or a query sent traversal when an event is inserted or a query sent

14. Inserting an Event ► Hashing an event to a zone ► Routing an event to its owner ► Resolving undecided zone boundaries during insertion -- Event-driven zone shrinking

17. Resolving and Routing Queries ► Minimum sub-tree contain entire range query ► Compute the prefix of zone code of all zones in the sub-tree ► Split into sub-queries recursively

18. Robustness ► Maintaining zones 1. new node join 1. new node join 2. turn a node off 2. turn a node off --backup zone will take over its zone --backup zone will take over its zone --zone expansion sib ling zone --zone expansion sib ling zone 3. node failure 3. node failure ► Preventing data loss from node failure 1. Local replication 2. Mirror replication 1. Local replication 2. Mirror replication ► Robustness to packet loss -- simple ACK scheme -- simple ACK scheme

19. DIM: Analysis ► Performance metrics: -- Average insertion cost -- Average insertion cost -- Average query delivery cost -- Average query delivery cost ► Distribution of query range size -- uniform distribution -- uniform distribution -- bounded uniform distribution -- bounded uniform distribution -- algebraic distribution -- algebraic distribution -- exponential distribution -- exponential distribution

20. DIM: Analysis (cont.) ► Alternative choices: 1.external storage 2.store event at the node where they are generate.Queries are flooded. generate.Queries are flooded. 3.Geographic Hash Table for Range queries (GHT-R) (GHT-R) insertion cost query cost O( √ n) zero zeroO(n) O(r √ n)

21. DIM: Simulation Result

22. DIM: Simulation Result (cont.)

26. Implementation

27. Conclusion ► Distributed index ► exact match query v.s Range query ► Single attribute v.s Multi attribute ► Query can be issued from any node ► DIM outperform GHT-R

28. Future work ► Adaptation to skewed data distribution ► Node Heterogeneity ► Efficient resolution of existential query ► Port DIM to Mica mote and integrate them into TinyDB