1. Optimizing Query Processing In Sensor Networks Ross Rosemark

2. Our Research We argue that the sensor network can be viewed as a database where each node is a table. –Under this view.. we argue that a query plan can now dictate how to abstract data from the sensor network rather than a sensor node.

3. Our Goal Given a query Q2 –Define how to process the query What metadata (if any) should be collected What query plan should a node utilize to abstract data from it’s local sensors What is the routing infrastructure of the query Example –Given Q2 Define cost of –Collecting metadata + Execution Cost + Routing cost Define cost of –Not collecting metadata + execution cost + routing cost Choose the lowest cost

4. Idea Evaluate multiple different infrastructures for a query Choose the infrastructure that utilizes the least energy The * operator means aggregation –Not database aggregation (i.e. Sum, Count) but rather aggregation that is discussed in networks

5. Research Issue We use metadata to evaluate different query plans –Metadata becomes an important research issue Which nodes should send metadata to the AP What metadata does the AP require We do an on demand approach in terms of collecting metadata

6. Metadata Collection Algorithm to collect metadata –Only nodes participating in query send metadata Algorithm –Access Point routes query to spatial area utilizing GPSR –Utilizing GPSR query is routed around spatial area –Each node on perimeter of spatial area floods msg inside spatial area –Each node in spatial area sends metadata to the AP utilizing GPSR

7. Metadata For a given query Q1 Initially the access point knows: –The number of nodes in the network (N) –The spatial area of the network (SA) –The query area (QA)…. (we only consider spatial queries) –A histogram that represents the selectivity of each attribute Bad representation –Using this information Query Plan 1 –Estimate metadata collection cost –Estimate query execution cost if metadata is collected –Estimate result collection cost Query Plan 2 –Estimate query execution cost if metadata is not collected –Estimate result collection cost if metadata is not collected –If (Query Plan 1 > Query Plan 2) Choose Query Plan 1 –Else Choose Query Plan 2

8. Metadata Collection When metadata is collected –nodes participating in a query send the selectivity of each of the queries predicates It’s longitude and latitude Example –Query 1-> Select * From Sensors Where Light > 10 –Node participating in query send the selectivity of the predicate Light > 10 –Node participating in query send Longitude/Latitude (i.e. Longitude = 1002.3 Latitude = 2003.1

9. Metadata If query 2 now comes in and covers the same/subset of the spatial area of query 1 then we evaluate the following: –Should we collect more metadata, or just optimize with our current metadata Estimate the metadata collection cost Estimate query execution cost Estimate routing cost This is a repeat of the initial problem –Our estimates are now better though

10. Results Estimated mathematically the energy associated with –Metadata collection –Query Execution Ran simulations to get real values for these metrics In simulations inserted 1 spatial query into the network Ran this experiment varying –The query (spatial area) (predicates) –Topology (5 different topologies) –Metadata (5 different metadata distributions)

11. Query Execution.

12. Metadata Collection

13. Total

14. Questions?