1. 1 Data-Centric Storage in Sensornets Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin ICSI/UCB/USC/UCLA Presenter: Vijay Sundaram

2. 2 Outline Background Existing Schemes Data-Centric Storage Conclusion

3. 3 Background Sensornet ♦ A distributed sensing network comprised of a large number of small sensing devices equipped with processor memory radio ♦ Great volume of data Data Dissemination Algorithm ♦ Scalable ♦ Self-organizing ♦ Energy efficient

4. 4 Observations/Events/Queries Observation ♦ Low-level output from sensors ♦ E.g. detailed temperature and pressure readings Event ♦ Constellations of low-level observations ♦ E.g. elephant-sighting, fire, intruder Query ♦ Used to elicit the event information from sensornets ♦ E.g. locations of fires in the network Images of intruders detected

5. 5 Existing Schemes External Storage (ES) Local Storage (LS) Data-Centric Storage (DCS)

6. 6 External Storage (ES)

7. 7 Local Storage (LS)

8. 8

9. 9 Data-Centric Storage (DCS) Events are named with keys DCS provides (key, value) pair DCS supports two operations: ♦ Put (k, v) stores v ( the observed data ) according to the key k, the name of the data ♦ Get (k) retrieves whatever value is stored associated with key k Hash function ♦ Hash a key k into geographic coordinates ♦ Put() and Get() operations on the same key k hash k to the same location

10. 10 DCS – Example (11, 28) Put(“elephant”, data) (11,28)=Hash(“elephant”)

11. 11 DCS – Example (11, 28) (11,28)=Hash(“elephant”) Get(“elephant”)

12. 12 DCS – Example – contd.. elephant fire

13. 13 Geographic Hash Table (GHT) Builds on ♦ Peer-to-peer Lookup Systems ♦ Greedy Perimeter Stateless Routing GHT GPSR Peer-to-peer lookup system

14. 14 Problems Not robust enough ♦ Nodes could move (new home node?) ♦ Home nodes could fail Not scalable ♦ Home nodes could become communication bottleneck ♦ Storage capacity of home nodes

15. 15 Solutions Perimeter Refresh Protocol ♦ Extension for robustness ♦ Handles nodes failure and topology change Structured Replication ♦ Extension for scalability ♦ Load balance

16. 16 Comparison Study Metrics ♦ Total Messages total packets sent in the sensor network ♦ Hotspot Messages maximal number of packets sent by any particular node

17. 17 Comparison Study - contd.. Assume ♦ n is the number of nodes ♦ Asymptotic costs of O(n) for floods O(n 1/2 ) for point-to-point routing ESLSDS Cost for StorageO(n 1/2 )0 Cost for Query0O(n)O(n 1/2 ) Cost for Response0O(n 1/2 )

18. 18 Comparison Study -contd.. D total, the total number of events detected Q, the number of event types queries for D q, the number of detected events of event types No more than one query for each event type, so there are Q queries in total. Assume hotspot occurs on packets sending to the access point.

19. 19 Comparison Study – contd.. ESLSDCS Total Hotspot DCS is preferable if  Sensor network is large  D total >> max[D q, Q]

20. 20 Conclusion In DCS, relevant data are stored by name at nodes within the sensornets. GHT hashes a key k into geographic coordinates, the key-value pair is stored at a node in the vicinity of the location to which its key hashes. To ensure robustness and scalability, DCS uses Perimeter Refresh Protocol (PRP) and Structured Replication (SR). Compared with ES and LS, DCS is preferable in large sensornet.