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Data-Centric Storage in Sensornets

Data-Centric Storage in Sensornets. Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin ICSI/UCB/USC/UCLA Presenter: Vijay Sundaram. Outline. Background Existing Schemes Data-Centric Storage Conclusion. Background. Sensornet

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Data-Centric Storage in Sensornets

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

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

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

  6. External Storage (ES)

  7. Local Storage (LS)

  8. Local Storage (LS)

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

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

  12. DCS – Example – contd.. elephant fire

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

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

  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. 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

  18. Comparison Study -contd.. • Dtotal, the total number of events detected • Q , the number of event types queries for • Dq, 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. Comparison Study – contd.. DCS is preferable if • Sensor network is large • Dtotal >> max[Dq, Q]

  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 .

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