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Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table

Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin, Li Yin and Fang Yu ICSI/UCB/USC/UCLA. Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table. Outline. Background Existing Schemes Data-Centric Storage Conclusion. Background. Sensornet

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Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table

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  1. Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin, Li Yin and Fang Yu ICSI/UCB/USC/UCLA Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table

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

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

  12. DCS – Example – contd.. elephant fire

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

  14. 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 ES LS DS Cost for Storage O(n 1/2)‏ 0 O(n1/2)‏ Cost for Query 0 O(n)‏ O(n1/2)‏ Cost for Response 0 O(n1/2)‏ O(n1/2)‏

  15. 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.

  16. Comparison Study – contd.. ES LS DCS Total Hotspot DCS is preferable if • Sensor network is large • Dtotal >> max[Dq, Q]

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

  18. Review GPSR Greedy forwarding algorithm Perimeter forwarding algorithm

  19. GHT • Home node • to be the node geographically nearest the destination coordinates of the packet • Home perimeter • the entire perimeter that encloses the destionation.

  20. 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

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

  22. Perimeter Refresh Protocol PRP stores a copy of a key-value pair at each node on the home perimeter. PRP generates refresh packets periodically.

  23. Structured Replication Use a hierarchical decomposition of the key space. For a given root r and a given hierarchy depth d, one can compute 4d-1 mirror images of r

  24. Simulation • Success rate • the mean over all queries of the fraction of events returned in each response, divided by the total number of events known to have been stored in the network for that key. • f • the fraction of nodes that remain up for the entire simulation.

  25. Simulation • Stable and Static Nodes

  26. Simulation • Static but Failing Nodes

  27. Simulation System parameters: N, the number of nodes in the system T, the number of event types, T = 100 Q, the number of event types queried for Di, the number of detected events of event type i. Di = 100

  28. Simulation Three version of DCS Normal DCS (N-DCS): a query returns a separate message for each detected event Summarized DCS (S-DCS): A query returns a single message regardless of the number of detected events Structured Replication DCS (SR-DCS)‏

  29. Simulation Test 1: Varying Q

  30. Simulation Test 1: Varying Q

  31. Simulation Test 2: Varying n

  32. Simulation Test 2: Varying n

  33. Conclusion Advantages: In DCS, relevant data are stored by name at nodes within the sensornets. 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.

  34. Conclusion Disadvantages: GHT requires approximate knowledge of a sensornet's boundaries Only supports binary events, not range queries.

  35. Questions?Thanks

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