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Data-Centric Storage in Sensor Networks: Robustness and Scalability for Efficient Data Management

This presentation discusses data-centric storage (DCS) in sensor networks, focusing on its advantages over traditional external and local storage schemes. DCS efficiently stores observed data as key-value pairs using geographic hashing, enhancing data retrieval processes in large, distributed sensing environments. The outline includes background information, existing storage schemes, and the conclusion that DCS, incorporating perimeter refresh protocols and structured replication, is particularly effective for managing substantial data influx in extensive sensor networks.

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Data-Centric Storage in Sensor Networks: Robustness and Scalability for Efficient Data Management

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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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