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

Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table. Presented by: Yan Lu Date: 04/03/2003. Outline. Background Existing Schemas Data-Centric Storage Performance Conclusion References. Background. Sensornet

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

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  1. Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table Presented by: Yan Lu Date: 04/03/2003 CS691-WMU

  2. Outline • Background • Existing Schemas • Data-Centric Storage • Performance • Conclusion • References CS691-WMU

  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 CS691-WMU

  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 CS691-WMU

  5. Existing Schemas • External Storage (ES) • Local Storage (LS) • Data-Centric Storage (DCS) CS691-WMU

  6. External Storage (ES) CS691-WMU

  7. ES Problems CS691-WMU

  8. Local Storage (LS) CS691-WMU

  9. Local Storage (LS) CS691-WMU

  10. 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 CS691-WMU

  11. Put(“elephant”, data) DCS – Example (11, 28) (11,28)=Hash(“elephant”) CS691-WMU

  12. DCS – Example Get(“elephant”) (11, 28) (11,28)=Hash(“elephant”) CS691-WMU

  13. DCS – Example – contd.. elephant fire CS691-WMU

  14. Geographic Hash Table (GHT) • Builds on ♦ Peer-to-peer Lookup Systems ♦ Greedy Perimeter Stateless Routing GHT GPSR Peer-to-peer lookup system CS691-WMU

  15. Peer-to-peer Lookup System • P2P Model ♦ Each user stores a subset of data ♦ Each user can access data of all the other users in the system ♦ More scalable than centralized / hierarchy networks • Schema based on hashing techniques ♦ Content Addressable Networks (CAN) CS691-WMU

  16. Hash(k,v) 3 p 4 5 1 2 Content Addressable Networks • Dynamically partition entire coordinate space, every node owns its distinct zone • Each node stores entire hash table, and adjacent zones information • Data Storage • Data Retrieval (k,v) CS691-WMU

  17. Greedy Perimeter Stateless Routing • The position of destination is known • Nodes know neighbor’s positions • Routing decision is made based on the position of the neighbors and a packet’s destination • Greedy Forwarding • Perimeter Forwarding CS691-WMU

  18. GPSR – Greedy Forwarding CS691-WMU

  19. GPSR - void CS691-WMU

  20. GPSR – Perimeter Forwarding 2 Right Hand Rule: Each node to receive a packet forwards the packet to the next link counterclockwise about itself from the ingress link X Z 3 1 Y CS691-WMU

  21. Home Node and Home Perimeter • Hash function is ignorant of the placement of individual nodes in the topology • Home Node ♦ Geographically nearest the destination coordinates of the hashed location • Home Perimeter ♦ Uses GPSR perimeter mode ♦ Packet traverses the entire perimeter that enclose the destination, before returning to the home node CS691-WMU

  22. 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 CS691-WMU

  23. Solutions • Perimeter Refresh Protocol ♦ Extension for robustness ♦ Handles nodes failure and topology change • Structured Replication ♦ Extension for scalability ♦ Load balance CS691-WMU

  24. Perimeter Refresh Protocol (replica) (replica) E D ♦Key stored at location L. ♦Home node A. ♦Replicas D and E on the home perimeter L F A (home) C B CS691-WMU

  25. PRP – contd.. • Consistency ♦ Every Th seconds, the home node generates a refresh packet, it will take a tour of the current home perimeter ♦ If the receiver is closer to the destination, it consumes that refresh packet, and initiates its own ♦ If not, forwards the refresh packet in perimeter mode ♦ Ensure the node closest to a key’s hash location will become home node CS691-WMU

  26. PRP – contd.. • Persistency ♦Replica node receives a refresh packet, •caches the data in the packet •sets a takeover timer Tt for that key ♦ The timer expires, replica node initiates a refresh for that key and its data, addressed to the key’s hashed location ♦ When home node fails, its replica nodes step forward to initiate refreshes CS691-WMU

  27. PRP – contd.. (replica) (replica) E D ♦Some time after node A fails, replica D initiates a fresh for L L F C B CS691-WMU

  28. PRP – contd.. (replica) (replica) E D ♦Node F becomes the new home node ♦Node F recruits replicas B, C, D and E L F (home) C (replica) (replica) B CS691-WMU

  29. root point level1 mirror points level2 mirror points Structured Replication (SR) Too many events with the same key are detected, key’s home node could become a hotspot Hierarchical decomposition of key hashed location ♦ d, hierarchy depth ♦ mirrors, 4d -1 e.g. d = 2 CS691-WMU

  30. SR – contd.. • Storage cost reduces ♦ A nodestores detected event at the mirror closest to its location • Query cost increases ♦ Route queries to all mirror nodes recursively ♦ Responses traverse the same path, reverse direction CS691-WMU

  31. Comparison Study • Metrics ♦ Total Messages • total packets sent in the sensor network ♦ Hotspot Messages • maximal number of packets sent by any particular node CS691-WMU

  32. 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 CS691-WMU

  33. 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. CS691-WMU

  34. Comparison Study – contd.. DCS is preferable if • Sensor network is large • Dtotal >> max[Dq, Q] CS691-WMU

  35. Performance Total Messages, varying queries CS691-WMU

  36. Performance – contd.. Hotspot Messages, varying queries CS691-WMU

  37. Performance – contd.. Total Messages, varying network size CS691-WMU

  38. Performance – contd.. Hotspot Messages, varying network size CS691-WMU

  39. 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 . CS691-WMU

  40. References • [1] Sylvia Ratnasamy, Brad Karp, Scott Shenker, Deborah Estrin, Ramesh Govindan, Li Yin and Fang Yu, Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table • [2] Sylvia Ratnamy, Paul Francis, Mark Handley, Richard Karp, Scott Shenker, A Scalable Content-Addressable Network • [3] Ion Stoica, Rober Morris, David Karger, M. Frans Kaashoek, Hari Balakrishnan, Chord: A Scalable Peer-to-peer Lookup Service for Internet Application • [4] C.Intanagonwiwat, R.Govindan, and D.Estrin, Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. • [5] Philippe Bonnet, Johannes Gehrke, Praveen Seshadri, Towards Sensor Database Systems CS691-WMU

  41. References • [6] John Heidemann, Fabio Silva, Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, Deepak Ganesan, Building Efficient Wireless Sensor Networks with Low-Level Naming • [7] Sri Kumar, David Shepherd, and Feng Zhao, Collaborative Signal and Information Processing in Micro-Sensor Networks • [8] Brad Karp, H.T.Kung, GPSR: Greedy Perimeter Stateless Routing For Wireless Networks • [9] Li Yin, Fang Yu, Presentation slides for A Scalable Routing Schema Based on Hashing Technique for P2P Wireless Ad Hoc Networks • [10] Chengdu Huang, Presentation slides for Data Storage Schemas in Sensor Networks CS691-WMU

  42. Questions and Comments Thank you! CS691-WMU

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