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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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Presentation Transcript
slide1
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
Outline

Background

Existing Schemes

Data-Centric Storage

Conclusion

background
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

observations events queries
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

existing schemes
Existing Schemes

External Storage (ES)‏

Local Storage (LS)‏

Data-Centric Storage (DCS)‏

data centric storage dcs
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

dcs example
DCS – Example

Put(“elephant”, data)‏

(11, 28)‏

(11,28)=Hash(“elephant”)‏

dcs example1
DCS – Example

Get(“elephant”)‏

(11, 28)‏

(11,28)=Hash(“elephant”)‏

comparison study
Comparison Study

Metrics

♦ Total Messages

• total packets sent in the sensor network

♦ Hotspot Messages

• maximal number of packets sent by any particular node

comparison study contd
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)‏

comparison study contd1
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.

comparison study contd2
Comparison Study – contd..

ES

LS

DCS

Total

Hotspot

DCS is preferable if

  • Sensor network is large
  • Dtotal >> max[Dq, Q]
geographic hash table ght
Geographic Hash Table (GHT)‏

Builds on

♦ Peer-to-peer Lookup Systems

♦ Greedy Perimeter Stateless Routing

GHT

GPSR

Peer-to-peer

lookup system

review gpsr
Review GPSR

Greedy forwarding algorithm

Perimeter forwarding algorithm

slide19
GHT
  • Home node
    • to be the node geographically nearest the destination coordinates of the packet
  • Home perimeter
    • the entire perimeter

that encloses the

destionation.

problems
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

solutions
Solutions

Perimeter Refresh Protocol

♦ Extension for robustness

♦ Handles nodes failure and topology change

Structured Replication

♦ Extension for scalability

♦ Load balance

perimeter refresh protocol
Perimeter Refresh Protocol

PRP stores a copy of a key-value pair at each node on the home perimeter.

PRP generates refresh packets periodically.

structured replication
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

simulation
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.
simulation1
Simulation
  • Stable and Static Nodes
simulation2
Simulation
  • Static but Failing Nodes
simulation3
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

simulation4
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)‏

simulation5
Simulation

Test 1: Varying Q

simulation6
Simulation

Test 1: Varying Q

simulation7
Simulation

Test 2: Varying n

simulation8
Simulation

Test 2: Varying n

conclusion
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.

conclusion1
Conclusion

Disadvantages:

GHT requires approximate knowledge of a sensornet\'s boundaries

Only supports binary events, not range queries.

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