Data centric Storage In Sensor networks

1
Data centric Storage In Sensor networks

• Based on
• Balaji Jayaprakashs slides

2
Overview of the Seminar

• Introduction
• Keywords and Terminology
• Existing Schemes
• Why Data centric Storage?
• Assumptions
• Geographic Hash table
• Comparitive Study
• Conclusion

3
Introduction

• Sensornet
• ? A distributed network comprised of a large
  number of small sensing devices equipped with
• Computation Communication Storage
• ? Great volume of data
• Data Dissemination Algorithm
• ? Energy efficient
• ? Scalable
• ? Self-organizing

4
Keywords and Terminology

• Observation
• ? low-level readings from sensors
• ? e.g. Detailed temperature readings
• Events
• ? Predefined constellations of low-level
  observations
• ? e.g. temperature greater than 75 F
• Queries
• ?Used to elicit information from sensor network

5
Total Usage /Hotspot Usage

• Total Usage
• Total number of packets sent in the
  Sensor network
• Hotspot Usage
• The maximal number of packets send by a
  particular sensor node

6
Existing schemes for Storage

• External Storage (ES)
• Local Storage (LS)
• Data Centric Storage (DCS)

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External Storage (ES)
External storage
event
8
Local Storage (LS)
event
event
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Why do we need DCS?

• Scalability
• Robustness against Node failures and Node
  mobility
• To achieve Energy-efficiency

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Assumptions in DCS

• Large Scale networks whose approximate
  geographic boundaries are known
• Nodes have short range communication and are
  within the radio range of several other nodes
• Nodes know their own locations by GPS or some
  localization scheme
• Communication to the outside world takes place by
  one or more access points

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Data Centric Storage

• Relevant Data are stored by name at nodes
  within the Sensor network
• All data with the same general name will be
  stored at the same sensor-net node.
• e.g. (elephant sightings)
• Queries for data with a particular name are then
  sent directly to the node storing those named
  data

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Data centric Storage
Elephant Sighting
sourcelass.cs.umass.edu
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Geographic Hash Table

• Events are named with keys and both the storage
  and the retrieval are performed using keys
• GHT provides (key, value) based associative memory

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Geographic Hash Table Operations

• GHT supports two operations
• ? Put(k,v)-stores v (observed data) according
  to the key k
• ? Get(k)-retrieve whatever value is
  associated with key k
• Hash function
• ? Hash the key in to the geographic
  coordinates
• ? Put() and Get() operations on the same
  key k hash k to the same location

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Storing Data in GHT
Put (elephant, data)
(12,24)
Hash (elephant)(12,24)
sourcelass.cs.umass.edu
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Retrieving data in GHT
(12,24)
Hash (elephant)(12,24)
Get (elephant)
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Geographic Hash Table
Node A
Node B
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Algorithms Used By GHT

• Geographic hash Table uses GPSR for Routing
• (Greedy Perimeter stateless routing)
• PEER-TO-PEER look up system
• (data object is associated with key and each
  node in the system is responsible for storing a
  certain range of keys)

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Algorithm (Contd)

• GPSR- Packets are marked with position of
  destinations and each node is aware of its
  position
• Greedy forwarding algorithm
• Perimeter forwarding algorithm

B
B
A
A
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Home Node and Home perimeter

• In GHT packet is not addressed to specific node
  but only to a specific location, hence only
  perimeter mode is used
• The packet will traverse the entire perimeter
  that encloses the destination
• before being consumed at the home node (the
  node closest to destination)

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Problems

• Robustness could be affected
• Nodes could move (i.d. of Home node?)
• Node failure can Occur
• Deployment of new Nodes
• Not Scalable
• Storage capacity of the home nodes
• Bottleneck at Home nodes

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Solutions to the problems

• Perimeter refresh protocol
• Structured Replication

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Perimeter refresh protocol

• Replicates stored data for key k at nodes around
  the location to which k hashes, and ensures that
  one node is chosen consistently as the home node
  for that K consistency persistence
• By hashing keys, GHT spreads storage and
  communication load between different keys evenly
  throughout the sensornet

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Perimeter Refresh Protocol
E
E
Replica
Replica
D
Replica
Replica
D
L
L
F
F
home
A
home
C
Replica
B
C
B
Replica
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Time Specifications

• Refresh time (Th)
• Take over time (Tt)
• Death time (Td)
• General rule
• TdgtTh and TtgtTh
• In GHT Td3Th and Tt2Th

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Characteristics Of Refresh Packet

• Refresh packet is addressed to the hashed
  location of the key
• Every (Th) secs the home node will generate
  refresh packet
• Refresh packet contains the data stored for the
  key and routed exactly as get() and put()
  operations
• Refresh packet always travels along the home
  perimeter

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Structured Replication

• Too many events are detected then home node will
  become the hotspot of communication.
• Hierarchical decomposition of the key space
• Structured replication reduces the cost of
  storage and is useful for frequently detected
  events.

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Comparative Study

• Comparison based on Cost
• Comparison based on Total usage and Hot spot
  usage

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Assumptions in comparison

• Asymptotic costs of O(n) for floods and O( n) for
  point to point routing
• Event locations are distributed randomly
• Event locations are not known in advance
• No more than one query for each event type
• (Q Queries in total)
• Assume access points to be the most heavily used
  area of the sensor network

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Comparison based on Cost
Cost External storage (ES) Local storage (LS) Data-centric storage
Cost for Storage O(n) 0 O(n)
Cost for query 0 O(n) O(n)
Cost for Response 0 O(n) O(n)
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Comparison based onHot-spot/Total Usage

• n - Number of nodes
• T - Number of Event types
• Q Number Of Event types queried for
• Dtotal Total number of detected events
• DQ- Number of detected events for queries

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DCS TYPES

• Normal DCS Query returns a separate message for
  each detected event
• Summarized DCS Query returns a single message
  regardless of the number of detected events
• (usually summary is preferred)

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Comparison Study contd..
ES LS DCS
Total
Hot spot
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Observations from the Comparison

• DCS is preferable only in cases where
• Sensor network is Large
• There are many detected events and not all even
  types queried
• Dtotalgtgtmax(Dq,Q)

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Simulations

• To check the Robustness of GHT
• To compare the Storage methods in terms of total
  and hot spot usage

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Simulation Setup

• ns-2
• Node Density 1node/256m2
• Radio Range 40 m
• Number of Nodes -50,100,150,200
• Mobility Rate -0,0.1,1m/s
• Query generation Rate -2qps
• Event types 20
• Events detected -10/type
• Refresh interval -10 s

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Performance metrics

• Availability of data stored to Queriers
• (In terms of success rate)
• Loads placed on the nodes participating in GHT
  (hotspot usage)

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Simulation Results for Robustness

• GHT offers perfect availability of stored events
  in static case
• It offers high availability when nodes are
  subjected to mobility and failures

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Simulation Results under varying Q
Number of nodes is
constant 10000
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Simulation results under varying N
Number of Queries Q 50
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Simulation Results for comparison of 3-storage
methods

• S-DCS have low hot-spot usage under varying Q
• S-DCS is has the lowest hot-spot usage under
  varying n

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Conclusion

• Data centric storage entails naming of data and
  storing data at nodes within the sensor network
• GHT- hashes the key (events) in to geographical
  co-ordinates and stores a key-value pair at the
  sensor node geographically nearest to the hash
• GHT uses Perimeter Refresh Protocol and
  structured replication to enhance robustness and
  scalability
• DCS is useful in large sensor networks and there
  are many detected events but not all event types
  are Queried

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REFERENCES

• Deepak Ganesan, Deborah Estrin, John Heidemann,
  Dimensions why do we need a new data handling
  architecture for sensor networks?, ACM SIGCOMM
  Computer Communication Review,  Volume 33 Issue
  1, January 2003    Scott Shenker, Sylvia
  Ratnasamy, Brad Karp, Ramesh Govindan, Deborah
  Estrin, Data-centric storage in sensornets, ACM
  SIGCOMM Computer Communication Review,  Volume 33
  Issue 1, January 2003
• Sylvia Ratnasamy, Brad Karp, Scott Shenker,
  Deborah Estrin, Ramesh Govindan, Li Yin, Fang Yu,
  Data-centric storage in sensornets with GHT, a
  geographic hash table, Mobile Networks and
  Applications,  Volume 8 Issue 4, August 2003
• Chalermek Intanagonwiwat, Ramesh Govindan,
  Deborah Estrin, John Heidemann, Fabio Silva,
  Directed diffusion for wireless sensor
  networking, IEEE/ACM Transactions on Networking
  (TON),  Volume 11 Issue, February 2003
• R. Govindan, J. M. Hellerstein, W. Hong, S.
  Madden, M. Franklin, S. Shenker, The Sensor
  Network as a Database, USC Technical Report No.
  02-771, September 2002