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Data centric Storage In Sensor networks

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Data centric Storage In Sensor networks Based on Balaji Jayaprakash s s Overview of the Seminar Introduction Keywords and Terminology Existing Schemes Why Data ... – PowerPoint PPT presentation

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Title: 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)

7
External Storage (ES)
External storage
event
8
Local Storage (LS)
event
event
9
Why do we need DCS?
  • Scalability
  • Robustness against Node failures and Node
    mobility
  • To achieve Energy-efficiency

10
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

11
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

12
Data centric Storage
Elephant Sighting
sourcelass.cs.umass.edu
13
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

14
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

15
Storing Data in GHT
Put (elephant, data)
(12,24)
Hash (elephant)(12,24)
sourcelass.cs.umass.edu
16
Retrieving data in GHT
(12,24)
Hash (elephant)(12,24)
Get (elephant)
17
Geographic Hash Table
Node A
Node B
18
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)

19
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
20
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)

21
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

22
Solutions to the problems
  • Perimeter refresh protocol
  • Structured Replication

23
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

24
Perimeter Refresh Protocol
E
E
Replica
Replica
D
Replica
Replica
D
L
L
F
F
home
A
home
C
Replica
B
C
B
Replica
25
Time Specifications
  • Refresh time (Th)
  • Take over time (Tt)
  • Death time (Td)
  • General rule
  • TdgtTh and TtgtTh
  • In GHT Td3Th and Tt2Th

26
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

27
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.

28
Comparative Study
  • Comparison based on Cost
  • Comparison based on Total usage and Hot spot
    usage

29
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

30
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)
31
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

32
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)

33
Comparison Study contd..
ES LS DCS
Total
Hot spot
34
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)

35
Simulations
  • To check the Robustness of GHT
  • To compare the Storage methods in terms of total
    and hot spot usage

36
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

37
Performance metrics
  • Availability of data stored to Queriers
  • (In terms of success rate)
  • Loads placed on the nodes participating in GHT
    (hotspot usage)

38
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

39
Simulation Results under varying Q
Number of nodes is
constant 10000
40
Simulation results under varying N
Number of Queries Q 50
41
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

42
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

43
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
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