Data centric Storage In Sensor networks

Based on

Balaji Jayaprakash’s slides

Overview of the Seminar

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

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

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

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

Existing schemes for Storage

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

External Storage (ES)

External

storage

event

Local Storage (LS)

EventData

EventData

event

event

Why do we need DCS?

Scalability Robustness against Node failures and Node

mobility To achieve Energy-efficiency

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

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

Data centric Storage

EventData

Elephant Sighting

source:lass.cs.umass.edu

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

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

Storing Data in GHT

Put (“elephant”, data)

(12,24)

Hash (‘elephant’)=(12,24)

source:lass.cs.umass.edu

Retrieving data in GHT

Get (“elephant”)

Hash (‘elephant’)=(12,24)

(12,24)

Geographic Hash Table

PDA

Node A

Node B

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)

Algorithm (Contd)

GPSR- Packets are marked with position of destinations and each node is aware of its position

Greedy forwarding algorithm Perimeter forwarding algorithm

A

B

A

B

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)

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

Solutions to the problems

Perimeter refresh protocol

Structured Replication

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

Perimeter Refresh Protocol

E

F

B

D

A

C

L

ED

F

C

B

L

home

Replica

ReplicaReplica

Replica

home

Replica

Replica

Time Specifications

Refresh time (Th)

Take over time (Tt)

Death time (Td)

General rule

Td>Th and Tt>Th

In GHT Td=3Th and Tt=2Th

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

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.

Comparative Study

Comparison based on Cost

Comparison based on Total usage and Hot spot usage

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

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)

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

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)

Comparison Study – contd..

ES LS DCS

Total

Hot

spot

nDtotal nDQn q nDnDnQ qtotal

totalDqDQ qDQ

)(summarynQnDnQ total

)(2 summaryQ

Observations from the ComparisonDCS is preferable only in cases where

Sensor network is Large There are many detected events and not all

even types queried

Dtotal>>max(Dq,Q)

Simulations

To check the Robustness of GHT

To compare the Storage methods in terms of total and hot spot usage

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

Performance metrics

Availability of data stored to Queriers

(In terms of success rate)

Loads placed on the nodes participating in GHT (hotspot usage)

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

Simulation Results under varying Q

Number of nodes is

constant= 10000

Simulation results under varying N

Number of Queries Q =50

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”

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

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