location-based data overlay for intermittently-connected networks

Location-based Data Overlay for Intermittently-Connected Networks

Nathanael Thompson, Riccardo Crepaldi, Robin Kravets

Location-based Data Overlay for Intermittently-Connected Networks2

The Urban ExperienceCracking civil infrastructures – I-35 W Mississippi River bridge collapse during rush hour on August 1st, 2007

Traffic delays – Extra commuting time caused by congestion totals ~$7 billion US dollars of loss for the greater ChicagoJ. Hilkevitch, Chicago Tribune August 5, 2008

Increased pollution – Mexico City estimates unhealthy ozone emissions nearly 85% of the year

Revisiting Vehicular Networks


Cars represent an untapped resource Large-scale Geographic coverage Diverse mobility patterns Abundant resources

(energy and storage)

Add sensors Environmental Traffic Local conditions

3

Large-scale Distributed

Sensor Network


What do we do with all of that data?

Distributed location-based services Route planning Real-time carbon

footprint monitoring Live monitoring of critical

infrastructure

Traffic conditionsRoad conditions

WeatherFuel efficiency

NoisePollution


Challenge: Too Much Data! Existing approaches

Upload data to centralized databases Overwhelms wide-area infrastructure

In dense environments, users get ~ 5 – 50 Kbps Need uploads and downloads

Cellular network already overloaded Delayed uploads don’t support live/real time

applications Main Problem

Centralized solutions do not consider location when storing data


Locality of Information Observation

Data is tied to location at which it was sensed

Data storage Maintain at sensed

location Challenges

On which nodes should the data live?

How are the nodes populated with the data?

6


Locus: a Location-based Data Overlay Data tied at specific

location, not device Home location: where

data was created Creates bubbles where

data lives DTN forwarding

techniques Nodes opportunistically

exchange data Keep data as close to

home location as possible

Limited encounter time


A New Challenge: Data Access Access method

Query geographic area

Challenges Enabling on-line access Getting the response

back to the querying node

?


There’s No Place Like Home Store data objects near

home location Maximum utility in home

area Distance-based utility near

home area (buffer) Within buffer, increased

distance decreases utility

Utility-based replication

Home

Utility

Buffer


Data Storage: Location-based Utility If node is in home area

Copy object to all nearby peers

U(node.curr) is high

If in the buffer Copy objects to nodes within

the home area U(peer.current) is high

If leaving the buffer Predict future location Copy objects to nodes

entering the bufferU(peer.future) is high


Data Access: Query Need to send queries to

target location Existing DTN forwarding

focuses on connecting two users

Queries start far from target bubble No utility for distant objects

Will not be forwarded Will be dropped immediately if

buffers are full Need to assign utilities that

move queries through void


Data Access: Query Forwarding Queries compete with data

objects Incorrect utilities lead to

starvation

Distant queries: Seed message to peers using

quota Move message as fast as possible

toward home location Forward with high utility if:

(dist(peer.fut) > dist(node.fut))

Queries close to home location treated like data objects


Data Access: Response Forwarding Any matching node can send

a response Target location is source node’s

location Responses forwarded like

queries

Query source node might be moving Send response to predicted

location Adjust bubble size to account for

prediction error Flood once within range


Evaluating Locus Compare against other object copying policies

combined with basic DTN forwarding Least sent policy, newest, oldest, random

Metrics Distance to home location Number of unique data messages Query success rate

Evaluated in a simulated vehicle network 150 Cars move along 5km X 5km area (map of Chicago) Fixed bubble size of 500m, buffer 300m 1KB data objects every 10s at every node One query every 5s network-wide


Data Distance to Location

Location-based policy does keep messages near home

Location-based policy preserves distance at cost of number of unique messages

But if you can’t find it, it might as well not be there!

50th

Per

cent

ile D

ista

nce

Uni

que

Mes

sage

sTimeTime


Query/Response Success Rate

Keeping data near home location increases query success rate Location-based forwarding increases response success rate Fewer unique messages leads to lower historical queries success

0-12 13-24 24-36 37-48 49-600.00

0.10

0.20

0.30

0.40

0.50

0.60 LocationLeastSentRandomNewestOldest

Query Age (minutes)Fr

actio

n D

eliv

ered

Series10.000.050.100.150.200.250.300.350.40 Location

LeastSentRandomNewestOldest

Frac

tion

Del

iver

ed


Conclusion Location-based data can enable a new class of

applications Data overlay on top of mobile devices is promising

approach By building on DTN forwarding techniques with

location-awareness high query success rate can be achieved

Future directions Should the data live where is it sensed or where it is

needed? Or both? Accuracy vs. proximity Expedited access for frequently queried data Managing accuracy and response time


[email protected]://mobius.cs.illinois.edu/

Thank you!


Data Object Utility Function

X’ X’’

Bubble

Buffer

Map distance to utility value [0.0,1.0]

Parameterized Sigmoid function:

is rate of slope is point utility = 0.5

Buffer zone = X’’-X’ according to

Bubble size = - buffer/2

)(111)( xe

xf

location-based data overlay for intermittently-connected networks

Documents

data livesdtn

specific location data

homestore data objects

locationbased utilityif

devicehome location

connected networkscars

connected networks1

connected networks6data