Location Resident Services

Emmanouil KoukoumidisPrinceton University

Group Talk on 04/15/09

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Location Determines Usefulness of Information

• Based on one’s location specific information may be of particular interest:– Nearby parking spots availability.– Traffic ahead and nearby roads.– Potholes and accidents ahead.– Cultural events taking place in the vicinity.– Localized advertisements.

• Several types of information are most useful to people within a certain geographical region and make sense to be provided only to them.

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Location-specific Services

• Location-specific service: Provision of such location specific information.

• Problem:– How to make these location specific services

available?• Solution:

– Location Resident Services.– Ad-hoc distributed coordination scheme.

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Outline

• Motivation• Location Specific Services Design Space

(Related Work).• Our solution: Location Resident Services/ Tri-

fold LRS problem• LRS early results• Future Work

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R

Location Specific Services Design Space

• Problem: Give data D to all interested nodes that enter region R within the service lifetime T.

• Infrastucture-based approach.– Need to deploy enough fixed nodes so as

to cover all region of interest.– $$$

• Grassroots approach.– Nodes collaboratively disseminate the

information to each other.mobile node w/ data

mobile node w/o data

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R

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mobile node without interest in data

Other Approaches

• Abiding geocast [Maihoefer, VANET, 2005].– Server approach.

• Server stores data.• Periodically initiates geocasts

– Pros: Simple– Cons:

• Relay nodes overhead.• Periodic flooding.

– Too wasteful in dense networks. Broadcasts when not necessary.– Nodes might be unreachable in sparse networks.

• Centralized and infrastructure based.

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serverrelay nodes

unicast

broadcast

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Other Approaches

• Abiding geocast [Maihoefer, VANET, 2005].– Election approach.

• Node within region is elected to store data and periodically initiates broadcasts.

• All others just forward it.

– Cons:• Election overhead.• Periodic flooding.• Need multiple elected nodes for fault tolerance ->

Increased flooding.• In sparse network its hard to ensure coordination for

election and fault tolerance.

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Elected node

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Other Approaches

• Abiding geocast [Maihoefer, VANET, 2005].– Neighbor approach.

• All nodes within region store data.• Keep list of encountered nodes that they forwarded

the data to.• On encountering a new one:

– Forward the data.– Add encountered node to list.

– Pros: Fully distributed and fault tolerance scheme.– Cons:

• Maintained lists have local view -> A node will get the data from all nodes that it encountered (wasteful).

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R

Outline

• Motivation• Location Specific Services Design Space

(Related Work).• Our solution: Location Resident Services/ Tri-

fold LRS problem• LRS early results• Future Work

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Location Resident Services Design• Fully decentralized scheme that ensures that the location specific

service will remain(reside) within the region of interest passed on to all interested nodes.

• Other approaches ([Maihoefer, VANET, 2005], Impala [Liu, PPoPP, 2003], Trickle [Levis, NSDI, 2004]) disseminate data to ALL nodes in region.

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R

Approach: • Try to achieve a targeted degree of service

replication across the region R. Only SOME nodes carry the data NOT ALL.

• Target: Maintain N carriers in R.• Service carriers detect nodes that need the data

and update them in an epidemic style (like in Impala [Liu, PPoPP, 2003], Trickle [Levis, NSDI, 2004]). Service carrier

mobile node w/o data

mobile node with no interest in data

LRS Tri-fold problem• Problem: Maintain N carriers in region R.• Nodes move in and out. Nodes may also fail.• In order to maintain fixed number of carriers in R in a

distributed way carriers need to able to:– Estimate total number of carriers in region.– Spawn new carriers when necessary ie too few carriers

currently exist.

1111

1) Service carrier number estimation.2) Spawn policies: When to spawn? 3) Carrier selection criteria: How to spawn?

Carrier Number Estimation • Target number of nodes N →Target node density D. • Nodes as they move try to estimate D.• How to measure carrier densities?

– In epidemic protocols carriers periodically (every T sec) broadcast a small version advertisement.

– Every T carriers calculate:

– So estimation of D can be done with zero communication overhead.

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2π

_#

R

receivedpacketsm

Density Estimators• Cumulative Moving Average (CMA)

k

dkdd

kk

)1(1

• Exponential Moving Average (EMA)• The newer the measurement the more important it is.

dfdf kkd )1(112

1

1

2

21

....1

....

k

k

kkkk

fff

fmfmfmmd

k

mmmm kkkkd 121 ....

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Collaborative Density Estimation1) Nodes as they move estimate node densities:

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R

AB

dfdf AkAkd )1(,1, 12

1

1

2

21,

....1

....

k

k

kkkAk

fff

fmfmfmmd

Collaborative Density Estimation1) Nodes as they move estimate node densities:

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R

AB

dfdf AkAkd )1(,1, 12

1

1

2

21,

....1

....

k

k

kkkAk

fff

fmfmfmmd

2) When nodes meet they exchange:• Their density estimation: dk, A

• Their Estimation Quality Factor ie their confidence about the quality of their estimation. • EQF = 1 + f+ f2 + … + fk

Collaborative Density Estimation1) Nodes as they move estimate node densities:

BA

BBAAA

EQFEQF

EQFdEQFdd

fEQFEQFEQF BAA 1

1,max

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AB

dfdf AkAkd )1(,1, 12

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1

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....1

....

k

k

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2) When nodes meet they exchange:• Their density estimation: dk, A

• Their Estimation Quality Factor ie their confidence about the quality of their estimation. • EQF = 1 + f+ f2 + … + fk

3) Merging of estimations:

More on Quality of Estimations• The quality of an estimation should also be a factor

of node’s mobility.– Node speed– Area travelled

• When merging the more disjoint the areas the nodes have covered the better.

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R

AB

R

A

B

Experimental Setup: ORBIT testbed

• Grid of 20x20 linux PCs with 802.11a/b/g Atheros cards.

• Wireless interfaces configured in 802.11a ad-hoc demo mode.

• Mobility is emulated:• Socket receive() has been wrapped.• Filters out packets from nodes that

are “out of range”.• Range = 100m.

• Mobility scheme:• Random teleport every 10sec.• Currently working on more realistic

schemes.

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Some graphs now…

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CMA, fixed,no, 4

CMA, mobile,no, 4

CMA, fixed, yes, 4

CMA, mobile, yes,4

time (10sec periods)

Nod

es w

ith

estim

ation

off

by >

50%

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time (10sec periods)N

odes

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ff by

>20

%

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• 100 nodes in 850x850 m2 → 4 nodes in range on average.• Density estimations based on CMA.• Collaborative estimation helps carriers get better estimates and faster…

Denisty Estimators Evaluation

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• 100 nodes in 1700x1700 m2 → 1 nodes in range on average.• Density estimations based on CMA.• Collaborative estimation helps carriers get better estimates and faster…

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CMA, fixed,no, 1

CMA, mobile,no, 1

CMA, fixed, yes, 1

CMA, mobile, yes, 1

time (10sec periods)Nod

es w

ith

estim

ation

off

by >

50%

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time (10sec periods)N

odes

wit

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timati

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ff by

>20

%

Spawn Policies• When should a carrier decide to spawn a new one?• Policy 1:

– if (estimated_density < targeted_density) spawn()• Policy 2:

– if (estimated_density < targeted_density && EQF > EQFTHRESHOLD) spawn()• What EQF is good enough to make a reasonable decision to spawn?

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• Policy 3:• Why rush to spawn?• A carrier can estimate based on its trajectory the

amount of time it will stay within region…• So it knows if it can afford to wait before spawning…

Spawn Policies

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yes no CMA 862.3 sp1 50yes no CMA 862.3 sp2 50yes no CMA 862.3 sp3 50

spawn policy 1:spawn policy 2:spawn policy 3:

Carrier Selection

• How should potential carriers be selected among available nodes?– Randomly– Based on estimated residence within

region R.• B, C etc transmit to A their location,

and velocity vector.• A using that can estimate their time of

residence within the area.

• Will evaluate savings achieved in number of spawns necessitated.

R

A

B

C

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Future Work

• Short future:– Improve mobility schemes:

• Random Waypoint model• Real traces from crawdad

– Lots left to do for:• Density estimators• Spawn policies• Carrier selection criteria

• Far future:– Improve node density estimation when in non-optimal

communication environments.– Weak service consistency.

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Conclusions

• Collaborative density estimation can significantly help nodes get better estimations and faster…

• LRS seems a viable scheme for the provision of location-specific services especially when only a subset of the nodes needs the data.

• More work to evaluate the tri-fold problem is necessary…

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Questions ?No

Thank you!(good choice)

Think about it again….

Yes

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