Data Stashing: Energy-Efficient Information Delivery to Mob - PowerPoint Presentation

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Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction

HyungJune Lee

,

Martin

Wicke

,

Branislav

Kusy

,

Omprakash

Gnawali

, and

Leonidas

Guibas

Stanford University

ACM/IEEE IPSN’10

April 15, 2010Slide2

Traditional Data Delivery to Mobile Sinks in Wireless Ad-Hoc/Sensor Networks

Immediate

delivery from data source to mobile sinks

Proactive scheme: DSDV, OLSRReactive scheme: DSR, AODVPerformance degradesrapidly with increasing mobility

Data MULEs to collect data as it passes each of the sensor nodesWait until mobile sinks come to collectOften infeasible if we cannot control the movement

2

?

What’s a compromise between two extremes?

How to exploit the tolerated delay?

How to use regularity of mobility pattern?

How to select only a partial set of effective relays?Slide3

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Overview: Predictive

Mobile Routing

1. Trajectory PredictionAnticipated trajectory nodes2. Data request and trajectory announcement

3. Stashing node selection

To cover

the likely paths and minimize the routing cost

4. Data stashing

5. Data collection by mobile nodes Slide4

Summary of Contributions

Predictive Model of Users’ Trajectories

In the space of

wireless connectivityCapture Long-term behavior (in minutes) a set of the future connected relays

Predictive Data Delivery Propose an energy-efficient data delivery scheme to mobile sinksTurn even limited knowledge of future connectivity into networking benefit

A

4Slide5

Outline

[Off-line Learning Phase]

Mobile Trajectory Model

In the space of wireless connectivityFor packet delivery purpose[Routing]Prediction of Future Relay ConnectivityPredictive Data Delivery to Mobile Users[Evaluation]

5Slide6

Capturing Mobile Trajectory Patterns

Background

Trajectory: a sequence of node associations on a given spatial path

Trajectories from the same spatial trajectory are not necessarily identicalDue to imperfect links and radio signal strength fluctuationsGoal

To cluster similar mobile trajectories General trajectory pattern models explored by a number of spatial trajectories

a

l

q

o

r

t

z

b

p

y

u

i

x

s

T

=

a l o r t z b p y u

T’

=

a l q o r z s p i u z

T’’

=

a q r t z t s b y i x

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Constructing trajectory clusters

Step I. Similarity measure

Step II. Hierarchical clustering

Step III. Compact representation

7Slide8

Step I: Similarity Measure

Similarity

measure (normalized)

Not a distance metric

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Step II. Hierarchical Clustering

Hierarchical clustering :

Every point is its own cluster

Find most similar pair of clusters

Merge it into a parent cluster

Calculate the average similarity between objects in two clusters

Repeat

9Slide10

Step III: Probabilistic Representation

Execute multiple sequence

alignment

(using ClustalW

tool)- Computation complexity

Construct Profile

: A probabilistic representation

for efficient search in the usage phase

R T E A C E G I P D S

R E C E I G I P S D SY E C I R E C E I C G I G N G N D S

E D E C I G P D SR E C H C I G K D SR E C I G C

R I E C G S G D L D K SK E C G I G T D W D SR E C N I G D G T D S

R E P E C N I G I D G D K D S

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-

RT-EACE-GIP----D--S

-R--E-CEIGIPS---D--S

--Y-E-C---I---------

REC-EICG--IGNG-ND--S

-ED-E-C---IGP---D--S

-R--E-CH-CIGK---D--S

-R--E-C---IGC-------

-RI-E-CG--SG-D-LDK-S

--K-E-CG--IGTD-WD--S

-R--E-CN--IG-DGTD--S

-REPE-CN--IGID-GDKDSSlide11

Summary: Mobility Trajectory Clustersin an off-line phase

Trajectory sequences

………………

……………………….

………………….………………………….……………11Slide12

Outline

[Off-line Learning Phase]

Mobile Trajectory Model

[Routing]Prediction of Future Relay ConnectivityPredictive Data Delivery to Mobile Users[Evaluation]

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Prediction of Future Relay Connectivity

Given a partial test sequence,

1) First find the closest

cluster A variant of Smith-Waterman algorithm for local matchingWith the largest

F(*,*) among all profiles2) Find the highly overlapped region

Test sequence

:

Profile

:

R C E C N C

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Mobility Profile Database

J

. . .

?Slide14

Prediction of Future Relay Connectivity

3) Obtain the most probable subsequences starting

from

J+1 through J

+W

J

W

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Optimal Route Selection Using Predictive Knowledge

Data stashing:

Given a set of future trajectories of

multiple mobile users,Find the optimal stashing nodes for each data sourceConsidering Cover all possible future trajectoriesMinimize routing cost to the selected relay nodes

M1

M

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Optimal Route Selection Using Predictive Knowledge

Optimization

problem

For sensor node A, Minimize total routing cost From sensor node itself To the selected stashing nodesSubject toStashing nodes cover all possible

future paths of multiple mobile usersSolved by LP/IP solvers such as CPLEX, Gurobi, GLPK, …

M1

M

2

A

T

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1

T

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16Slide17

Outline

[Off-line Phase]

Mobile Trajectory Model

[Routing]Prediction of Future Relay ConnectivityPredictive Data Delivery to Mobile Users

[Evaluation]Dynamic mobility modelPrediction AccuracyRouting performance ScalabilityTolerated Delay Load BalanceComputation for Selecting Stashing Nodes

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Validated trajectory clustering using UMass

DieselNet

real-world dataset

: 34 buses, 4198 APs, 789 bus trips around UMass campus

Prediction method results in excellent stashing node selections for real-world dataPrediction Accuracy of Mobile Trajectory Model

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Simulation Setup for Routing

TOSSIM under ‘

meyer

-light’ interference830x790 m2

716 nodes20 mobile trajectoriesVehicle moves at a random speed N(30, 52

) km/hVehicle sends a beacon every 1 sec

Each sensor node has data to deliver to mobile sinks19Slide20

Scalability depending on # of mobile sinks

Data stashing consumes less energy than

immediate point-to-point routingScalable with # of mobile sinks!Data stashing keeps high packet delivery even for network congestion

Data stashing performs closely to the upper bound by perfect prediction Even limited knowledge of future trajectories can significantly improve routing performance! (lower is better)

(higher is better)

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W

: # of future trajectory

hops

Large W means more chance to exploit data stashing scheme

As W  1, data stashing should breakImplication

Trade-off:

Tolerated delay vs.

Network performance

Tolerated Delay W

(lower is better)

(higher is better)

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Data stashing has a good

load balancing

performance compared to a point-to-point routing

immediately to mobile sinks

Load Balance

better

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Immediate Routing

Data Stashing Slide23

PC: Dell Precision 390

(2.4 GHz Core 2 Duo)

Small Embedded: fit-PC2

(Intel Atom Z530 1.6GHz)Measured running time for solving the optimization problem - binary integer programFeasible even in a small embedded platform, taking less than 500ms

(lower is better)

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Running time for a

source to compute stashing nodes Slide24

Conclusion

Dynamic mobile trajectory model in the space of

wireless connectivity, capturing wireless volatility

Mobile data delivery can be improved through mobility pattern learning and predictionEven limited knowledge of the future trajectory can improve networking performanceTake-home lesson:“If you know where someone is going (even uncertainly), you can deliver data to him more

efficiently and reliably.”24Slide25

Two problems

Current delivery scheme is “best-effort”

Current clustering method cannot share common pieces of

trajectories

More robust packet delivery:

When the system detects delivery would fail,

restashing

can significantly improve

robustness

Trajectory prediction and data stashing can be more intertwined

Multi-tier clustering:

Long trajectories can be

partitioned into short pieces

for efficient

clustering

On-line clustering

A

multi-tier

clustering approach can deal with extremely large complex networks

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Limitations & Future

WorksSlide26

Questions?

HyungJune Lee

abbado@stanford.edu

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