hyungjune lee, martin wicke , branislav kusy , omprakash gnawali , and leonidas guibas

Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks

through Trajectory Prediction (IPSN 2010)HyungJune Lee, Martin Wicke, Branislav Kusy, Omprakash Gnawali,

and Leonidas GuibasStanford University, University of California, CSIRO ICT Centre

2011/03/14, Junction

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Routing

Evaluation Conclusion

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

from data source to mobile sinks Proactive scheme: DSDV,

OLSR Reactive scheme: DSR,

AODV

Performance degradesrapidly with increasing mobility

Data MULEs to collect data as it passes each of the sensor nodes Wait until mobile sinks

come to collectOften infeasible if we

cannot control the movement

• 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?

Overview: Predictive Mobile Routing1. Trajectory Prediction

Anticipated trajectory nodes

2. 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

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Routing

Evaluation Conclusion

Summary of Contributions Predictive Model of Users’ Trajectories

In the space of wireless connectivity Capture

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

Predictive Data Delivery Propose an energy-efficient data delivery scheme to

mobile sinks Turn even limited knowledge of future connectivity

into networking benefit

A

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Mobility Trajectory Model

Routing Evaluation Conclusion

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 identical Due to imperfect links and

radio signal strength fluctuations

Goal To cluster similar mobile

trajectories General trajectory pattern

models explored by a number of spatial trajectories

al

q

o

rt

zb

py

uix

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

Constructing trajectory clusters Step I. Similarity measure

Step II. Hierarchical clustering

Step III. Compact representation

T1 a l o r t z t b o r t how similar?T2 t o p r b o t a

Step I: Similarity Measure Similarity measure

(normalized)

Not a distance metric

F(m,n)

min(m,n)

where F(m,n) is the length of the longest common subsequence (LCS)

[ Example 1.]T1 a l o r t z t b o r t how similar?T2 t o p r b o t a

LCS o r b o t

[ Example 2.]T1 a l o r t z t b o r t how similar?T2 a z o t

LCS a z o t

sim(T1,T2) 5 /min(11,8) 5 /8

sim(T1,T2) 4 /min(11,4) 1

Step II. Hierarchical Clustering• Hierarchical clustering :

Every point is its own cluster

1. Find most similar pair of clusters

2. Merge it into a parent cluster

3. Calculate the average similarity between objects in two clusters

4. Repeat

sim(r,s) 1nrns

sim(xri,xsj )j1

ns

i1

nr

, i (1,,nr ), j (1,,ns)

Step III: Probabilistic Representation1. Execute multiple

sequence alignment(using ClustalW tool)- Computation complexity

2. Construct Profile: A probabilistic

representation for efficient search in the usage phase

R T E A C E G I P D SR 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 SE 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 SR E P E C N I G I D G D K D S

O(N 2L2) where N : # of sequencesL : the sequence length

Px, j : probability of column j that is character x

-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-GDKDS

Mobility Trajectory Clustersin an off-line phase

Trajectory sequences……………………………………….………………….………………………….……………

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Routing

Prediction of Future Connectivity Model Prediction Data Delivery to Mobile Users

Evaluation Conclusion

Prediction of Future Relay Connectivity Given a partial test

sequence,

1) First find the closest cluster A variant of Smith-

Waterman algorithm for local matching

With the largest F(*,*) among all profiles

2) Find the highly overlapped region

Test sequence:

Profile:

R C E C N C

Mobility Profile Database

J

. . .?

Prediction of Future Relay Connectivity

3) Obtain the most probable subsequences starting from J+1 through J+W

J W

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 source

Considering Cover all possible future trajectories Minimize routing cost to the

selected relay nodes

M1

M2

A

T3T1T2

T4

T5T6

N

Optimal Route Selection Using Predictive Knowledge Optimization problem

For sensor node A, Minimize total routing cost

From sensor node itself To the selected stashing nodes

Subject to Stashing nodes cover all possible

future paths of multiple mobile users

Solved by LP/IP solvers such as CPLEX, Gurobi, GLPK, …

M1

M2

A

T3T1T2

T4

T5

N

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Routing

Evaluation Dynamic Mobile Model Routing Performace

Conclusion

Prediction Accuracy of Mobile Trajectory Model

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 data

Simulation Setup for Routing TOSSIM under ‘meyer-light’

interference 830x790 m2

716 nodes 20 mobile trajectories

Vehicle moves at a random speed N(30, 52) km/h

Vehicle sends a beacon every 1 sec Each sensor node has data to deliver

to mobile sinks

Scalability depending on # of mobile sinks Data stashing consumes less

energy than immediate point-to-point routing Scalable 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)

Tolerated Delay W W: # of future trajectory

hops

Large W means more chance to exploit data stashing scheme

As W 1, data stashing should break

ImplicationTrade-off: Tolerated delay vs. Network performance

(lower is better)

(higher is better)

Load Balance Data stashing has a good

load balancing performance compared to a point-to-point routing immediately to mobile sinks

better

Immediate Routing

Data Stashing

Running time for a source to compute stashing nodes

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 program

Feasible even in a small embedded platform, taking less than 500ms

(lower is better)

Outline Motivation Contributions Proposed Protocol

Offline Learning Phase Routing

Evaluation Conclusion

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 prediction

Even limited knowledge of the future trajectory can improve networking performance