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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
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Routing
Evaluation Conclusion
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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?
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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
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Routing
Evaluation Conclusion
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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
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Mobility Trajectory Model
Routing Evaluation Conclusion
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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
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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
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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
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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)
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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
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Mobility Trajectory Clustersin an off-line phase
Trajectory sequences……………………………………….………………….………………………….……………
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Routing
Prediction of Future Connectivity Model Prediction Data Delivery to Mobile Users
Evaluation Conclusion
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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 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
. . .?
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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 source
Considering Cover all possible future trajectories Minimize routing cost to the
selected relay nodes
M1
M2
A
T3T1T2
T4
T5T6
N
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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 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
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Routing
Evaluation Dynamic Mobile Model Routing Performace
Conclusion
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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
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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
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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)
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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)
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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
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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)
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Outline Motivation Contributions Proposed Protocol
Offline Learning Phase Routing
Evaluation Conclusion
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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
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