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, 2010
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
Data MULEs to collect data
as it passes each of the
sensor nodes
– Wait until mobile sinks come
• What’s a compromise between two
extremes?
to collect
Performance degrades
Often infeasible if we cannot
• How
exploit the tolerated delay?
rapidly
withto
increasing
control the movement
• How to use regularity of mobility pattern?
mobility
• How to select only a partial set of effective relays?
2
Overview: Predictive Mobile Routing
1. 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
3/34
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
A
• Predictive Data Delivery
– Propose an energy-efficient data delivery scheme to mobile sinks
– Turn even limited knowledge of future connectivity
into networking benefit
4
Outline
[Off-line Learning Phase]
• Mobile Trajectory Model
– In the space of wireless connectivity
– For packet delivery purpose
[Routing]
• Prediction of Future Relay Connectivity
• Predictive Data Delivery to Mobile Users
[Evaluation]
5
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
y
p
s
t
u
x
i b
z
r q
l
a
o
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
– To cluster similar mobile
trajectories
– General trajectory pattern
models explored by a number of
spatial trajectories
6
Constructing trajectory clusters
• Step I. Similarity measure
T1  a l o r t z t b o r t
how similar?
T2  t o p r b o t a
• Step II. Hierarchical clustering

• Step III. Compact representation
7
Step I: Similarity Measure
• Similarity measure (normalized)

F(m,n)
min(m,n)
where F(m,n) is the length of
the longest common subsequence (LCS)
– Not a distance metric


[ Example 1.]
T1  a l o r t z t b o r t
[ Example 2.]
T1  a l o r t z t b o r t
how similar?
how similar?
T2  t o p r b o t a
T2  a z o t
 LCS  o r b o t
 LCS  a z o t
sim(T1,T2 )  5 /min(11,8)  5 /8
sim(T1,T2 )  4 /min(11,4)  1
8
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
n
n
1 r s
sim(r,s) 
  sim(x ri, x sj ), i  (1, ,nr ), j  (1, ,ns )
n r n s i1 j1

4.
Repeat
9
Step III: Probabilistic Representation
R
R
(using ClustalW tool)
Y
- Computation complexity
R
O(N 2 L2 ) where
E
R
N : # of sequences
R
L : the sequence length
R
2. Construct Profile
K
: A probabilistic representation
R

for efficient search in the
R
usage phase
1. Execute multiple
sequence alignment
T
E
E
E
D
E
E
I
E
E
E
E-RT-EACE-GIP----D--S
A C E G I P D S
C-R--E-CEIGIPS---D--S
E I G I P S D S
C--Y-E-C---I--------I
CREC-EICG--IGNG-ND--S
E I C G I G N G N D S
-ED-E-C---IGP---D--S
E C I G P D S
-R--E-CH-CIGK---D--S
C H C I G K D S
-R--E-C---IGC------C I G C
-RI-E-CG--SG-D-LDK-S
E--K-E-CG--IGTD-WD--S
C G S G D L D K S
C-R--E-CN--IG-DGTD--S
G I G T D W D S
C-REPE-CN--IGID-GDKDS
N I G D G T D S
P E C N I G I D G D K D S
Px, j : probability of column j that is character x
10
Summary: Mobility Trajectory Clusters
in an off-line phase
Trajectory sequences
………………
……………………….
………………….
………………………….
……………
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Outline
[Off-line Learning Phase]
• Mobile Trajectory Model
[Routing]
• Prediction of Future Relay Connectivity
• Predictive Data Delivery to Mobile Users
[Evaluation]
12
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:
RCECNC
?
...
Profile:
J
Mobility Profile Database
13
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:
T
T1 2
Given a set of future trajectories of
multiple mobile users,
• Cover all possible future trajectories
• Minimize routing cost to the selected relay
nodes
T4
T5 T6
N
M1
– Find the optimal stashing nodes
for each data source
– Considering
T3
A
M2
15
Optimal Route Selection Using
Predictive Knowledge
• Optimization problem
– For sensor node A,
– Minimize total routing cost
T1
T3
T4
T5 T6
• From sensor node itself
• To the selected stashing nodes
– Subject to
T2
N
M1
A
• Stashing nodes cover all possible
future paths of multiple mobile users
• Solved by LP/IP solvers such as
CPLEX, Gurobi, GLPK, …
M2
16
Outline
[Off-line Phase]
• Mobile Trajectory Model
[Routing]
• Prediction of Future Relay Connectivity
• Predictive Data Delivery to Mobile Users
[Evaluation]
• Dynamic mobility model
– Prediction Accuracy
• Routing performance
–
–
–
–
Scalability
Tolerated Delay
Load Balance
Computation for Selecting Stashing Nodes
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Prediction Accuracy of Mobile Trajectory Model

Validated trajectory clustering
using UMass DieselNet realworld dataset
: 34 buses, 4198 APs, 789 bus
trips around UMass campus
• Prediction method results
in excellent stashing node
selections for real-world
data
18
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!
(lower is
better)
• Data stashing keeps high packet
delivery even for network congestion
• Data stashing performs closely to the
upper bound by perfect prediction
(higher is
better)
– Even limited knowledge of future
trajectories can significantly improve
routing performance!
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Tolerated Delay W
• W: # of future trajectory hops
• Large W means more chance to
exploit data stashing scheme
(lower is
better)
• As W  1, data stashing should
break
• Implication
Trade-off:
Tolerated delay vs.
Network performance
(higher is
better)
21
Load Balance
• Data stashing has a good
load balancing performance
compared to a point-to-point
routing immediately to mobile sinks
Immediate Routing
better
Data Stashing
22
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
(lower is
better)
• Feasible even in a small embedded
platform, taking less than 500ms
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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
• Take-home lesson:
“If you know where someone is going (even uncertainly),
you can deliver data to him more efficiently and reliably.”
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Limitations & Future Works
Two problems
 Current delivery scheme is “best-effort”
 Current clustering
method cannot share common pieces of trajectories
More robust packet delivery:
Multi-tier clustering:
 When the system detects delivery
 Long trajectories can be partitioned
would fail, restashing can
significantly improve robustness
 Trajectory prediction and data
stashing can be more intertwined
short pieces for efficient clustering
 On-line clustering
 A multi-tier clustering approach can deal
with extremely large complex networks
into
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Questions?
HyungJune Lee
abbado@stanford.edu
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