data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction...
TRANSCRIPT
Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks
through Trajectory Prediction
HyungJune Lee,Martin Wicke, Branislav Kusy,
Omprakash Gnawali, and Leonidas GuibasStanford University
ACM/IEEE IPSN’10April 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
Performance degradesrapidly with increasing mobility
• Data MULEs to collect data as it passes each of the sensor nodes– Wait until mobile sinks come
to collect
Often 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?
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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
4
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
5
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]
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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) 1
nrnssim(xri,xsj )
j1
ns
i1
nr
, i (1,,nr ), j (1,,ns)
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Step III: Probabilistic Representation
1. 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 sequences
L : 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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Summary: Mobility Trajectory Clustersin 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]
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
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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
T3T1
T2T4
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
T3T1
T2T4
T5T6
N
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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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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
Prediction Accuracy of Mobile Trajectory Model
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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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• W: # of future trajectory hops
• Large W means more chance to exploit data stashing scheme
• As W 1, data stashing should break
• Implication
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
Immediate Routing Data Stashing
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• 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)
Running time for a source to compute stashing nodes
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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.”
Two problemsCurrent 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 clusteringOn-line clusteringA multi-tier clustering approach can deal
with extremely large complex networks
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Limitations & Future Works
