LAST MILE NAVIGATION
USING SMARTPHONES
Yuanchao Shu, Kang G. Shin, Tian He and Jiming Chen
ACM MobiCom
Paris, France
September 2015
Yuanchao Shu  Par is, Fr ance  Sept em ber 10, 2015
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NAVIGATION
THE ACT OF FINDING THEWAY TO GET TO A PLACE
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MODERN NAVIGATION SYSTEMS
WORKING PRINCIPLE
Positioning
Mapping
Path
Planning
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MODERN NAVIGATION SYSTEMS
STATE-OF-THE-ART
• Positioning
– Outdoor: satellite-based, meter-level positioning accuracy.
– Indoor: WiFi, geomagnetism, IMU, Bluetooth.
• Mapping
Does this suffice?
– We need a map.
• Satellite mapping, war-driving, floorplan mapping etc.
• Path Planning
– Extensively studied in robotics and mathematics.
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LACK OF MAP INFORMATION
BOTTLENECK OF NAVIGATION SYSTEMS
Coverage
Global, rural, indoor
Positioning
Path Planning
Maps ?
Resolution
Fidelity
Trails, parking lots
Provisional paths
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Navigation
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LAST MILE NAVIGATION PROBLEM
Navigates one to the vicinity of destination tens of miles away,
but fails to find a feasible path from there to final destination
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•
•
•
•
Yuanchao Shu  Par is, Fr ance  Sept em ber 10, 2015
Plug-and-play
Lightweight
Smartphone-based
Last mile navigation
FollowMe
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BASIC IDEA
OF FOLLOWME
• Leader-Follower
• Exploits “scents/crumbs” left behind by the previous travelers.
Ice Age: Scrat's Continental Crack-Up
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BASIC IDEA
OF FOLLOWME
(leader ’s)
Trace-collection
phase
(follower ’s)
Navigation phase
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DESIGN
Trace Collection & Real-time Navigation
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ARCHITECTURE
OF FOLLOWME
Trace Collection Module
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TECHNICAL DESIGN
REFERENCE TRACE CONSTRUCTION
Detection results
Sensory data
Geo-magnetic
Accelerometer
Gyroscope
Barometer
Steps
Turns
Level changes
Timestamps
Level changes
Turns
Steps
Sensory data
Reference trace
Time
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ARCHITECTURE
OF FOLLOWME
Navigation Module
Trace Collection Module
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ARCHITECTURE
OF FOLLOWME
Navigation Module
Trace Collection Module
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A NAVIGATION EXAMPLE
70
Trace 1
Trace 2
Magnitude (T)
60
D
50
40
A
a
30
d
e
E
Bb
20
C c
10
0
20
40
60
80
Time (s)
100
120
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TECHNICAL DESIGN
WALKING PROGRESS ESTIMATION
• Step-constrained trace synchronization algorithm
– Filter out high-freq. mag. and utilize differential info. to
handle device and usage diversity
– Sync. based on legacy dynamic time warping (DTW)
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TECHNICAL DESIGN
WALKING PROGRESS ESTIMATION
• Step-constrained trace synchronization algorithm
– Filter out high-freq. mag. and utilize differential info. to
handle device and usage diversity
– Sync. based on legacy dynamic time warping (DTW)
• Full knowledge of traces
• Quadratic computational complexity
– Online DTW with linear computation overhead
Reference trace
User trace
Yuanchao Shu  Par is, Fr ance  Sept em ber 10, 2015
(1,1)
‫=]݆[]݅[ܦ‬min(‫݅[ܦ‬−1][݆−1],‫݅[ܦ‬−1][݆],‫݆[]݅[ܦ‬−1])+݀݅‫݅(ݐݏ‬,݆)
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TECHNICAL DESIGN
WALKING PROGRESS ESTIMATION
• Step-constrained trace synchronization algorithm
Step #
– Filter out high-freq. mag. and utilize differential info. to
handle device and usage diversity
– Sync. based on legacy dynamic time warping (DTW)
1
2
1
2
3
4
5
6
7
6
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• Full knowledge of traces
• Quadratic computational complexity
– Online DTW with linear computation overhead
• Step-constrained search space
3
4
5
• Dynamically changing search band
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TECHNICAL DESIGN
WALKING PROGRESS ESTIMATION
• Step-constrained trace synchronization algorithm
2
3
4
5
6
7
7
– Filter out high-freq. mag. and utilize differential info. to
handle device and usage diversity
1
3
– Online DTW with linear computation overhead
5
• Quadratic computational complexity
4
• Full knowledge of traces
6
– Sync. based on legacy dynamic time warping (DTW)
1
2
• Step-constrained search space
• Dynamically changing search band
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IMPLEMENTATION AND EVALUATION
• Implementation
– Android 4.4.2, Samsung Galaxy S5
– Two threads
• Data collection : 50Hz
• Signal processing
• Evaluation
– Four-story campus building
– 5 participants
– 10 different reference traces
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EVALUATION
NAVIGATION ACCURACY
CDF of spatial error in navigation
1.0
0.8
0.6
FollowMe
0.4
Geomagnetism, with PF
0.2
WiFi, with PF
0.0
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Spatial error (m)
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EVALUATION
NAVIGATION PERFORMANCE
Lead time of navigation instructions at different checkpoints
10 s
Checkpoint A
Checkpoint B
Checkpoint C
Checkpoint D
8s
6s
4s
2s
0s
User A
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User B
User C
User D
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EVALUATION
Energy Consumption
• 303.4mA vs 349.5mA (Travi-Navi)
• 224.6mA vs 433.4mA (Travi-Navi)
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CONCLUDING REMARKS
INFRASTRUCTURE FREE
HIGH EFFICIENCY
MINI. USER INVOLVEMENT
No need of floor plans (maps)
Low-power sensors
Low computation
Fast and easy bootstrapping
WiFi/Bluetooth-independent
No action required during NAV
Energy efficient
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Limitation of Experiments
 Device Diversity
 Group of users
 Parameters
 Complicated environment
 Large open indoor space
 Temporal obstacles
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Open Issues
 Scalability
 How to store reference traces
 How to combine traces
 Quality Control
 Based on multiple traces
 Information Privacy
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THANKYOU
Q&A
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