Sybot: An Adaptive and Mobile
Spectrum Survey System
for WiFi Networks
Kyu-Han Kim
Deutsche Telekom R&D Lab USA
Alexander W. Min and Kang G. Shin
Real-Time Computing Lab, University of Michigan
ACM MobiCom 2010 © Kyu-Han Kim
Why Spectrum Site-Survey for WiFi?
Coverage and capacity
 Interference or attack
 RF-based localization

WiFi Spectrum Map
Survey system for efficient and accurate monitoring
2
Outline
Limitations and Challenges
 System Design
 Performance Evaluation
 Conclusion

3
Limitations and Challenges

Exhaustive measurements
Comprehensive results [Raniwala03]
 Easy to visualize and analyze data
 Labor-intensive operation


Sensor-based measurements
Continuous monitoring [Yin08]
 Can be inexpensive [Bahl05]
 Inflexible, due to its static location

Accuracy and repeatability
 Efficiency and flexibility
 Adaptation and awareness

4
Outline
Challenges and Limitations
 System Design
 Performance Evaluation
 Conclusion

5
Sybot: Spectrum Survey Robot

Design
Accuracy and repeatability
 Efficiency and flexibility
 Adaptation and awareness


Periodic and aperiodic surveys

Decomposition of a survey task

Extraction of site-specific spectrum characteristics

Controlling key survey parameters to meet requirements
6
Sybot Operations
Periodic & on-demand
Adaptive monitoring
Grid-based spectrum map
Build/control a profile
Thour
Tmin
Diagnostic
GUI
Selective
Mobility
Controller
Diagnostic
MAP

Spectrum
Monitor
Complete
Selective
Complete
Filters
driver
Tday
Scheduler


App. layer





 Metric of Interests
1 m
-  i   rssi ( j )
m j 1
-
i 
1 m
( rssi ( j )   i ) 2

m j 1
7
Complete Monitoring
Complete
Selective
Diagnostic
 Comprehensive measurement
co
rri d
or
AP
AP
AP
ii
Measurement
Unit grid
point
ro o
m
Good
 Cumulate n spectrum maps
- Baseline spectrum map, Bi
 Selection of a grid size
Bad
8
Complete
Selective Monitoring
Selective
Diagnostic
 Cope with temporal variance
 Identify areas with correlation
b(i)  { j | for grid i and j, |  (i)   ( j ) |  }
ido
r
AP
AP
i
co
rr
Measurement
point
ro o
m
Good
Reference grid

Bad
Block
b(i)
1
3
2
4
R, a set of reference grids
b(1)={1,2}
b(2)={1,2,4}
b(3)={3,4}
b(4)={2,3,4}
Candidate R
{1,2,3}
{2,3}
{1,3}
{1,4}
9
Complete
Diagnostic Monitoring
Selective
Diagnostic
AP
ido
r
APi
co
rr
Measurement
point
ro o
m
Good
 Detect areas with deviation
- diff (i) |  i   i | , diff (i)  k
 Update area w/ suspicious grids
 Perform diagnostic movements
Bad
Diagnostic
movements
Suspicious
reference grids
g1
g0
10
Outline
Challenges and Limitations
 System Design
 Performance Evaluation
 Conclusion

11
Performance Evaluation

Prototype


Wireless Router
IEEE 802.11 Router (Linux)
iRobot Create for automation
Sensors
iRobot
Sybot Prototype

Measurement and analysis


Corridors and office rooms
4 weeks and >10,000 points
WiFi Test-bed
12
Generating Repeatable Baseline Map

Complete monitoring result

Histogram of σ
87% of grids
< 4 dBm
13
Reducing Space to Survey

Complete monitoring result

Selected reference grids
Measurement space reduction > 50 %
14
Building a Profile for Efficiency vs. Accuracy

Efficiency Profile

Accuracy Profile
70% reduction
b(i)  { j | for grid i and j, |  (i)   ( j ) |  }
Construction of a trade-off profile per site
15
Effectiveness of Diagnostic Monitoring

Complete monitoring result
OBSTACLE

Diagnostic monitoring result
Measurement space reduction > 56 %
16
Conclusion

Spectrum site-survey for WiFi networks is important
for key network management and services.

Key challenges and limitations in designing a spectrum
survey system have been identified.

Sybot is a novel spectrum survey system that adaptively
uses three complementary monitoring techniques.

A prototype and extensive measurement study show its
feasibility and effectiveness (> 50% reduction).
17
Q&A
Thank You
Contact Information:
kyu-han.kim@telekom.com
18