Andrew Leeming is a first year PhD student supervised by Nick Filer. He is researching exploiting context-awareness and mobility in opportunistic networks. Specifically, he aims to use contextual information like a device's mobility, activity, topology, and other sensor data to improve routing in opportunistic networks. This could help address issues with infrastructure-based networks like limited coverage. Leeming has conducted preliminary experiments with an Android phone's accelerometer to detect mobility patterns. He believes incorporating contextual data into routing decisions could create communities and help forwarding in opportunistic networks.

1. Exploiting Context-Awareness and
Mobility in Opportunistic Networks
By Andrew Leeming - First year PhD
leeming@cs.man.ac.uk
Supervised By Nick Filer
nick@cs.man.ac.uk
1Andrew Leeming

2. Introduction
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http://goo.gl/7UIIT
http://goo.gl/WafQw
2011
http://goo.gl/72ZDP

3. Andrew Leeming 3
http://goo.gl/Rnz4W
Introduction
http://goo.gl/OQnld
http://goo.gl/FliiM

4. Problems with Infrastructure
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http://goo.gl/WafQw
Problems
Capacity
Coverage£££
Outages

5. Problems with OppNets
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Problems
Delivery
LatencyEfficiency
Neighbour
Discovery
http://goo.gl/8D3xR
http://goo.gl/g9WYH

6. Opportunistic Routing
 Epidemic
 High delivery but poor power efficiency
 Spray and Wait
 Poor power efficiency in dense networks
 PRoPHET
 Stochastic
 Insensitive to situational changes
 BUBBLE
 Community based
 What else?
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7. What is a Context?
 “The set of attributes that describe the aspects of
the system that can be used to optimise the
process of message delivery” – Musolesi et al
 Categories of contexts
(Schilit et al, Chen et al)
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User
Computing
Physical
Time

8. What is available?
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WiFi
Computing
Cell
Bluetooth
Accelerometer
Physical
Gyroscope
Light
Sensor
Battery
Power
User
Location
Contact
Details
Time
Calendar
Day of
Week
Alarms

9. Contextual Example
 Basic example but with contexts
 Both A and C are PhD students.
 B is their supervisor
 A has a supervisor meeting at 10am
 A also knows C will have a meeting at 11.30am
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C
B
A

10. Contextual Example (2)
 Does this always make B the best relay?
 What if it is Friday at 5pm?
 Is there an office by the building exit?
 What if a PhD student regularly walks
around the building to avoid work?
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C
B
A

11. Recap
 Infrastructure based networks are not
perfect
 Opportunistic networks are not perfect
either
 Routing is one of the biggest issues
 Enable mobile devices to use context to
make a more informed routing decision
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12. Andrew Leeming 12
Current Work

13. Mobility + Topology
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Mobility : High
Topology : Static
Mobility : High
Topology : Dynamic
Mobility : Low / Static
Topology : Static
Mobility : Low / Static
Topology : Dynamic
Sat near a
busy street
Driving your car
Travelling on the
bus or train
At home
In the office

14. Detecting Movement
 Accelerometer on Android phone
 Use machine learning techniques to
classify types of movement
 Stationary
 Walking
 Running
 Moving Vehicle
 etc
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http://goo.gl/iOc3v

15. Activity Recognition
 Previous work in this area includes:
 Bujari et al : Detects pedestrian crossing
points. Also introduces ‘Magnitude’ for
sensor readings independent of orientation
 Bedogni et al : Combines accelerometer and
gyroscope sensors to detect modes of
transport
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http://goo.gl/AMi36

16. Experiment
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17. Example Accelerometer
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18. Example Accelerometer (2)
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19. Topology Detection
 Using any, or all, wireless interfaces
 Bluetooth
 WiFi
 GPRS/UMTS/LTE
 Create a history of nearby nodes or
base stations
 Detect spatio-temporal patterns
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20. Other Contextual Information
 Contact list on phone
 Regular communications
 Length of phone calls
 Calendar appointments
 Morning alarm clock
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http://goo.gl/S3AHy
http://goo.gl/yaj3Y

21. But this isn’t networking?
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http://goo.gl/zBYNk

22. Opportunistic Routing
 Devices can broadcast their context
 Use a combination of the device’s
context and neighbours to decide how
to forward
 Create labelled communities from
common contexts
 Adapt neighbour detection timings
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23. Alternative Applications
 Context-Aware advertising
 Emergency discovery
 Medical activity loggers
 General adaptive behaviour for
applications
 Put phone in silent mode if in a meeting
 Motivating music for exercising
 Reallocate system resources
 Change scanning frequencies
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24. References
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 Mundur, P.; Seligman, M., "Delay tolerant network routing: Beyond epidemic routing," Wireless
Pervasive Computing, 2008. ISWPC 2008. 7-9 May 2008
 Spyropoulos, T; Psounis, K; Raghavendra, C . 2005. Spray and wait: an efficient routing scheme for
intermittently connected mobile networks. In Proceedings of the 2005 ACM SIGCOMM workshop on
Delay-tolerant networking (WDTN '05). ACM, New York, NY, USA, 252-259.
 Grasic, S., Davies, E., Lindgren, A., & Doria, A. (2011, September). The evolution of a DTN routing
protocol-PRoPHETv2. In Proceedings of the 6th ACM workshop on Challenged networks (pp. 27-30).
ACM.
 Hui, P., Crowcroft, J., & Yoneki, E. (2011). Bubble rap: Social-based forwarding in delay-tolerant
networks. Mobile Computing, IEEE Transactions on, 10(11), 1576-1589.
 Musolesi, M.; Hailes, S.; Mascolo, C., "Adaptive routing for intermittently connected mobile ad hoc
networks," World of Wireless Mobile and Multimedia Networks, 2005. WoWMoM 2005. 13-16 June
2005
 Chen, Guanling, and David Kotz. “A survey of context-aware mobile computing research”. Vol. 1, no.
2.1. Technical Report TR2000-381, Dept. of Computer Science, Dartmouth College, 2000
 Schilit, Bill, Norman Adams, and Roy Want. "Context-aware computing applications." In Mobile
Computing Systems and Applications. WMCSA 1994.
 Bujari, A.; Licar, B.; Palazzi, Claudio E., "Movement pattern recognition through smartphone's
accelerometer," Consumer Communications and Networking Conference (CCNC), 2012 IEEE ,
pp.502,506, 14-17 Jan. 2012
 Jennifer R. Kwapisz; Gary M. Weiss; Samuel A. Moore. 2011. Activity recognition using cell phone
accelerometers. SIGKDD Explor. Newsl. 12, 2 (March 2011)
 Bedogni, L.; Di Felice, M.; Bononi, L., "By train or by car? Detecting the user's motion type through
smartphone sensors data," Wireless Days (WD), 2012 IFIP , 21-23 Nov. 2012

25. Questions?
Go easy on me
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http://goo.gl/7xVDl