1. Place Identification in Location Based Urban VANETs
The Fourth IEEE Annual International Workshop on MissionOriented Wireless Sensor Networking
IEEE MiSeNet 2015
7/29/2018
Place Identification in Location Based Urban VANETs
Heng Li a, Yonghe Liu b, Yi Sun a, Ruiyun Yu c a Hunan University, China b The University of Texas at Arlington, USA
c Northeastern University, China
October 19, 2015, Dallas, Texas, USA

2. Place Identification in LUV Experimental Study
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Outline
Introduction
Place Identification in LUV
Experimental Study
Conclusion and Future Work

3. Background Features of VANETs Challengers of Urban VANETs
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Features of VANETs
1. High mobility of vehicles
2. Random occurrence of vehicles contact opportunities
Challengers of Urban VANETs
1. Reliability
2. Large-scale

4. Related work Current solutions Our solution
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Current solutions
Focused on short periods of transient opportunistic contacts.
Our solution
A framework termed Location based Urban Vehicular network (LUV), utilizing the stable connections among vehicles.

5. Advantages of LUV
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Places provide relatively stable and extended contacts period for vehicles.
Much more feasible than existing designs that focus on transient contacts.
High capacity and relatively stable connections among common places and vehicles.
Place-centered nature of LUV provides a natural approach of scaling to different network sizes.

6. Place Identification in LUV Experimental Study
Outline
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Introduction
Place Identification in LUV
Experimental Study
Conclusion and Future Work

7. Figure 1. Framework for Location Urban VANETs (LUV)
LUV Framework
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In LUV, data exchange only happens in each place
Vehicles belongs to Place where they frequently park daily
How to identify LUV place in Urban area?
Figure 1. Framework for Location Urban VANETs (LUV)

8. Observation for Places in LUV
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Range
be within a certain communication range
Density
satisfy a certain density requirement
Frequency & Residence Time
have enough frequency visiting the place and certain amount of time residing in that place.

9. Place Identification in LUV
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Partition Interested Urban Area based on Predefined Criteria
Obtaining Residing Time of Vehicle in a Unit for a period of time
Calculating the Units a Vehicle Belongs to
Identifying LUV Places

10. Place Identification in LUV Experimental Study
Outline
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Introduction
Place Identification in LUV
Experimental Study
Conclusion and Future Work

11. Real life trace data of Vehicles in an urban environment
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The data source
Vehicle monitoring systems installed on 8900 privately own vehicles in Changsha, China, with a population of 7 million.
The trajectory data
It is sampled at every 10 minutes, totaling 22,513,575 records spanning 31 days from March of 2013.

12. Trace Data Preprocessing
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from March 1 to March 14, 2013
from N28.07 to N28.27,
E to E113.10;
filtering out duplicate records
3,591,533 records as the final data
Figure 2. Targeted LUV area in Changsha, China

13. LUV Place Identification Results
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Figure 3(a). LUV Place Distribution with Thr_density=0
Figure 3(b). LUV Place Distribution with Thr_density=19
We effectively identify 873 LUV places(each with an area of 100m by 100m. ) over a 20km by 20km area.

14. LUV Place Identification Results
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Figure 4. Location of Region A and B

15. Figure 5. LUV Place Distribution in Region A
Zoom in Region A
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Figure 5. LUV Place Distribution in Region A

16. Figure 6. LUV Place Distribution in Region B
Zoom in Region B
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Figure 6. LUV Place Distribution in Region B

17. Place Identification in LUV Experimental Study
Outline
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Introduction
Place Identification in LUV
Experimental Study
Conclusion and Future Work

18. Conclusion and Future Work
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Done
Describe a simple place identification approach for location based urban vehicular network.
Doing
Analyzing the relationship among the number of LUV places, the size of LUV unit, and the number of LUV nodes.
To Do
Search for optimal tradeoffs for different network performance

19. 7/29/2018
Thanks!
October 19,2015