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

Place Identification in LUV Experimental Study 7/29/2018 Outline Introduction Place Identification in LUV Experimental Study Conclusion and Future Work

Background Features of VANETs Challengers of Urban VANETs 7/29/2018 Features of VANETs 1. High mobility of vehicles 2. Random occurrence of vehicles contact opportunities Challengers of Urban VANETs 1. Reliability 2. Large-scale

Related work Current solutions Our solution 7/29/2018 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.

Advantages of LUV 7/29/2018 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.

Place Identification in LUV Experimental Study Outline 7/29/2018 Introduction Place Identification in LUV Experimental Study Conclusion and Future Work

Figure 1. Framework for Location Urban VANETs (LUV) LUV Framework 7/29/2018 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)

Observation for Places in LUV 7/29/2018 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.

Place Identification in LUV 7/29/2018 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

Place Identification in LUV Experimental Study Outline 7/29/2018 Introduction Place Identification in LUV Experimental Study Conclusion and Future Work

Real life trace data of Vehicles in an urban environment 7/29/2018 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.

Trace Data Preprocessing 7/29/2018 from March 1 to March 14, 2013 from N28.07 to N28.27, E112.90 to E113.10; filtering out duplicate records 3,591,533 records as the final data Figure 2. Targeted LUV area in Changsha, China

LUV Place Identification Results 7/29/2018 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.

LUV Place Identification Results 7/29/2018 Figure 4. Location of Region A and B

Figure 5. LUV Place Distribution in Region A Zoom in Region A 7/29/2018 Figure 5. LUV Place Distribution in Region A

Figure 6. LUV Place Distribution in Region B Zoom in Region B 7/29/2018 Figure 6. LUV Place Distribution in Region B

Place Identification in LUV Experimental Study Outline 7/29/2018 Introduction Place Identification in LUV Experimental Study Conclusion and Future Work

Conclusion and Future Work 7/29/2018 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

7/29/2018 Thanks! October 19,2015