1 / 15

Histogram-Based Density Discovery in Establishing Road Connectivity

Histogram-Based Density Discovery in Establishing Road Connectivity. Kevin Lee, Jiajie Zhu, Jih Chung Fan, Mario Gerla University of California, Los Angeles VNC, 10/28/09. Why Do We Need Density?. Using density information to avoid traps in VANET

gilles
Download Presentation

Histogram-Based Density Discovery in Establishing Road Connectivity

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Histogram-Based Density Discovery in Establishing Road Connectivity Kevin Lee, Jiajie Zhu, Jih Chung Fan, Mario Gerla University of California, Los AngelesVNC, 10/28/09

  2. Why Do We Need Density? • Using density information to avoid traps in VANET • What is the relationship between density and connectivity? D ? What is a good density algorithm that allows us to establish accurate connectivity of the road? S Nodes bunched up at the intersection => Can’t assume uniform density Credit: M. Fiore and J. H¨arri, ACM MobiHoc 2008

  3. Histogram-Based Density Discovery Algorithm • Break up the roads into segments • Nodes within a segment keep track of density in that segment in P2P fashion • Nodes keep a histogram of density Ni for other segments by broadcast • Road is connected if Segment center

  4. Advantages of Histogram-Based Approach • Scalable • E.g. 1500-meter road, 250-meter segment length • The number of segments is 6 (1500/250) • P2P can only store 6 cars, not enough • More accurate • Each segment size is smaller than the road length • Connectivity correlates better with segment density than road density NOT CONNECTED

  5. Why Optimal Segment Size? • No fluctuation of density due to influx of cars • A histogram of segment densities correlates well with road connectivity • Note that Optimal Segment Size is NOT necessarily the radio range

  6. Density Accuracy • Intuition: Given a car’s Spd and convergence time Conv, it should stay within the segment (thus not change the density of a segment) • SegSizeopt >= Conv * Spd

  7. Convergence Time • Convergence time does NOT vary with segment size • Convergence time varies with either traffic or road length RL 900m RL 1000m

  8. Minimum Segment Size • Extrapolate the relationship between road length and convergence, density and convergence • Use [SegSizeopt >= Conv * Spd] to obtain relationship between SegSizeopt, road length, and density

  9. Connectivity Accuracy • Places upperbound on the optimal segment size • Definition: the number of runs that are identified correctly/total number of runs • 1,000 runs • 300 runs are connected, 270 are identified correctly • 700 runs are not, 650 are identified correctly • 92% accurate ((270+650)/1000) • 30 false negatives • 50 false positives

  10. Segment Size vs. Connectivity Accuracy • Connectivity accuracy drops when segment size increases • Periodic rise and drop due to last segment not evenly divisible by segment size RL 900m RL 1000m SegSizemax = 325m SegSizemax = 375m

  11. Optimal Segment Size • For each road length and density, find SegSizeminand SegSizemax • Average is SegSizeopt

  12. Evaluation • Connectivity accuracy between P2P and histogram-based approach • Road Percentage Connectivity (RPC) vs. Connectivity Accuracy (CA) • If road is connected, CA = RPC • If road is not, CA = 1 – RPC • Broadcast overhead between P2P and histogram-based approach • 1,000 traces from VanetMobiSim

  13. Connectivity Accuracy between P2P and Histogram • P2P underperforms when density is low • This is due to sparse density that models cluster behavior

  14. Broadcast Overhead between P2P and Histogram • Broadcast/node/sec • P2P has scalability issue as it needs to keep track of unique cars

  15. Conclusion • Systematic way to obtain optimal SegSize • Evaluation shows histogram-based scheme’s scalability

More Related