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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

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Histogram-Based Density Discovery in Establishing Road Connectivity

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  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

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