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Trajectory Improves Data Delivery in Urban Vehicular Networks
April 2014 (vol. 25 no. 4)
pp. 1089-1100
Bo Li, Dept. of Comput. Sci. & Eng., Hong Kong Univ. of Sci. & Technol. (HKUST), Kowloon, China
Yuchen Wu, Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China
Yanmin Zhu, Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China
Efficient data delivery is of great importance, but highly challenging for vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in data delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor data delivery performance. In this paper, we mine the extensive data sets of vehicular traces from two large cities in China, i.e., Shanghai and Shenzhen, through conditional entropy analysis, we find that there exists strong spatiotemporal regularity with vehicle mobility. By extracting mobility patterns from historical vehicular traces, we develop accurate trajectory predictions by using multiple order Markov chains. Based on an analytical model, we theoretically derive packet delivery probability with predicted trajectories. We then propose routing algorithms taking full advantage of predicted probabilistic vehicular trajectories. Finally, we carry out extensive simulations based on three large data sets of real GPS vehicular traces, i.e., Shanghai taxi data set, Shanghai bus data set and Shenzhen taxi data set. The conclusive results demonstrate that our proposed routing algorithms can achieve significantly higher delivery ratio at lower cost when compared with existing algorithms.
Index Terms:
Vehicles,Trajectory,Entropy,Routing,Prediction algorithms,Silicon,Spatiotemporal phenomena,Markov chain,Vehicular networks,probabilistic trajectory,routing,prediction
Citation:
Bo Li, Yuchen Wu, Yanmin Zhu, "Trajectory Improves Data Delivery in Urban Vehicular Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 4, pp. 1089-1100, April 2014, doi:10.1109/TPDS.2013.118
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