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Trajectory Improves Data Delivery in Urban Vehicular
Networks
Abstract:
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

2. 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.
Hardware Requirement:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Ram : 256 Mb.
Software Requirement:
Operating system : Windows XP Professional.

3. Coding Language : Java
Tool Used : Netbeans 7.0.1