TRAJECTORY IMPROVES DATA  DELIVERY IN URBAN VEHICULAR NETWORKS(2014)

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 datasets 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 datasets of real GPS vehicular traces, i.e., Shanghai taxi dataset, Shanghai bus dataset and Shenzhen taxi dataset. The conclusive results demonstrate that our proposed routing algorithms can achieve significantly higher delivery ratio at lower cost when compared with existing algorithms.

EXISTING SYSTEM:

Efficient inter-vehicle data delivery is of central importance to vehicular networks and such importance has been recognized by many existing studies. In this paper we focus on such vehicular networks that are sparse and do no assume that all vehicles on the road are member nodes of the vehicular network. Such sparse vehicular networks feature infrequent communication opportunities. Inter-vehicle data delivery may introduce nonneligible delivery latency because of frequent topology disconnection of a vehicular network. Thus, we should stress that the inter-vehicle communication in vehicular network are suitable for those applications which can tolerate certain delivery latency. For example, in the context of urban sensing, vehicles continuously collect useful information, such as road traffic conditions and road closures. A vehicle may send a query for a specific kind of information and the one that has the information should respond the querying node with the data. Such communication requires multi-hop data delivery in vehicular networks. Other examples of such applications include peer-to-peer file sharing, entertainment, advertisement, and file downloading.

DISADVANTAGES OF EXISTING SYSTEM:

v It has adopted only simple mobility patterns, such as the spatial distribution and inter- meeting time distribution, which support coarse-grained predictions of vehicle movements.

v It ignores the fact that links in a vehicular network have unique characteristics

PROPOSED SYSTEM:

To overcome the limitations of existing algorithms, this paper proposes an approach to exploiting the hidden mobility regularity of vehicles to predict future trajectories. By mining the extensive dataset of vehicular traces from more than 4,000 taxis in Shanghai, China, we show that there is strong spatiotemporal regularity with vehicle mobility. More specifically, our results based on conditional entropy analysis demonstrate that the future trajectory of a vehicle is greatly correlated with its previous trajectory. Thus, we develop multiple order Markov chains for predicting future trajectories of vehicles. With the available future trajectories of vehicles, we propose an analytical model and theoretically derive the delivery probability of a packet.

ADVANTAGES OF PROPOSED SYSTEM:

v It develop an efficient global algorithm for computing routing paths when predicted trajectories are available.

v It considerably outperforms other algorithms in terms of delivery probability and delivery efficiency.

SYSTEM SPECIFICATION

Hardware Requirements:

•         System                        :   Pentium IV 3.5 GHz.

•         Hard Disk                   :   40 GB.

•         Floppy Drive              :   1.44 Mb.

•         Monitor                       :   14’ Colour Monitor.

•         Mouse                        :   Optical Mouse.

•         Ram                            :   1 GB.

Software Requirements:

•         Operating system         :   Windows XP or Windows 7, Windows 8.

•         Coding Language        :   Java – AWT,Swings,Networking

•         Data Base                      :   My Sql / MS Access.

•         Documentation             :  MS Office

•         IDE                                  : Eclipse Galileo

•         Development Kit           : JDK 1.6