Vehicular Ad Hoc Networks and Trajectory-Based Routing

With the rapid advance in short-range radio communications for (e.g., IEEE 802.11p), vehicular ad hoc networks have emerged as a new paradigm of mobile ad hoc networks. Within a vehicle ad hoc network, there are several types of wireless communications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V). A wide variety of existing applications are being developed based on vehicular ad hoc networks, such as traffic safety, transport efficiency, and entertainment on the move. A key enabling technology for vehicular ad hoc network is message routing among vehicles. Efficient message routing is particularly challenging for vehicular ad hoc networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in message routing. By extracting mobility patterns from historical vehicular traces, we develop trajectory predictions by using multiple order Markov chains. We then present routing algorithms taking full advantage of predicted probabilistic vehicular trajectories. We carry out extensive simulations based on large datasets of real GPS vehicular traces. The simulation results demonstrate that the trajectory based routing algorithms can achieve higher delivery ratio at lower cost.

[1]  Alexandre Proutière,et al.  Complexity in wireless scheduling: impact and tradeoffs , 2008, MobiHoc '08.

[2]  Fan Bai,et al.  Toward understanding characteristics of dedicated short range communications (DSRC) from a perspective of vehicular network engineers , 2010, MobiCom.

[3]  Brian Gallagher,et al.  MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[4]  Arun Venkataramani,et al.  DTN routing as a resource allocation problem , 2007, SIGCOMM 2007.

[5]  Injong Rhee,et al.  Max-Contribution: On Optimal Resource Allocation in Delay Tolerant Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[6]  Lionel M. Ni,et al.  SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data , 2009, IEEE INFOCOM 2009.

[7]  Richard E. Hansen,et al.  Prioritized epidemic routing for opportunistic networks , 2007, MobiOpp '07.

[8]  Cecilia Mascolo,et al.  GeOpps: Geographical Opportunistic Routing for Vehicular Networks , 2007, 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[9]  Timur Friedman,et al.  DTN routing in a mobility pattern space , 2005, WDTN '05.

[10]  Hari Balakrishnan,et al.  A measurement study of vehicular internet access using in situ Wi-Fi networks , 2006, MobiCom '06.

[11]  Minglu Li,et al.  Compressive Sensing Approach to Urban Traffic Sensing , 2011, 2011 31st International Conference on Distributed Computing Systems.

[12]  Marco Roccetti,et al.  Efficient vehicle-to-pedestrian exchange of medical data: an empirical model with preliminary results , 2011, MobileHealth '11.

[13]  Rabin K. Patra,et al.  Routing in a delay tolerant network , 2004, SIGCOMM '04.

[14]  H. T. Kung,et al.  Ad hoc relay wireless networks over moving vehicles on highways , 2001, MobiHoc '01.

[15]  Eric Horvitz,et al.  Predestination: Where Do You Want to Go Today? , 2007, Computer.

[16]  Cecilia Mascolo,et al.  Extending Access Point Connectivity through Opportunistic Routing in Vehicular Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[17]  Nahid Shahmehri,et al.  A peer-to-peer approach to vehicular communication for the support of traffic safety applications , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.