Vehicular sensors capture a large amount of data, and a routing algorithm is needed to effectively propagate the data in vehicular sensor network (VSN). The existing routing algorithms in vehicular ad hoc network (VANET) can not be transplanted to VSN directly. After analyzing the mobility of vehicles, we find that the delivery time of vehicle to vehicle (V2V) or vehicle to infrastructure (V2I) is very short especially when a deliverer is at a corner or crossroad and tries to deliver data to its left/right direction. Using existing routing algorithms in VANET will cause the big amount of data to stuck at corners or crossroads and to be transmitted back and forth with very few data packets being delivered. It is very time consuming and computation consuming. In this paper, we propose a data delivery algorithm called distribution-based data delivery to handle the above-mentioned corner problem with the help of some vehicular sensors like accelerometer. Evaluations show that the time cost of data delivery at corners is saved for at least 30% by our algorithm comparing to other routing algorithms like VADD in VANET. 1. Introduction VANET is a hot topic in recent years; it is proposed to enhance driving safety by propagating traffic information among driving vehicles. Although great progress has been made in data routing, VANET still cannot find enough convincing applications. Modern vehicles are equipped with various sensors, the sensors are used to monitor the states of vehicle and surroundings. The information vehicular sensors collected would be very useful for some crowdsourcing applications. For example, fuel tank sensor gives fuel level value; if the fuel level increases at a location, then the vehicle probably is refueling and the location is a fuel station; a vehicle can request the locations of fuel station from its neighbors so that it knows where to refuel without the help of online GIS system. Vehicular sensors enrich the information VANET could propagate; therefore, the combination of wireless sensors and VANET to construct vehicular sensor network (VSN) or vehicular ad hoc and sensor network (VASNET) [1] is a good complement of VANET. There are many good routing algorithms in VANET can be transplanted to VSN, like GPSR [2] and VADD [3], because VSN has the same network architecture as VANET. However, because vehicular sensors will collect a large amount of data, VSN needs a routing algorithm which can handle this amount of data. In most cases, routing algorithm works at network layer, it simply forwards the packet passed down from upper layers
References
[1]
M. J. Piran, G. R. Murthy, and G. P. Babu, “Vehicular ad hoc and sensor networks; principles and challenges,” International Journal of Ad hoc, Sensor and Ubiquitous Computing. In press.
[2]
B. Karp and H. T. Kung, “GPSR: Greedy Perimeter Stateless Routing for wireless networks,” in Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MOBICOM '00), pp. 243–254, August 2000.
[3]
J. Zhao and G. Cao, “VADD: Vehicle-assisted data delivery in vehicular ad hoc networks,” in Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM '06), pp. 1–12, April 2006.
[4]
C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA '99), pp. 90–100, February 1999.
[5]
G. Pei, M. Gerla, and T. W. Chen, “Fisheye State Routing: a routing scheme for ad hoc wireless networks,” in Proceedings of the IEEE International Conference on Communications (ICC '00), pp. 70–74, June 2000.
[6]
D. B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing, T. Imielinski and H. F. Korth, Eds., The Kluwer International Series in Engineering and Computer Science, Springer, 1996.
[7]
C. Lochert, M. Mauve, F. Holger, and H. Hartenstein, “Geographic routing in city scenarios,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 9, no. 1, pp. 69–72, 2005.
[8]
V. Naumov and T. R. Gross, “Connectivity-aware routing (CAR) in vehicular ad hoc networks,” in Proceedings of the 26th IEEE International Conference on Computer Communications (INFOCOM '07), pp. 1919–1927, May 2007.
[9]
E. Murphy-Chutorian, A. Doshi, and M. M. Trivedi, “Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation,” in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC '07), pp. 709–714, October 2007.
[10]
G. A. T. Holt, M. J. Reinders, and E. A. Hendriks, “Multidimensional dynamic time warping for gesture recognition,” in Proceedings of the 13th Annual Conference of the Advanced School for Computing and Imaging, 2007.
[11]
R. Muscillo, S. Conforto, M. Schmid, P. Caselli, and T. D'Alessio, “Classification of motor activities through derivative dynamic time warping applied on accelerometer data,” Proceedings of the 29th Annual International Conference of the IEEE, vol. 2007, pp. 4930–4933, 2007.
[12]
D. A. Johnson and M. M. Trivedi, “Driving style recognition using a smartphone as a sensor platform,” in Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA, 2011.
[13]
S. Y. Cheng and M. M. Trivedi, “Turn-intent analysis using body pose for intelligent driver assistance,” IEEE Pervasive Computing, vol. 5, no. 4, pp. 28–37, 2006.
[14]
J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan, “Mobile phone based drunk driving detection,” in Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health), March 2010.
[15]
U. Lee, B. Zhou, M. Gerla, E. Magistretti, P. Bellavista, and A. Corradi, “Mobeyes: Smart mobs for urban monitoring with a vehicular sensor network,” IEEE Wireless Communications, vol. 13, no. 5, pp. 52–57, 2006.
[16]
M. J. Piran and G. R. Murthy, “A novel routing algorithm for vehicular sensor networks,” Internation Journal of Wireless Sensor Networks, vol. 2, no. 12, pp. 919–923, 2010.
