近年來因為能源的短缺，所以省能的議題也越來越受到重視，由於車輛可以搭載許多無線的設備，可以讓車輛具備溝通和交換訊息的能力，也讓車用網路越來越重要且越來越多應用。導航系統也是在車用網路下一個非常重要的應用，但是傳統的導航系統只會根據電子地圖做一個最短路徑的規劃，所以當這條路徑有塞車的情況發生，可能會讓使用者花更多的時間跟油耗來到達目的地，所以考量實際路況是非常重要的。在這篇論文中，我們提出了一個以節油為基礎的車用導航系統，主要是透過實際路況下去做路徑的規劃，為了評估未來的路況，我們也會記錄這些資料來當作歷史訊息，以便日後參考；在規劃路徑方面，我們也會根據實際路況做動態的規劃路徑，讓使用者能避開壅塞的道路；在模擬結果可以看出，我們的導航系統可以比最短路徑規劃省下20%的耗油量，且也可以在每一百公里中省下40分鐘的時間，並且不會比最短路徑多走了25%的距離。

Because of shortage of energy, the energy-saving issues become popular in Vehicular Ad-hoc Networks (VANETs). A VANET is formed by traveling vehicles with communicating capability and thus it brings various applications. Navigation system is one of important applications in VANETs. The traditional navigation system usually plans a shortest path for users according to geographic maps but the planned path may become a slower one due to the traffic congestion. A congested path not only delays vehicle traveling time but also wastes fuel. So the real-time traffic information should be considered while constructing a navigation path. In this paper, we propose a navigation system to find a fuel-saving navigation path by considering real-time traffic information. In order to estimate the traffic load on the navigation path, we maintain a table of historical traffic information obtained by Intelligent Transport Systems (ITS). Furthermore, our system can dynamically change the navigation path when traffic information is updated. Our simulation results show that our approach can save more than 20% fuel consumption comparing to shortest-path algorithm when vehicles travel in a traffic congestion environment.

1 Introduction 1
2 RelatedWorks 4
3 FUEL-SAVING NAVIGATION SYSTEM 8
3.1 Single-source to single-destination path planning . . . . . . . . . 9
3.1.1 Phase 1: Initialization . . . . . . . . . . . . . . . . . . . 9
3.1.2 Phase 2: Path Planning . . . . . . . . . . . . . . . . . . . 11
3.1.3 Phase 3: Maintain path . . . . . . . . . . . . . . . . . . . 17
3.2 Group path planning . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Simulation Results 21
4.1 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5 Conclusions 31

[1] “Papago,” http://www.papago.com.tw.
[2] “Mio,” http://www.mio.com.
[3] “Garmic,” http://www.garmin.com.
[4] “Navman,” http://www.navman.com.
[5] S. L. Dhingral and I. Gull, “Traffic flow theory historical research
perspectives,” in Proceedings of Green shields Symposium, july 2008.
[6] A. Skordyli and N. Trigoni, “Delay-bounded routing in vehicular ad-hoc
networks,” in Proceedings of the 9th ACM international symposium on
Mobile ad hoc networking and computing (MobiHoc’08), May 26-30
2008, pp. 341 – 350.
[7] D. K. Ashish Goel, K.G. Ramakrishnan and D. Logothetis, “Efficient
computation of delay-sensitive routes from one source to all destinations,”
in Proceedings of the 20th IEEE International Conference on
Computer Communications (INFOCOM ’01), vol. 2, April 2001, pp.
854 – 858.
[8] M. D. R. Q. Li and D. Rus, “Distributed algorithms for guiding
navigation across a sensor network,” in Proceedings of the 9th annual
international conference on Mobile computing and networking (Mobi-
Com ’03), 2003, pp. 313–325.
[9] M.-S. P. Y.-C. Tseng and Y.-Y. Tsai, “A distributed emergency navigation
algorithm for wireless sensor networks,” IEEE Computers, vol. 39,
no. 7, pp. 55 –62, July. 2006.
[10] D. A. C. Buragohain and S. Suri, “Distributed navigation algorithms
for sensor networks,” in Proceedings of the 25th IEEE International
Conference on Computer Communications (INFOCOM ’06), April 2006,
pp. 1–10.
[11] “The fuel economy guide,” http://www.fueleconomy.gov.
[12] J. H. S. S. D. S. B.H. West, R.N. McGill, “Development and Verification
of Light-Duty Modal Emissions and Fuel Consumption Values for
Traffic Models,” Tech. Rep., April. 1997.
[13] “Institute of transportation, motc,” http://e-iot.iot.gov.tw.
[14] “Google maps,” http://maps.google.com.tw.