Mobile Networks and Applications

, Volume 20, Issue 2, pp 220–238 | Cite as

Safety Enhancement and Carbon Dioxide (C O 2) reduction in VANETs

  • Amilcare Francesco Santamaria
  • Cesare Sottile
  • Floriano De Rango
  • Salvatore Marano


Nowadays one of the hottest theme is the application of the newest technologies in road safety. Several proposals have been made and both US and European standardization institutes are working on them. In this work we present a novel cooperative architecture that allows vehicles to communicate between them exploiting Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) connections. In order to spread information we propose a network protocol called Safety Enhancement for WAVE based protocol (SeAWave) that takes advantages of IEEE802.11p standard and tries to enhance it adding useful messages increasing vehicles’ passive and active safety systems. In this work we propose a novel protocol in order to gather important data about environment such as collisions, block, emission levels and so on. These data are collected by the City Traffic Manager (CTM) exploiting dedicated messages sent by the vehicle and infrastructure devices. They are used by the system to activate alerting mechanism using protocol messages in a controlled broadcasting. In addiction, CTM knowing the whole status of the road network can avoid traffic blocks making some high level decisions. Also a smart traffic management system is addressed in the proposed framework in order to reduce vehicles’ C O 2 emissions in the urban area increasing, where possible, air quality. In order to validate proposed framework and protocol we use a well know Discrete-Event Simulator (DES) simulator with a dynamic mobility generator that allow us to change and control reference areas, area size, and loads rate.


Road safety VANET IEEE 802.11p WSMP Traffic management Data dissemination 


  1. 1.
    Taherkhani N, Pierre S (2012) Congestion control in vehicular ad hoc networks using meta-heuristic techniques. In: Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications, pp 47–54Google Scholar
  2. 2.
    Barba CT, Mateos MA, Soto PR, Mezher AM, Igartua MA (2012) Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights. Intelligent Vehicles SymposiumGoogle Scholar
  3. 3.
    Leontiadis I, Marfia G, Mack D, Pau G, Mascolo C, Gerla M (2011) On the effectiveness of an opportunistic traffic management system for vehicular networks. IEEE Trans Intell Transp Syst 12Google Scholar
  4. 4.
    Younes MB, Boukerche A (2013) Efficient traffic congestion detection protocol for next generation VANETs, IEEE International Conference on Communications (ICC)Google Scholar
  5. 5.
    Katsaros K, Kernchen R, Dianati M, Rieck D, Zinoviou C (2011) Application of vehicular communications for improving the efficiency of traffic in urban areas. International Conference on Wireless Communications and Mobile ComputingGoogle Scholar
  6. 6.
    Chisalita I, Shahmehri N (2004) A context-based vehicular communication protocol. In: 2004 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2004, vol 4, pp 2820–2824Google Scholar
  7. 7.
    Dobre C (September 2012) Using Intelligent Traffic Lights to Reduce Vehicle Emissions. Int J Innov Comput Inf Control 8(9)Google Scholar
  8. 8.
    Lee W, Lai Y, Chen P (2014) A study on energy saving and co2 emissions reduction on signal countdown extension by vehicular ad-hoc network. IEEE Trans Veh Technol PPGoogle Scholar
  9. 9.
    Dornbush S, Joshi A (2007) StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANET’s, IEEE 65th Vehicular Technology ConferenceGoogle Scholar
  10. 10.
    Lee WH, Chen PY (2012) Decision-tree based green driving suggestion system for carbon emission reduction, 12th International Conference on ITS Telecommunications (ITST)Google Scholar
  11. 11.
    Dobre C, Szekeres A, Pop F, Cristea V, Xhafa F (2012) Intelligent traffic lights to reduce vehicle emissions. In: Proceedings 26th European Conference on Modelling and SimulationGoogle Scholar
  12. 12.
    Li C, Chen W, He D, Hu X, Shimamoto S (2013) A Travel-Efficient Driving Assistance Scheme in VANETs by Providing Recommended Speed. IEICE Trans Fundam Electron Commun Comput Sci E96-A(10):2007–2015CrossRefGoogle Scholar
  13. 13.
    Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of sumo - simulation of urban mobility, International Journal On Advances in Systems and MeasurementsGoogle Scholar
  14. 14.
    Fazio P, Tropea M (2012) A New Markovian Prediction Scheme for Resource Reservation in Wireless Networks With Mobile Hosts. Adv Electr Electron Eng Ser, Elsevier AEEE journal 10(4)Google Scholar
  15. 15.
    Martinez FJ, Toh CK, Cano JC, Calafate CT, Manzoni P (2012) Determining the Representative Factors Affecting Warning Message Dissemination in VANETs. Wirel Pers Commun 67(2):295–314CrossRefGoogle Scholar
  16. 16.
    Orozco O, Llano G (2014) OSA: A Vanet application focused on fuel saving and reduction of CO2 emissions. Revista S and T 12:25–47Google Scholar
  17. 17.
    Fazio P, De Rango F, Sottile C, Santamaria AF (2013) Routing Optimization in Vehicular Networks: A New Approach Based on Multi-objective Metricsand Minimum Spanning Tree, International Journal of Distributed Sensor NetworksGoogle Scholar
  18. 18.
    Ahmed SAM, Ariffin SHS, Fisal N (2013) Overview of Wireless Access in Vehicular Environment (WAVE) Protocols and Standards. Indian J Sci Technol 6(7)Google Scholar
  19. 19.
    Fazio P, De Rango F, Sottile C (2011) A new interference aware on demand routing protocol for vehicular networks, International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS, The Hague, NetherlandsGoogle Scholar
  20. 20.
    Cappiello A, Chabini I, Nam EK, Lue A, Abou Zeid M (2002) A statistical model of vehicle emissions and fuel consumption, IEEE 5th International Conference on Intelligent Transportation Systems (IEEE ITSC), pp 801–809Google Scholar
  21. 21.
    Stolfi DH, Alba E (2014) Eco-friendly reduction of travel times in european smart cities. In: Proceedings of the 2014 conference on Genetic and evolutionary computation GECCO’14, pp 1207–1214Google Scholar
  22. 22.
    Mathew TV (2014) Transportation Systems Engineering, Chapter 13, National Programme on Technology Enhanced Learning (NPTEL)Google Scholar
  23. 23.
    Varga A, Hornig R (2008) An overview of the OMNeT ++ simulation environment. In: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems workshopsGoogle Scholar
  24. 24.
    Sommer C, German R, Dressler F (2011) Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Trans Mob Comput 10(1):3–15CrossRefGoogle Scholar
  25. 25.
    Fazio P, De Rango F, Lupia A (2013) Vehicular Networks and Road Safety: an Application for Emergency/Danger Situations Management Using the WAVE/802.11 p Standard. Adv Electr Electron Eng 11(5):357–364Google Scholar
  26. 26.
    Li Y (2012) An Overview of the DSRC/WAVE Technology. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 74:544–558CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Amilcare Francesco Santamaria
    • 1
  • Cesare Sottile
    • 1
  • Floriano De Rango
    • 1
  • Salvatore Marano
    • 1
  1. 1.DIMES DepartmentUniversity of CalabriaRendeItaly

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