Wireless Personal Communications

, Volume 96, Issue 4, pp 5203–5219 | Cite as

Node Re-Routing and Congestion Reduction Scheme for Wireless Vehicular Networks

  • Peppino Fazio
  • Mauro Tropea
  • Salvatore Marano


Recently, the interest of research is going to be focused on the emerging vehicular ad-hoc networks paradigm. In these networks, vehicles communicate with each other and have the possibility of exploiting a distributed approach, typical of ad-hoc networks, which allow mobile nodes (vehicles) to communicate with each other. Thanks to the different standards for this kind of network, such as DSRC, WAVE/IEEE802.11p, the researchers have the possibility of designing and developing new MAC and routing algorithms, trying to enhance the mobile users experience in the mobile environment. In this paper, the attention is focused on the optimization of traffic flowing in a vehicular environment with vehicle-2-roadside capability. The proposed idea exploits the information that is gathered by road-side units with the main aim of redirecting traffic flows (in terms of vehicles) to less congested roads, with an overall system optimization, also in terms of Carbon Dioxide emissions reduction. A deep campaign of simulations has been carried out to give more effectiveness to our proposal.


802.11p Congestion DSRC Traffic flow VANET WAVE 


  1. 1.
    Da Cunha, F. D., Boukerche, A., Villas, L., Viana, A. C., & Loureiro, A. A. F. (2014). Data communication in VANETs: A survey, challenges and applications. [Research Report] RR-8498, INRIA Saclay.Google Scholar
  2. 2.
    Vegni, A. M., Vegni, C., & Little, T. D. C. (2010). Opportunistic vehicular networks by satellite links for safety applications. In Proceedings of the the fully networked car workshop, Geneva international motor show, Geneva, Switzerland, March 34.Google Scholar
  3. 3.
    Giang, A. T., Busson, A., & Veque, V. (2013). Message dissemination in VANET: Protocols and performances. In Wireless vehicular networks for car collision avoidance (pp. 71–96).Google Scholar
  4. 4.
    Socievole, A., Yoneki, E., De Rango, F., & Crowcroft, J. (2013). Opportunistic message routing using multi-layer social networks. In Proceedings of the 2nd ACM workshop on high performance mobile opportunistic systems (pp. 39–46). ACM.Google Scholar
  5. 5.
    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. Advances in Electrical and Electronic Engineering, 11(5), 357–364.Google Scholar
  6. 6.
    Fazio, P., De Rango, F., & Lupia, A. (2013). A new application for enhancing VANET services in emergency situations using the WAVE/802.11 p standard. In Wireless Days (WD), IFIP (pp. 1–3).Google Scholar
  7. 7.
    Fazio, P., De Rango, F., Sottile, C., Manzoni, P., & Calafate, C. (2011). A distance vector routing protocol for VANET environment with dynamic frequency assignment. In Wireless communications and networking conference (WCNC) (pp. 1016–1020). IEEE.Google Scholar
  8. 8.
    Cassano, E., Florio, F., De Rango, F., & Marano, S. (2009). A performance comparison between ROC-RSSI and trilateration localization techniques for WPAN sensor networks in a real outdoor testbed. In Wireless Telecommunications Symposium (WTS 2009). Prague, Czech Republic.Google Scholar
  9. 9.
    Toulni, H., Nsiri, B., Boulmalf, M., Bakhouya, M., & Sadiki, T. (2014). An approach to avoid traffic congestion using VANET. In Fifth international conference on next generation networks and services (NGNS) (pp. 154–159). IEEE. (2014)Google Scholar
  10. 10.
    Pan, J., Khan, M. A., Popa, I. S., Zeitouni, K., & Borcea, C. (2012). Proactive vehicle rerouting strategies for congestion avoidance. In 8th International conference on distributed computing in sensor systems (DCOSS), 2012 (Vol. 16, No. 18, pp. 265–272). IEEE.Google Scholar
  11. 11.
    De Rango, F., Gerla, M., & Marano, S. (2006). A scalable routing scheme with group motion support in large and dense wireless ad hoc networks. Computers and Electrical Engineering, 32(1), 224–240.CrossRefzbMATHGoogle Scholar
  12. 12.
    Lee, W., Lai, Y., & Chen, P. (2015). A study on energy saving and emission reduction on signal countdown extension by vehicular ad hoc networks. In IEEE transactions on vehicular technology (Vol.64, No. 3, pp. 890–900).Google Scholar
  13. 13.
    De Rango, F., & Fotino, M. (2009). Energy efficient OLSR performance evaluation under energy aware metrics. In Proceedings of the 2009 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2009 (pp. 193–198).Google Scholar
  14. 14.
    De Rango, F., Leonetti, P., & Marano, S. (2008). MEA-DSR: A multipath energy-aware routing protocol for wireless ad hoc networks. In IFIP International Federation for Information Processing (Vol. 265, pp. 215–225).Google Scholar
  15. 15.
    De Rango, F., Guerriero, F., Marano, S., & Bruno, E. (2006). A multiobjective approach for energy consumption and link stability issues in ad hoc networks. IEEE Communications Letters, 10(1), 28–30.Google Scholar
  16. 16.
    Kurmis, M., Dzemydiene, D., Andziuli, A., Voznak, M., Jakovlev, S., Lukosius, Z., et al. (2014). Prediction based context data dissemination and storage model for cooperative vehicular networks. Advances in Intelligent Systems and Computing, 289, 21–30.CrossRefzbMATHGoogle Scholar
  17. 17.
    De Rango, F., Fazio, P., & Marano, S. (2006). Cell stay time analysis under random way point mobility model in WLAN. IEEE Communications Letters, 10(11), 763–765.CrossRefGoogle Scholar
  18. 18.
    Zhu, L., Yu, F. R., Ning, B., & Tang, T. (2013). A joint design of security and quality-of-service (QoS) provisioning in vehicular ad hoc networks with cooperative communications. EURASIP Journal on Wireless Communications and Networking, 2013, 88.CrossRefGoogle Scholar
  19. 19.
    Zhou, B., Lee, Y. Z., Gerla, M., & De Rango, F. (2006). GeoLANMAR: A scalable routing protocol for ad hoc networks with group motion. Wireless Communications and Mobile Computing, 6(7), 989–1002.CrossRefGoogle Scholar
  20. 20.
    Sanguesa, J. A., Barrachina, J., Fogue, M., Garrido, P., Garrido, P., Martinez, F. J., et al. (2015). Sensing traffic density combining V2V and V2I wireless communications. Sensors, 15(12), 31794–31810.CrossRefGoogle Scholar
  21. 21.
    Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2013). Reducing emergency services arrival time by using vehicular communications and evolution strategies. Expert Systems With Applications, 41(4), 1206–1217.Google Scholar
  22. 22.
    Task Group p. (2006). IEEE P802.11p: Wireless access in vehicular environments (WAVE), draft standard ed. IEEE Computer Society.Google Scholar
  23. 23.
    Fazio, P., De Rango, F., & Selvaggi, I. (2010). A novel passive bandwidth reservation algorithm based on Neural Networks path prediction in wireless environments. Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 11–14, 38–43.Google Scholar
  24. 24.
    De Rango, F., Veltri, F., & Marano, S. (2011). Channel modeling approach based on the concept of degradation level discrete-time Markov chain: UWB system case study. IEEE Transactions on Wireless Communications, 10(4), 1098–1107.CrossRefGoogle Scholar
  25. 25.
    Kurmis, M., Andziulis, A., Dzemydiene, D., Jakovlev, S., Voznak, M., & Drungilas, D. (2013). Development of the real time situation identification model for adaptive service support in vehicular communication networks domain. Advances in Electrical and Electronic Engineering, 11(5), 342–348.CrossRefGoogle Scholar
  26. 26.
    Festa, D. C., & Astarita, V. (2012). Rilievi. Collana Trasporti: Modellizzazione e controllo del traffico veicolare.Google Scholar
  27. 27.
    Van Woensel, T., & Vandaele, N. (2007). Modelling traffic ows with queueing models: A review AsiaPacific. Journal of Operational Research, 24(4), 127.Google Scholar
  28. 28.
    Highway Capacity Manual. (2000). Transportation research board. Washington D.C: The National Academies.Google Scholar
  29. 29.
    Mohammad, S. A.., Rasheed, A., & Qayyum, A. (2011). VANET architectures and protocol stacks: A survey. In Communication Technologies for Vehicles Lecture notes in computer science (Vol. 6596, pp. 95–105).Google Scholar
  30. 30.
    Santamaria, A. F., & Sottile, C. (2014). Smart traffic management protocol based on VANET architecture. Advances in Electrical and Electronic Engineering, 12(4), 279–288.CrossRefGoogle Scholar
  31. 31.
    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 workshops.Google Scholar
  32. 32.
    Sommer, C., German, R., & Dressler, F. (2011). Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Transactions on Mobile Computing, 10(1), 3–15.CrossRefGoogle Scholar
  33. 33.
    Behrisch, M., Bieker, L., Erdman, J., & Krajzewicz, D. (2011). SUMO simulation of urban mobility: An overview. In The third international conference on advances in system simulation, SIMUL.Google Scholar
  34. 34.
  35. 35.
  36. 36.
    Cappiello, A., Chabini, I., Nam, E., Lue, E., & Zeid M. A. (2002). A statistical model of vehicle emissions and fuel consumption. In 5th IEEE international conference on intelligent transportation systems (IEEE ITSC) (pp. 801–809).Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.DIMES UNICALRendeItaly

Personalised recommendations