Skip to main content
Log in

A Framework For Handling Local Broadcast Storm Using Probabilistic Data Aggregation In VANET

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Data aggregation is used to combine correlated data items from different vehicles before redistributing to other vehicles in the vehicular ad hoc networks (VANET). The number of retransmissions and the communication overhead can be reduced considerably by using aggregation. It is a prerequisite for applications that require periodic dissemination of information into a large region so that, drivers can be informed well in advance and can take alternative route in case of traffic congestion. Dissemination of information to vehicles through broadcasting creates a broadcast storm problem in VANET. In this paper a novel framework is proposed for handling the local broadcast storm problem using probabilistic data aggregation which reduces the bandwidth consumption and hence improves the information dissemination. This system exploits the knowledge base and stores the decisions for aggregation and is based on a flexible and extensible set of criteria. These criteria’s can be application specific and can enable a dynamic fragmentation of the road according to the various application requirements. The framework is evaluated for VANET based traffic information system through simulation for strictly limited bandwidth and local broadcast problem. The results demonstrate that completely structure-free probabilistic data aggregation reduces the bandwidth consumption by eliminating the local broadcast problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Schoch, E., Kargl, F., Weber, M., & Leinmuller, T. (2008). Communication patterns in VANETS. IEEE Communications Magazine, 46(11), 119–125.

    Article  Google Scholar 

  2. Fubler, H., Mauve, M., Hartenstein, H., Kasemann, M., & Vollmer, D. (2003). Location-based routing for vehicular ad-hoc networks. ACM SIGMOBILE Mobile Computing and Communications, Review (MC2R), 7(1), 47–49.

    Article  Google Scholar 

  3. US Department of Transportation. (2003). Standard specification for telecommunications and information exchange between roadside and vehicle systems. ASTM E2213–03.

  4. Status of Project IEEE 802.11p. (2006). http://grouper.ieee.org/groups/802/11/Reports/tgp_update.htm.

  5. Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 70–87.

    Article  Google Scholar 

  6. Ren, S. Q., & Park, J. S. (2009). Fault-tolerance data aggregation for clustering wireless sensor network. Wireless Personal Communications, 51, 179–192. doi:10.1007/s11277-008-9598-7.

    Article  Google Scholar 

  7. Martinez, F. J., Toh, C. K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2012). Determining the representative factors affecting warning message dissemination in VANETs. Wireless Personal Communications, 67(2), 295–314.

    Article  Google Scholar 

  8. Al-Karaki, J. N., Ul-Mustafa, R., & Kamal, A. E. (2009). Data aggregation and routing in wireless sensor networks: Optimal and heuristic algorithms. Computer Networks, 53, 945–960.

    Article  MATH  Google Scholar 

  9. Yousefi, H., Yeganeh, M. H., & Alinahipour, N. (2012). Structure-free real-time data aggregation in wireless sensor networks. Computer Communications, 35(9), 1132–1140.

    Article  Google Scholar 

  10. Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). Tag: a Tiny AGgregation service for ad-hoc sensor networks. In Proceedings of the 5th symposium on operating systems design and implementation (Vol. 36, no. (SI), pp. 131–146) USA: ACM SIGOPS.

  11. Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 11(1), 2–16.

    Article  Google Scholar 

  12. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-Efficient Gathering in Sensor Information Systems. In Proceedings of IEEE aerospace Vol. 3, pp. 1125–1130.

  13. Yao, Y., & Gehrke, J. (2002). The cougar approach to in-network query processing in sensor networks. SIGMOD Record, 31(3), 9–18.

    Article  Google Scholar 

  14. Fan, K. W., Liu, S., & Sinha, P. (2006) On the potential of structure-free data aggregation in sensor networks. In Proceedings of IEEE INFOCOM pp. 1–12.

  15. Fan, K. W., Liu, S., & Sinha, P. (2007). Structure-free data aggregation in sensor networks. IEEE Transactions on Mobile Computing, 6(8), 929–942.

    Article  Google Scholar 

  16. Dietzel, S., Bako, B., Schoch, E., & Kargl, F. (2009). A fuzzy logic based approach for structure-free aggregation in vehicular ad-hoc networks. Proceedings of 6th ACM international workshop on VehiculArInterNETworking (pp. 79–88). USA: ACM Press.

  17. Kosch, T., Adler, C. J., Eichler, S., Schroth, C., & Strassberger, M. (2006). The scalability problem of vehicular ad hoc networks and how to solve it. IEEE Wireless Communications, 13(5), 22–28.

    Article  Google Scholar 

  18. Wischhof, L., Ebner, A., Rohling, H., Lott, M., & Halfmann, R. (2003). SOTIS-A self-organizing traffic information system. In Proceedings of 57th IEEE vehicular technology conference pp. 2442–2446.

  19. Nadeem, T., Dashtinezhad, S., Liao, C., & Iftode, L. (2004). TrafficView: Traffic data dissemination using car-to-car communication. ACM SIGMOBILE Mobile Computing and Communication Review, 8(3), 6–19.

    Article  Google Scholar 

  20. Caliskan, M., Graupner, D., & Mauve, M. (2006). Decentralized discovery of free parking places. In Proceedings of 3rd ACM international workshop on vehicular ad hoc networks VANET ’06 (pp. 30–39) New York, USA.

  21. Lochert, C., Scheuermann, B., & Mauve, M. (2007). Probabilistic aggregation for data dissemination in VANETs. In Proceedings of 4th ACM international workshop on vehicular ad hoc networks (pp. 1–8) New York, USA.

  22. Scheuermann, B., Lochert, C., Rybicki, J., & Mauve, M. (2009). A fundamental scalability criterion for data aggregation in VANETs. In Proceedings of 15th annual international conference on mobile computing and networking (pp. 285–296) USA.

  23. Van Eenennaam, M., & Heijenk, G. (2008). Providing over-the-horizon awareness to driver support systems. In Proceedings of 4th IEEE workshop on V2V communications.

  24. Ibrahim, K., & Weigle, M. C. (2008). CASCADE: cluster-based accurate syntactic compression of aggregated data in VANETs. In Proceedings of IEEE globecom workshops pp. 1–10.

  25. Dietzel, S., Kargl, F., Heijenk, G., & Schaub, F. (2010). On the potential of generic modeling for VANET data aggregation protocols. In Proceedings of IEEE vehicular networking conference (pp. 78–85) USA.

  26. Rybicki, J., Scheuermann, B., Koegel, M., & Mauve, M. (2009). PeerTIS: A peer-to-peer traffic information system. In Proceedings of 6th ACM international workshop on vehiculAr InterNETworking (pp. 23–32) USA.

  27. Ding, Z., & Guting, R. (2004). Modeling Temporally Variable Transportation Networks. In Proceedings of database systems for advanced applications (Vol. 2973, pp. 651–724) LNCS Springer.

  28. Flinsenberg, C. (2004). Route planning algorithms for car navigation. PhD Thesis, Technische Universiteit Eindhoven.

  29. George, B., & Shekhar, S. (2008). Time-aggregated graphs for modeling spatio-temporal networks. Journal on Data Semantics XI, ser. LNCS Springer Vol. 5383, pp. 191–212.

  30. Kostakos, V. (2009). Temporal graphs. Physica A: Statistical Mechanics and its Applications, 388(6), 1007–1023.

    Article  MathSciNet  Google Scholar 

  31. Kumar, R., & Dave, M. (2011). Knowledge Based Framework for Data Aggregation in Vehicular Ad Hoc Networks. In Proceedings of CIIT (vol. 250, part 2, pp. 722–727) CCIS Springer.

  32. Zionts, S., & Wallenius, J. (1976). An interactive programming method for solving the multiple criteria problem. Management Science, 22(6), 652–663.

    Article  MATH  Google Scholar 

  33. Yu, X., Guo, H., & Wong, W. (2011). A reliable routing protocol for VANET communications. In Proceedings of 7th IEEE wireless communication and mobile computing conference pp. 1748–1753.

  34. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). Cambridge: The MIT Press.

    MATH  Google Scholar 

  35. Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509–517.

    Article  MathSciNet  MATH  Google Scholar 

  36. Wald, I., & Havran, V. (2006). On building fast Kd-trees for ray tracing and on doing that in O(NlogN). IEEE symposium on interactive ray tracing pp. 61–69.

  37. Kakde, H. M. (2005). Range Searching using Kd tree. pp. 1–12. Available at:www.cs.fsu.edu/ lifeifei/cis5930/kdtree.pdf.

  38. Guo, W., Xiong, N., Vasilakos, A. V., Chen, G., & Cheng, H. (2011). Multi-source temporal data aggregation in wireless sensor networks. Wireless Personal Communications, 56, 359–370. doi:10.1007/s11277-010-9976-9.

    Article  Google Scholar 

  39. Wisitpongphan, N., Bai, F., Mudalige, P., Sadekar, V., & Tonguz, O. (2007). Routing in sparse vehicular ad hoc wireless networks. IEEE Journal on Selected Areas in Communications, 25(8), 1538–1556.

    Article  Google Scholar 

  40. Benslimane, & A. Bachir. (2003). Inter-vehicle geocast protocol supporting non-equipped GPS vehicles. In Proceedings of ad-hoc, mobile and wireless networks (Vol. 2865, pp. 281–286) LNCS Springer.

  41. Sommer, I. Dietrich, & Dressler, F. (2010). Simulation of ad hoc routing protocols using OMNeT++: A case study for the DYMO protocol. Mobile Networks and Applications, 15(6), 786–801.

    Article  Google Scholar 

  42. Stanica, R., Chaput, E., & Beylot, A. (2011). Simulation of vehicular ad-hoc networks: Challenges, review of tools and recommendations. Computer Networks, 55(14), 3179–3188.

    Article  Google Scholar 

  43. The NS-2. http://nsnam.isi.edu/nsnam/index.php/Main_Page.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rakesh Kumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, R., Dave, M. A Framework For Handling Local Broadcast Storm Using Probabilistic Data Aggregation In VANET. Wireless Pers Commun 72, 315–341 (2013). https://doi.org/10.1007/s11277-013-1016-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1016-0

Keywords

Navigation