We propose an idle probability-based broadcasting method, iPro, which employs an adaptive probabilistic mechanism to improve performance of data broadcasting over dense wireless ad hoc networks. In multisource one-hop broadcast scenarios, the modeling and simulation results of the proposed iPro are shown to significantly outperform the standard IEEE 802.11 under saturated condition. Moreover, the results also show that without estimating the number of competing nodes and changing the contention window size, the performance of the proposed iPro can still approach the theoretical bound. We further apply iPro to multihop broadcasting scenarios, and the experiment results show that within the same elapsed time after the broadcasting, the proposed iPro has significantly higher Packet-Delivery Ratios (PDR) than traditional methods. 1. Introduction Classical performance analyses for multihop broadcast mostly focus on single-source sporadic broadcasting over network topologies with appropriate network densities. In the paper, we examine the scenarios that two or more sources continuously broadcast over the network at the same time, and observe that the packet-delivery performance of traditional methods degrades along with the increases of the network density and the number of broadcast sources. To improve the broadcast performance, we propose an adaptive probabilistic mechanism, iPro, which operates atop standard IEEE 802.11 with constant contention window-size. The proposed method was first analyzed in one-hop saturated scenarios, where the modeling and ns2 simulation results show that the proposed iPro approaches theoretical throughput maximum. After that, the proposed iPro integrating with counter-based scheme is further applied to multihop broadcast scenarios, where the performance of the proposed method is also significantly better than existing methods. A wireless ad hoc network consists of a set of nodes where the data delivery among nodes does not depend on any infrastructure; instead, nodes self-organize and relay messages among one another. A Mobile ad hoc Network (MANET) is a kind of wireless ad hoc network with mobility. Typically, a node in MANETs does not have fixed mobility patterns, and the power of the node would be limited. A Vehicular ad hoc Network (VANET) is a special case of MANET, with less concern on power consumption, intended for vehicles to communicate with other vehicles or with the road side infrastructures. Broadcast is a common communication operation in wireless ad hoc networks. In proactive routing, a node needs to exchange
References
[1]
E. Schoch, F. Kargl, M. Weber, and T. Leinmüller, “Communication patterns in VANETs,” IEEE Communications Magazine, vol. 46, no. 11, pp. 119–125, 2008.
[2]
K. Ramachandran, M. Gruteser, R. Onishi, and T. Hikita, “Experimental analysis of broadcast reliability in dense vehicular networks,” IEEE Vehicular Technology Magazine, vol. 2, no. 4, pp. 26–32, 2007.
[3]
IEEE 802.11 Working Group, “Part 11: wireless LAN medium access control (MAC) and physical layer (PHY) specifications ANSI/IEEE Std. 802,” June 2007.
[4]
S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, “The broadcast storm problem in a mobile ad hoc network,” in Proceedings of International Conference on Mobile Computing and Networking (MobiCom '99), 1999.
[5]
M. Jiang, J. Li, and Y. C. Tay, “Cluster based routing protocol (CBRP) functional specification,” internet draft, 1998.
[6]
B. Williams and T. Camp, “Comparison of broadcasting techniques for mobile ad hoc networks,” in Proceedings of the 3rd ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC '02), pp. 194–205, June 2002.
[7]
W. Peng and X. Lu, “On the reduction of broadcast redundancy in mobile ad hoc networks,” in Proceedings of the 1st ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC '00), 2000.
[8]
Q. Zhang and D. P. Agrawal, “Dynamic probabilistic broadcasting in MANETs,” Journal of Parallel and Distributed Computing, vol. 65, no. 2, pp. 220–233, 2005.
[9]
A. M. Hanashi, A. Siddique, I. Awan, and M. Woodward, “Performance evaluation of dynamic probabilistic flooding under different mobility models in MANETs,” in Proceedings of the 13th International Conference on Parallel and Distributed Systems (ICPADS '07), pp. 1–6, December 2007.
[10]
G. Bianchi, L. Fratta, and M. Oliveri, “Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs,” in Proceedings of the 7th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '96), pp. 392–396, Taipei, Taiwan, October 1996.
[11]
G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 535–547, 2000.
[12]
X. Ma and X. Chen, “Performance analysis of IEEE 802.11 broadcast scheme in ad hoc wireless LANs,” IEEE Transactions on Vehicular Technology, vol. 57, no. 6, pp. 3757–3768, 2008.
[13]
F. Calì, M. Conti, and E. Gregori, “Dynamic tuning of the IEEE 802.11 protocol to achieve a theoretical throughput limit,” IEEE/ACM Transactions on Networking, vol. 8, no. 6, pp. 785–799, 2000.
[14]
G. Bianchi and I. Tinnirello, “Kalman filter estimation of the number of competing terminals in an IEEE 802.11 network,” in Proceedings of the 22nd Annual Joint Conference on the IEEE Computer and Communications Societies (INFOCOM '03), vol. 2, pp. 844–852, San Francisco, Calif, USA, March-April 2003.
[15]
A. L. Toledo, T. Vercauteren, and X. Wang, “Adaptive optimization of IEEE 802.11 DCF based on Bayesian estimation of the number of competing terminals,” IEEE Transactions on Mobile Computing, vol. 5, no. 9, pp. 1283–1296, 2006.
[16]
L. Bononi, M. Conti, and E. Gregori, “Runtime optimization of IEEE 802.11 wireless LANs performance,” IEEE Transactions on Parallel and Distributed Systems, vol. 15, no. 1, pp. 66–80, 2004.
[17]
G. Bianchi and I. Tinnirello, “Remarks on IEEE 802.11 DCF performance analysis,” IEEE Communications Letters, vol. 9, no. 8, pp. 765–767, 2005.