Dynamic region-based mobile multicast

  • Authors:

  • Shengling Wang;Yong Cui;Sajal K. Das;Jianping Wu

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Engineering, University Texas at Arlington, Arlington, TX, U.S.A.;Department of Computer Science and Technology, Tsinghua University, Beijing, China

  • Venue:
  • Wireless Communications & Mobile Computing
  • Year:
  • 2012

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Abstract

Traditional mobile multicast schemes have higher multicast tree reconfiguration cost or multicast packet delivery cost. Two costs are very critical because the former affects the service disruption time during handoff while the latter affects the packet delivery delay. Although the range-based mobile multicast (RBMoM) scheme and its similar schemes offer the trade-off between two costs to some extent, most of them do not determine the size of service region, which is critical to the network performance. Hence, we propose a dynamic region-based mobile multicast (DRBMoM) to dynamically determine the optimal service region for reducing the multicast tree reconfiguration and multicast packet delivery costs. DRBMoM provides two versions: (i) the per-user version, named DRBMoM-U, and (ii) the aggregate-users version, named DRBMoM-A. Two versions have different applicability, which are the complementary technologies for pursuing efficient mobile multicast. Though having different data information and operations, two versions have the same method for finding the optimal service region. To that aim, DRBMoM models the users' mobility with arbitrary movement directional probabilities in 2-D mesh network using Markov Chain, and predicts the behaviors of foreign agents' (FAs') joining in a multicast group. DRBMoM derives a cost function to formulate the average multicast tree reconfiguration cost and the average multicast packet delivery cost, which is a function of service region. DRBMoM finds the optimal service region that can minimize the cost function. The simulation tests some key parameters of DRBMoM. In addition, the simulation and numerical analyses show the cost in DRBMoM is about 22∼50% of that in RBMoM. At last, the applicability and computational complexity of DRBMoM and its similar scheme are analyzed. Copyright © 2010 John Wiley & Sons, Ltd.