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Minimizing Expected Transmissions Multicast in Wireless Multihop Networks

机译:在无线多跳网络中最小化预期的多播传输

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We address the multicast problem in a wireless multihop network in the presence of errors. Finding the multicast schedule that minimizes the expected number of transmissions, while accounting for both the wireless broadcast advantage (WBA) and the wireless unreliable transmission (WUT) characteristics, has not been solved optimally before. We formally define the problem and propose an initial algorithm of non-polynomial complexity to obtain the optimal solution. The algorithm starts by transforming the wireless network topology into an auxiliary expanded graph that captures the WBA property, and assigning appropriate weights to the links so as to capture the WUT property. The proposed transformation is interesting on its own merit, since it permits us to consider only point-to-point transmissions (as in wireline networks) in the expanded graph, instead of the point-to-multipoint transmissions present in the original wireless network, and can also be useful in other optimization problems some of which we briefly describe. By solving the minimum Steiner tree problem on the expanded graph we obtain the optimal multicast solution for the initial graph. Since the optimal algorithm is of non-polynomial complexity, we also propose a number of heuristic algorithms. In particular, we first present a truncated graph transformation and then describe two heuristic algorithms for obtaining the multicast schedule. Simulation results show that the proposed heuristics have performance close to that of the optimal algorithm, at least for the instances for which we were able to track optimal solutions, while outperforming other heuristic multicast algorithms.
机译:在存在错误的情况下,我们解决了无线多跳网络中的多播问题。以前,找到兼顾无线广播优势(WBA)和无线不可靠传输(WUT)特性的组播计划可以最大程度地减少预期的传输次数,这尚未得到最佳解决。我们正式定义问题并提出非多项式复杂度的初始算法以获得最佳解。该算法首先将无线网络拓扑转换为捕获WBA属性的辅助扩展图,并为链路分配适当的权重以捕获WUT属性。所提出的变换本身就很有趣,因为它允许我们只考虑扩展图中的点对点传输(例如在有线网络中),而不是原始无线网络中存在的点对多点传输,还可用于我们简要描述的其他优化问题。通过解决扩展图上的最小Steiner树问题,我们获得了初始图的最佳多播解决方案。由于最优算法具有非多项式复杂度,因此我们还提出了许多启发式算法。特别是,我们首先提出了一个截断图变换,然后描述了两种用于获得多播调度的启发式算法。仿真结果表明,所提出的启发式算法在性能上至少接近于最佳算法,至少在我们能够跟踪最优解的情况下,其性能优于其他启发式组播算法。

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